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ioc-investigation

스타3

포크2

업데이트2026년 6월 3일 04:42

IoC (Indicator of Compromise) investigation skill for environments with Azure Monitor MCP (Log Analytics workspace queries) and Azure CLI access — currently without Sentinel Data Lake MCP, Sentinel Triage MCP, or Microsoft Graph MCP (not yet connectable to Azure SRE Agent; direct API access to Sentinel Data Lake and Microsoft Graph not yet implemented). KQL queries run against Log Analytics tables through the Azure Monitor MCP tool. MDE API calls (custom IOC list, TVM) are executed via RunAzCliReadCommands (az rest). 3rd-party IP enrichment is provided by enrich_ips.py (ipinfo.io, vpnapi.io, AbuseIPDB, Shodan).

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

stefanpems

stefanpems/sec-sre-ag

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

IoC (Indicator of Compromise) Investigation — Monitor MCP + Azure CLI

Purpose

This skill performs comprehensive security investigations on Indicators of Compromise (IoCs) including:

IP Addresses: Network connections, threat intel matches, geographic analysis, organizational exposure
DNS Domains: Domain reputation, connection events, email-based threats, URL analysis
URLs: URL reputation, phishing detection, email delivery, browser activity
File Hashes: Malware analysis, file prevalence, related alerts, affected devices

The investigation correlates IoCs with threat intelligence, identifies associated CVEs, and enumerates organizational assets affected by those vulnerabilities.

Environment: This skill operates in a constrained environment where:

✅ Azure Monitor MCP tool is available (monitor-client_monitor_workspace_log_query) for KQL queries against Log Analytics
✅ RunAzCliReadCommands tool is available for Azure CLI read operations (including az rest for MDE API and Graph API)
✅ KQL Search MCP (mcp_kql-search-mc_*) is available for schema validation and query examples
✅ Microsoft Learn MCP (mcp_microsoft_lea_* / mcp_microsoft_le2_*) is available for documentation
✅ Azure MCP Server (mcp_azure_mcp_ser_*) is available for Azure resource management
✅ enrich_ips.py is included in this skill folder for 3rd-party IP enrichment
❌ Sentinel Data Lake MCP — not integrated (no query_lake, list_sentinel_workspaces, search_tables)
❌ Sentinel Triage MCP — not integrated (no RunAdvancedHuntingQuery, GetDefenderIpAlerts, GetDefenderFileInfo, ListDefenderIndicators, etc.)
❌ Microsoft Graph MCP — not integrated (no microsoft_graph_get, suggest_queries)

Why these MCP servers are absent: Sentinel Data Lake MCP, Sentinel Triage MCP, and Microsoft Graph MCP cannot currently be connected to Azure SRE Agent. This does not mean the underlying data is inaccessible — the data exposed by these servers (Sentinel Data Lake, Defender XDR / Advanced Hunting, Microsoft Graph) can be reached via direct API calls. However, direct API access to Sentinel Data Lake and Microsoft Graph as a replacement for these MCP servers has not yet been studied and implemented in this skill.

Data sources (Log Analytics via KQL): ThreatIntelIndicators (new STIX table — ⚠️ NOT legacy ThreatIntelligenceIndicator), DeviceNetworkEvents, DeviceProcessEvents, DeviceFileEvents, DeviceRegistryEvents, DeviceLogonEvents, DeviceImageLoadEvents, DeviceEvents, AlertEvidence, AlertInfo, SecurityAlert, SigninLogs, AADNonInteractiveUserSignInLogs, EmailUrlInfo.

Data sources (MDE API via az rest): Custom IOC list, IP alerts/statistics, File info/stats/alerts/machines, CVE→affected devices (TVM).

NOT directly available (require Advanced Hunting in portal): DeviceTvmSoftwareVulnerabilities, DeviceTvmSoftwareInventory.

📑 TABLE OF CONTENTS

Critical Workflow Rules - Start here!
Prerequisites
Environment Configuration
Phase 0: Investigation Cache Check — Cache reuse logic
Investigation Types - By IoC type
Quick Start - 5-step investigation pattern
Execution Workflow - Complete process
KQL Execution Reference - How to run queries
Sample KQL Queries - Validated query patterns
MDE API via CLI - Defender API calls via az rest
JSON Export Structure - Required fields
Error Handling - Troubleshooting guide

Investigation shortcuts:

Suspicious IP from spray/brute-force: Q2 (network connections) → Q11 (sign-in analysis) → Q8 (alert evidence) → Q1 (TI match)
IP from user risk event: Q11 (sign-in analysis) → Q2 (device connections) → Q9 (security alerts) → enrich_ips.py
Phishing domain/URL: Q4 (DNS/HTTP connections) → Q6 (email delivery) → Q8 (alert evidence) → Q1 (TI match)
File hash from incident: Q7 (file events across all tables) → Q9 (security alerts) → MDE-IOC (custom indicator check) → Q12 (CVE extraction)
IoC organizational exposure: Q2/Q4 (affected devices) → Q9 (alert correlation) → Q12 (CVEs from alerts)

⛔ Shortcut Default Rule: When a matching shortcut exists for the investigation context, use it — don't run the full workflow. Only run the full query set when the user explicitly requests "full investigation", "comprehensive", or "deep dive". Shortcuts render only the report sections relevant to their query chain (plus Executive Summary and Recommendations, always).

⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Before starting ANY IoC investigation:

ALWAYS identify the IoC type FIRST (IP, Domain, URL, or File Hash)
ALWAYS normalize the IoC (lowercase domains, validate IP format, extract domain from URL)
ALWAYS complete Phase 0 (Cache Check) after normalization — Before any data collection, check for cached investigation results. See Phase 0 for full logic.
ALWAYS calculate date ranges correctly (use current date from context — see Date Range section)
ALWAYS track and report time after each major step (mandatory)
ALWAYS run independent queries in parallel (drastically faster execution)
ALWAYS use create_file for JSON export (NEVER use PowerShell terminal commands)
ALWAYS use Azure Monitor MCP for KQL execution (see KQL Execution Reference)
ALWAYS use RunAzCliReadCommands for MDE API calls (see defender-api-via-cli.md)

Prerequisites

Dependency	Required	Fallback	Notes
Azure Monitor MCP (`monitor-client_monitor_workspace_log_query`)	✅ Yes	None — core dependency	Must be configured and connected to the target Log Analytics workspace
`RunAzCliReadCommands` tool	⚠️ Optional	KQL-only mode	Used for MDE API calls via `az rest`. If unavailable, skip MDE API enrichment
KQL Search MCP (`mcp_kql-search-mc_*`)	⚠️ Optional	Use queries from this skill directly	Schema validation, query examples
Microsoft Learn MCP (`mcp_microsoft_lea_*`)	⚠️ Optional	N/A	Documentation reference
Python 3.x	⚠️ Optional	Q13 (KQL IP context)	`enrich_ips.py` requires Python + API tokens. If unavailable, use Q13 KQL fallback

Skill Files

File	Purpose
`SKILL.md`	This file — skill instructions, KQL queries, workflow
`enrich_ips.py`	3rd-party IP enrichment (ipinfo.io, vpnapi.io, AbuseIPDB, Shodan)
`generate_html_report.py`	HTML report generator — reads JSON export, produces styled HTML
`defender-api-via-cli.md`	MDE API reference for `az rest` calls

File Resolution (codeRefs-first)

Before executing any skill file (scripts, data files, companion files), resolve its location using this mandatory cascade:

1. codeRefs/sec-sre-ag/ioc-investigation/<filename>
   → If found: use/execute directly from this path (companion files are co-located here)
2. tmp/ioc-investigation/<filename>
   → If found: use from this path
3. Neither found:
   → read_skill_file("ioc-investigation", "<filename>") from Builder
   → CreateFile("tmp/ioc-investigation/<filename>", <content>)
   → Repeat for ALL companion files referenced by the script

Rules:

When a file is found in codeRefs/, execute it directly from there — do NOT copy it to tmp/.
When materializing from Builder (step 3), materialize ALL companion files the script depends on, not just the script itself.
This cascade applies to every file listed in the Skill Files table above.

Pre-requisite: Environment Configuration (config.json)

Before executing any script resolved via the File Resolution cascade, the agent MUST ensure that config.json exists at the workspace root (the top-level directory of the agent workspace, NOT inside codeRefs/ or skill-specific directories).

Procedure:

Check: Verify that config.json exists at the workspace root and contains a non-empty sentinel_workspace_id value. If it does, skip to step 3.
If config.json is missing or incomplete, create it: a. Ask the user for the tenant name using AskUserQuestion with header "Tenant" and question: "What is your tenant name? (e.g., contoso.onmicrosoft.com or contoso.it)?" b. Extract from agent system prompt settings:
- subscription_id → from the <azure_resource_access> section (the subscription ID the agent has access to)
- sentinel_workspace_id → from the <log_analytics_access> section (the workspace GUID after workspace=)
- workspace_name → from the <log_analytics_access> section (the workspace name before the colon) c. Discover the workspace resource group by running:
```
az monitor log-analytics workspace show --workspace-name <workspace_name> --subscription <subscription_id> --query resourceGroup -o tsv
```
d. Create config.json at the workspace root with this structure:
```
{
  "tenant_name": "<tenant_name>",
  "sentinel_workspace_id": "<workspace_guid>",
  "subscription_id": "<subscription_id>",
  "azure_mcp": {
    "subscription_id": "<subscription_id>",
    "resource_group": "<discovered_resource_group>",
    "workspace_name": "<workspace_name>"
  },
  "api_tokens": {}
}
```
Proceed with the skill workflow. All Python scripts find config.json by walking up from their own directory (max 6 levels), so the workspace root is the correct and expected location.

Rules:

Do NOT write config.json inside codeRefs/ or inside skill-specific directories.
Do NOT hardcode any environment-specific values in this skill file — all values are derived at runtime from the agent's own settings and user input.
The api_tokens object is left empty — API tokens are loaded from Key Vault or environment variables at runtime.

Output Modes

Mode	When	What
Inline (DEFAULT — ALWAYS)	Every invocation	Present all findings, threat intel, activity, risk assessment inline in chat
Markdown file	Only if the user explicitly requests	Save investigation report as `.md` file
HTML report	Only if the user explicitly requests	Generate one HTML report per IoC investigated. Present only the HTML report links to the user
JSON export	Internal only — created automatically as intermediate step for HTML generation	Save `ioc_investigation_.json` to `temp/`. NEVER show JSON file links to the user* unless they explicitly ask for JSON export

Rule: Always start with inline presentation. Never skip inline output. The other modes are additive, triggered only by explicit user request. Rule: JSON files are internal artifacts consumed by generate_html_report.py. Do NOT present JSON links in the final output — only present HTML report links.

HTML Report — Conditional Resolution (codeRefs-first)

When the user requests an HTML report:

Export investigation data to JSON: temp/ioc_investigation_{type}_{value}_{ts}.json
Resolve generate_html_report.py via the File Resolution cascade:
- Check codeRefs/sec-sre-ag/ioc-investigation/generate_html_report.py → if found, use that path.
- Else check tmp/ioc-investigation/generate_html_report.py → if found, use that path.
- Else: read_skill_file("ioc-investigation", "generate_html_report.py") → CreateFile("tmp/ioc-investigation/generate_html_report.py", <content>)
Execute: python3 <resolved_path>/generate_html_report.py temp/ioc_investigation_*.json --output-dir reports/ioc-investigation/

Do NOT resolve the script unless the user explicitly requests an HTML report.

Multi-IoC HTML Reports

When multiple IoCs are investigated together (e.g., an IP + a domain), generate one separate JSON export and one separate HTML report per IoC.

Each IoC gets its own temp/ioc_investigation_{type}_{value}_{ts}.json file
Each IoC gets its own HTML report via generate_html_report.py
Do NOT combine multiple IoCs into a single JSON/HTML — the report generator is designed for one IoC per file

Run the generator once per IoC:

python3 tmp/ioc-investigation/generate_html_report.py temp/ioc_investigation_ip_144.24.28.121_*.json --output-dir reports/ioc-investigation/
python3 tmp/ioc-investigation/generate_html_report.py temp/ioc_investigation_domain_ms-teams.us.com_*.json --output-dir reports/ioc-investigation/

Present all generated HTML report links to the user together at the end
NEVER present JSON file links to the user — JSON is an internal intermediate artifact

enrich_ips.py — Conditional Resolution (codeRefs-first)

Before running IP enrichment, resolve enrich_ips.py via the File Resolution cascade:

Check codeRefs/sec-sre-ag/ioc-investigation/enrich_ips.py → if found, run from there.
Else check tmp/ioc-investigation/enrich_ips.py → if found, run from there.
Else: read_skill_file("ioc-investigation", "enrich_ips.py") → CreateFile("tmp/ioc-investigation/enrich_ips.py", <content>) → run from tmp/.
Run: ABUSEIPDB_TOKEN=<value> python3 <resolved_path>/enrich_ips.py <ip1> <ip2> ...

Environment Configuration

Primary: Agent Settings Auto-Discovery (Recommended)

Workspace parameters are automatically available from the agent's system context:

<log_analytics_access> section provides:
- Workspace name (display name)
- Workspace ID (GUID) — use as the workspace parameter
<azure_resource_access> section provides:
- Subscription ID
<agent_identity> section provides:
- Resource group (extractable from the agent's ARM resource ID)

How to extract parameters from agent context:

Parameter	Source	Example
`workspace`	`<log_analytics_access>` → workspace GUID	the workspace ID from `<log_analytics_access>`
`subscription`	`<azure_resource_access>` → subscription ID	the agent's subscription from `<azure_resource_access>`
`resource-group`	`<agent_identity>` → extract from ARM resource ID	the agent's resource group, extracted from its ARM resource ID in `<agent_identity>`

When making Monitor MCP calls, always pass subscription from agent settings. The workspace parameter accepts the workspace GUID directly.

Secondary: config.json (Optional)

If a config.json file exists at the workspace root, it can provide additional configuration:

Field	Used By	Purpose
`azure_mcp.resource_group`	Monitor MCP	Resource group containing the Log Analytics workspace
`azure_mcp.workspace_name`	Monitor MCP	Log Analytics workspace display name
`azure_mcp.tenant`	Monitor MCP, `az rest`	Entra ID tenant
`azure_mcp.subscription`	Monitor MCP	Target Azure subscription
`sentinel_workspace_id`	CLI fallback	Log Analytics workspace GUID
`tenant_id`	Portal URLs	Entra ID tenant ID

Configuration Resolution Order

Agent settings (<log_analytics_access>, <azure_resource_access>) — always available
config.json — read if present, skip if absent
Never prompt the user for workspace parameters if either source is available

Secrets Management (API Tokens)

enrich_ips.py uses 4 external threat intelligence APIs. Each requires an API token. Tokens are resolved by the script in this order of precedence:

Environment variables (highest priority) — always checked
.env file in the script directory — auto-loaded by python-dotenv
config.json at the workspace root — JSON key/value pairs

API	Environment Variable	config.json Key	Purpose
ipinfo.io	`IPINFO_TOKEN`	`ipinfo_token`	Geolocation, ISP/ASN, VPN detection
vpnapi.io	`VPNAPI_TOKEN`	`vpnapi_token`	VPN/proxy/Tor detection
AbuseIPDB	`ABUSEIPDB_TOKEN`	`abuseipdb_token`	Abuse confidence score, reports
Shodan	`SHODAN_TOKEN`	`shodan_token`	Open ports, services, CVEs, tags

IoC Type Detection Rules:

Pattern	IoC Type	Normalization
`\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}`	IPv4 Address	Validate octets ≤255
`[a-fA-F0-9:]+` (with multiple colons)	IPv6 Address	Lowercase, expand if needed
`[a-zA-Z0-9][-a-zA-Z0-9]*\.[a-zA-Z]{2,}`	Domain	Lowercase, remove trailing dot
`https?://.*` or starts with `www.`	URL	Extract domain for separate analysis
32 hex chars	MD5 Hash	Lowercase
40 hex chars	SHA1 Hash	Lowercase
64 hex chars	SHA256 Hash	Lowercase

Date Range Rules:

Real-time/recent searches: Add +2 days to current date for end range
Historical ranges: Add +1 day to user's specified end date
Example: Current date = Jan 23; "Last 7 days" → datetime(2026-01-16) to datetime(2026-01-25)

Available Investigation Types

IP Address Investigation

When to use: Suspicious inbound/outbound connections, firewall alerts, sign-in anomalies

Example prompts:

"Investigate IP 203.0.113.42"
"Is 198.51.100.10 malicious?"
"Check threat intel for 192.0.2.1"

Data sources:

ThreatIntelIndicators (new STIX table via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
DeviceNetworkEvents (connection history via Log Analytics)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)
SecurityAlert (security alerts via Log Analytics)
SigninLogs (if used for authentication via Log Analytics)
MDE Custom IOC list (via az rest — see defender-api-via-cli.md)
MDE IP alerts/statistics (via az rest — see defender-api-via-cli.md)
enrich_ips.py (3rd-party enrichment: ipinfo.io geo/ISP, vpnapi.io VPN/proxy/Tor, AbuseIPDB abuse score & reports, Shodan ports/services/CVEs/tags)

Domain Investigation

When to use: Suspicious DNS queries, phishing domains, C2 communication

Example prompts:

"Investigate domain malware-c2.example.com"
"Is evil.com in our threat intel?"
"Check if any devices connected to suspicious.net"

Data sources:

DeviceNetworkEvents (DNS queries, HTTP connections via Log Analytics)
EmailUrlInfo (email-delivered URLs via Log Analytics)
ThreatIntelIndicators (domain indicators via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
MDE Custom IOC list (via az rest — see defender-api-via-cli.md)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)

URL Investigation

When to use: Phishing links, malicious downloads, suspicious redirects

Example prompts:

"Investigate URL https://phishing.example.com/login"
"Was this URL clicked by anyone?"
"Check threat intel for http://malware.site/payload.exe"

Data sources:

EmailUrlInfo (URLs in emails via Log Analytics)
DeviceNetworkEvents (HTTP/HTTPS connections via Log Analytics)
DeviceFileEvents (downloads from URL via Log Analytics)
ThreatIntelIndicators (URL patterns via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)

File Hash Investigation

When to use: Malware analysis, suspicious executables, file reputation

Example prompts:

"Investigate hash a1b2c3d4e5f6..."
"Is this SHA256 known malware?"
"Which devices have this file?"

Data sources:

DeviceFileEvents (file creation/modification via Log Analytics)
DeviceProcessEvents (process execution with hash via Log Analytics)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)
ThreatIntelIndicators (file hash indicators via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
MDE File info/stats/alerts/machines (via az rest — see defender-api-via-cli.md)

Quick Start (TL;DR)

When a user requests an IoC investigation:

Identify & Normalize IoC:

- Detect IoC type (IP/Domain/URL/Hash)
- Normalize format (lowercase, validate)
- Extract embedded IoCs (domain from URL)

Run Parallel KQL Queries (Batch 1 — Threat Intel):
- Q1: ThreatIntelIndicators query (via Azure Monitor MCP — ⚠️ NOT legacy ThreatIntelligenceIndicator)
- MDE-IOC: Custom IOC list search (via RunAzCliReadCommands + az rest)
- MDE-ALERTS: IP/File alerts from MDE API (via RunAzCliReadCommands + az rest)
Run 3rd-Party IP Enrichment (IP IoCs only):
```
python3 tmp/ioc-investigation/enrich_ips.py <IP_ADDRESS>
```
- ipinfo.io: Geolocation, ISP/ASN, hosting provider
- vpnapi.io: VPN, proxy, Tor exit node detection
- AbuseIPDB: Abuse confidence score, recent attack reports
- Shodan: Open ports, services/banners, CVEs, tags (e.g., c2, eol-os, self-signed)
Run Parallel KQL Queries (Batch 2 — Activity):
- Q2/Q3: DeviceNetworkEvents (connections involving IoC)
- Q8: AlertEvidence (alerts with IoC as evidence)
- Q9: AlertEvidence + AlertInfo (full alert correlation)
- Q10: SecurityAlert (mentions of IoC)
- Q6: EmailUrlInfo (if domain/URL)
- Q11: SigninLogs (if IP)
CVE & Vulnerability Correlation:
- Extract CVE IDs from alert results AND Shodan enrichment
- For each CVE: query affected devices via MDE API (az rest)
- Aggregate affected devices

Export to JSON & Generate Summary:

temp/ioc_investigation_{ioc_normalized}_{timestamp}.json

Phase 0: Investigation Cache Check (MANDATORY)

This phase MUST execute BEFORE any data collection. It determines whether to reuse cached investigation data or start a fresh investigation.

0.1 Cache File Convention

Investigation results are stored as JSON files following this naming pattern:

temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{YYYYMMDD_HHMMSS}.json

0.2 Cache Check Workflow

Step 0.1: After IoC normalization (Phase 1), search for existing cache files
          → Use: ls temp/ioc_investigation_{ioc_type}_{ioc_normalized}_*.json
          → If NO cache file exists → proceed to Phase 2 (fresh investigation)

Step 0.2: If one or more cache files exist, select the MOST RECENT one (latest timestamp)

Step 0.3: Calculate the cache age:
          → Extract timestamp from filename (YYYYMMDD_HHMMSS format)
          → age = current_UTC_time − cache_file_timestamp
          → If age > 4 hours → IGNORE cache entirely, proceed to Phase 2 (fresh investigation)
          → If age ≤ 4 hours → proceed to Step 0.4

Step 0.4: Analyze the user's ORIGINAL prompt for implicit intent:

          REDO KEYWORDS (triggers fresh investigation, any language):
            "ripeti", "aggiorna", "rifai", "repeat", "redo", "refresh",
            "update", "re-investigate", "start over", "da capo",
            "from scratch", "ricomincia", "nuovo", "nuova analisi"
          → If ANY redo keyword is detected → IGNORE cache, proceed to Phase 2

          USE-CACHE KEYWORDS (triggers cache reuse, any language):
            "completa", "continua", "complete", "continue", "finish",
            "usa i dati", "use cached", "use existing", "prosegui",
            "riprendi", "resume", "genera report", "generate report",
            "genera il report", "crea report"
          → If ANY use-cache keyword is detected → LOAD cache, skip to summary

          NO IMPLICIT INTENT DETECTED:
          → Proceed to Step 0.5 (ask the user)

Step 0.5: ASK the user:

          Question: "Ho trovato risultati di un'investigazione precedente per
                     l'IoC <IOC_VALUE> (<IOC_TYPE>), completata <TIME_AGO> fa
                     (alle <HH:MM> UTC).
                     Vuoi utilizzare questi dati o preferisci ripetere
                     l'investigazione da zero?"
          Options:
            1. "Usa i dati esistenti" — Riprende dall'investigazione precedente
            2. "Ripeti da zero" — Ignora la cache e ricomincia

          → If user selects "Usa i dati esistenti" → LOAD cache, present summary
          → If user selects "Ripeti da zero" → proceed to Phase 2

Step 0.6: LOAD cached data:
          → Read the JSON file
          → Present a brief inline summary of cached findings
          → Offer HTML/report generation or further analysis

0.3 Cache Decision Summary

Cache Exists?	Age	User Prompt	Action
No	—	—	Fresh investigation
Yes	> 4 hours	—	Fresh investigation — cache expired
Yes	≤ 4 hours	Contains REDO keyword	Fresh investigation
Yes	≤ 4 hours	Contains USE-CACHE keyword	Load cache
Yes	≤ 4 hours	No implicit intent	ASK user

0.4 Important Rules

NEVER silently reuse cached data — always either detect explicit intent from the prompt or ask the user.
NEVER ask the user if the prompt already contains an implicit answer — detect keywords first.
When loading cache, always show what was already completed — the user must understand what data is from cache vs. new queries.
Cache files from a DIFFERENT thread/session are still valid — the 4-hour TTL is the only expiration criterion.
If the user later requests a fresh investigation after loading cache — discard all cached data and restart.

Execution Workflow

🚨 MANDATORY: Time Tracking Pattern

YOU MUST TRACK AND REPORT TIME AFTER EVERY MAJOR STEP:

[MM:SS] ✓ Step description (XX seconds)

Required Reporting Points:

After IoC normalization and type detection
After 3rd-party IP enrichment (IP IoCs)
After threat intelligence lookup (KQL + MDE API)
After activity/connection analysis
After CVE correlation and device enumeration
After JSON file creation
Final: Total elapsed time

Phase 1: IoC Identification and Normalization (REQUIRED FIRST)

Step 1.1: Detect IoC Type

# Regex patterns for IoC detection
IPv4: r'^(\d{1,3}\.){3}\d{1,3}$'
IPv6: r'^([0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}$'
Domain: r'^([a-zA-Z0-9]([a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,}$'
URL: r'^https?://'
MD5: r'^[a-fA-F0-9]{32}$'
SHA1: r'^[a-fA-F0-9]{40}$'
SHA256: r'^[a-fA-F0-9]{64}$'

Step 1.2: Normalize IoC

IP Address: Validate octets, detect IPv4 vs IPv6
Domain: Lowercase, remove trailing dots, extract from URL if needed
URL: Keep full URL, also extract domain for parallel investigation
Hash: Lowercase

Step 1.3: Create Investigation Context

{
  "ioc_type": "ip|domain|url|hash",
  "ioc_value": "<normalized_value>",
  "ioc_original": "<user_provided_value>",
  "extracted_domain": "<if_url>",
  "investigation_start": "<timestamp>",
  "date_range_start": "<StartDate>",
  "date_range_end": "<EndDate>"
}

Phase 2: 3rd-Party IP Enrichment (IP Address IoCs)

MANDATORY for all IP address investigations. Run enrich_ips.py to get external threat intelligence context that is NOT available from Defender/Sentinel native tools.

Resolve and run:

Resolve enrich_ips.py via the File Resolution cascade:
- Check codeRefs/sec-sre-ag/ioc-investigation/enrich_ips.py → if found, run from there.
- Else check tmp/ioc-investigation/enrich_ips.py → if found, run from there.
- Else: read_skill_file("ioc-investigation", "enrich_ips.py") → CreateFile("tmp/ioc-investigation/enrich_ips.py", <content>) → run from tmp/.
Run:

ABUSEIPDB_TOKEN=<value> python3 <resolved_path>/enrich_ips.py <IP_ADDRESS_1> <IP_ADDRESS_2> ...

What it provides:

Source	Intelligence
ipinfo.io	Geolocation (city, country, coordinates), ISP/ASN, organization, hosting provider detection
vpnapi.io	VPN, proxy, Tor exit node, relay detection
AbuseIPDB	Abuse confidence score (0-100), total reports, last reported date, recent reporter comments with attack categories
Shodan	Open ports, service/banner details, OS detection, known CVEs, tags (e.g., `c2`, `eol-os`, `self-signed`, `honeypot`), CPEs, hostnames

Output: Per-IP detailed results printed to terminal + JSON export saved to temp/.

Integration with investigation:

AbuseIPDB score ≥ 75: 🔴 Strong indicator of malicious activity — flag as high risk
VPN/Proxy/Tor detected: 🟠 Potential evasion — note in risk assessment
Shodan tags contain c2: 🔴 Known C2 infrastructure — escalate immediately
Shodan CVEs found: Cross-reference with Phase 5 CVE correlation for organizational exposure
Hosting provider (not residential ISP): 🟡 May indicate attacker infrastructure

Note: For domain and URL IoCs, extract the resolved IP(s) from DeviceNetworkEvents results and run enrichment on those IPs as a follow-up step.

If enrich_ips.py is not available (no Python, no API tokens): Use Q13 (KQL IP Context Fallback) to get whatever context is available from Log Analytics tables.

Phase 3: Parallel Threat Intelligence Collection (KQL + MDE API)

CRITICAL: Run ALL threat intel queries in parallel for speed!

Pre-requisite: Read MDE API Reference (MANDATORY)

Before executing ANY MDE API call (az rest), you MUST read the reference file:

read_skill_file(skill_name="ioc-investigation", file_path="defender-api-via-cli.md")

This file contains the exact az rest commands, the required --resource parameter, the correct tool to use (RunAzCliReadCommands — NEVER RunAzCliWriteCommands), error handling, and the fallback strategy.

If the file has already been read in this session, skip this step.
Do NOT attempt MDE API calls from memory — the URLs and --resource parameter are precise and must match exactly.
If the first MDE API call returns 403 → skip ALL remaining MDE API calls and note "MDE API not accessible — KQL-only mode" in the report.

Batch 1: KQL Queries via Azure Monitor MCP (Run ALL in parallel)

Query	Table	IoC Types	Details
Q1	ThreatIntelIndicators	All	TI indicator match (STIX schema)
Q8	AlertEvidence	All	IoC in alert evidence
Q10	SecurityAlert	All	Security alerts mentioning IoC

Batch 2: MDE API Calls via `RunAzCliReadCommands` (Run ALL in parallel)

Call	Endpoint	IoC Types	Details
MDE-IOC	`/api/indicators`	All	Custom IOC list match
MDE-IP-ALERTS	`/api/ips/{ip}/alerts`	IP	Alerts for IP
MDE-IP-STATS	`/api/ips/{ip}/stats`	IP	IP organization prevalence
MDE-FILE-INFO	`/api/files/{hash}`	Hash	File details, threat determination
MDE-FILE-ALERTS	`/api/files/{hash}/alerts`	Hash	File-related alerts
MDE-FILE-STATS	`/api/files/{hash}/stats`	Hash	File organization statistics
MDE-FILE-MACHINES	`/api/files/{hash}/machines`	Hash	Devices with file

See defender-api-via-cli.md for exact az rest commands.

Phase 4: Activity and Connection Analysis (KQL via Azure Monitor MCP)

Run ALL activity queries in parallel!

Query	Table	IoC Types	Details
Q2	DeviceNetworkEvents	IP	Connection summary
Q3	DeviceNetworkEvents	IP	Connection timeline (top 20)
Q4	DeviceNetworkEvents	Domain	DNS/HTTP connection activity
Q5	DeviceNetworkEvents	Domain	Connection timeline (top 20)
Q6	EmailUrlInfo	Domain, URL	Email delivery analysis
Q7	DeviceFileEvents	Hash	File events across tables
Q7b	DeviceProcessEvents	Hash	Process events with hash
Q9	AlertEvidence + AlertInfo	All	Full alert correlation with details
Q11	SigninLogs	IP	Sign-in analysis (Azure AD)

Phase 5: CVE Correlation and Vulnerability Management

Step 5.1: Extract CVE IDs from Results AND Enrichment

Parse alert results for CVE references (pattern: CVE-\d{4}-\d{4,})
Extract from: alert descriptions, attack techniques, MITRE info
Extract from Shodan enrichment (shodan_vulns field from enrich_ips.py output)
Run Q12 (CVE Extraction from Alerts) to collect all CVE IDs

Step 5.2: Query Affected Devices per CVE

Use the MDE API via RunAzCliReadCommands:

For each CVE_ID found:
  → MDE-CVE-MACHINES: az rest to /api/vulnerabilities/{cveId}/machineReferences
  → Collect: deviceId, deviceName, osPlatform, rbacGroupName

See defender-api-via-cli.md for exact az rest commands.

Step 5.3: Aggregate Device Exposure

{
  "cve_correlation": {
    "cve_ids_found": ["CVE-2024-1234", "CVE-2024-5678"],
    "affected_devices_by_cve": {
      "CVE-2024-1234": [
        {"deviceId": "...", "deviceName": "...", "osPlatform": "..."}
      ]
    },
    "total_unique_affected_devices": 15,
    "critical_cves": 2,
    "high_cves": 3
  }
}

Phase 6: Export to JSON

Create single JSON file: temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{timestamp}.json

KQL Execution Reference

How to Run KQL Queries

All KQL queries in this skill are executed via the Azure Monitor MCP tool:

Tool: monitor-client_monitor_workspace_log_query
Parameters:
  workspace: <WORKSPACE_GUID>          # From agent settings <log_analytics_access>
  subscription: <SUBSCRIPTION_ID>      # From agent settings <azure_resource_access>
  query: "<KQL_QUERY>"                 # Query from templates below
  timespan: "P7D"                      # ISO 8601 duration (P7D = 7 days, P30D = 30 days)

Important notes:

The workspace parameter accepts the Log Analytics workspace GUID
The subscription parameter is REQUIRED
Date ranges in the query (datetime(...) / between) take precedence over timespan
Always use let variables for IoC values and dates at the start of each query

Table Availability

Table	Available in Log Analytics	Notes
ThreatIntelIndicators	✅ Yes	New STIX table (active since Apr 2025). ⚠️ Do NOT use legacy `ThreatIntelligenceIndicator` (deprecated July 2025, empty)
DeviceNetworkEvents	✅ Yes	MDE data connector
DeviceProcessEvents	✅ Yes	MDE data connector
DeviceFileEvents	✅ Yes	MDE data connector
DeviceRegistryEvents	✅ Yes	MDE data connector
DeviceLogonEvents	✅ Yes	MDE data connector
DeviceImageLoadEvents	✅ Yes	MDE data connector
DeviceEvents	✅ Yes	MDE data connector
AlertEvidence	✅ Yes	M365 Defender connector
AlertInfo	✅ Yes	M365 Defender connector
SecurityAlert	✅ Yes	Sentinel native
SigninLogs	✅ Yes	Entra ID connector
AADNonInteractiveUserSignInLogs	✅ Yes	Entra ID connector
EmailUrlInfo	✅ Yes	MDO connector
DeviceTvmSoftwareVulnerabilities	❌ No	Use MDE API via `az rest`
DeviceTvmSoftwareInventory	❌ No	Use MDE API via `az rest`

Sample KQL Queries

Use these exact patterns with Azure Monitor MCP. Replace <IOC_VALUE>, <StartDate>, <EndDate>.

⚠️ CRITICAL: These queries have been validated against the KQL schema. Use them as your PRIMARY reference.

📅 Date Range Quick Reference

🔴 STEP 0: GET CURRENT DATE FIRST (MANDATORY) 🔴

ALWAYS check the current date from the context header BEFORE calculating date ranges
NEVER use hardcoded years — the year changes and you WILL query the wrong timeframe

RULE 1: Real-Time/Recent Searches (Current Activity)

Add +2 days to current date for end range
Why +2? +1 for timezone offset + +1 for inclusive end-of-day
Pattern: Today is Jan 23 → Use datetime(2026-01-25) as end date

RULE 2: Historical Searches (User-Specified Dates)

Add +1 day to user's specified end date
Why +1? To include all 24 hours of the final day

Q1. Threat Intelligence Indicator Match

⚠️ CRITICAL — Table Migration (June 2026):

ALWAYS use ThreatIntelIndicators (new STIX table, active since April 2025, fed by Premium MDTI Connector).

NEVER use ThreatIntelligenceIndicator (legacy table, deprecated July 2025, no longer receives data — empty in most workspaces).

Schema is completely different: legacy used NetworkIP/DomainName/Active/ExpirationDateTime; new table uses ObservableKey/ObservableValue/IsActive/ValidUntil/Confidence/Data (STIX JSON).

ThreatIntelObjects is a companion table for STIX objects (threat actors, attack patterns) — query it for attribution context.

Table: ThreatIntelIndicators (Log Analytics — new STIX schema) IoC types: All (IP, Domain, URL, Hash) Replaces: Sentinel query_lake TI lookup + legacy ThreatIntelligenceIndicator

let ioc_value = '<IOC_VALUE>';
ThreatIntelIndicators
| where IsActive and (ValidUntil > now() or isempty(ValidUntil))
| where ObservableValue =~ ioc_value
    or ObservableValue has ioc_value
| extend ParsedData = parse_json(Data)
| extend Description = tostring(ParsedData.description)
| extend IndicatorTypes = tostring(ParsedData.indicator_types)
| extend ThreatLabels = tostring(ParsedData.labels)
| where Description !contains_cs "State: inactive;" and Description !contains_cs "State: falsepos;"
| summarize arg_max(TimeGenerated, *) by ObservableValue
| project
    TimeGenerated,
    IoC = ObservableValue,
    IoC_Type = ObservableKey,
    Confidence,
    Tags,
    SourceSystem,
    ValidFrom,
    ValidUntil,
    Description,
    IndicatorTypes,
    ThreatLabels,
    IsActive
| order by Confidence desc
| take 20

⚠️ Schema notes (validated against live workspace 2026-06-01):

Table name is ThreatIntelIndicators (NOT legacy ThreatIntelligenceIndicator)
Use IsActive (NOT Active), ValidUntil (NOT ExpirationDateTime)
Use ObservableKey + ObservableValue for IoC matching (NOT NetworkIP, DomainName, Url, FileHashValue)
Use Confidence (NOT ConfidenceScore), Data for full STIX JSON (NOT individual fields)
Tags contains activity group labels (e.g., activitygroup:storm-2785,type:ipv4)
SourceSystem identifies the feed (e.g., Premium Microsoft Defender Threat Intelligence)
Zero-results sanity check: if Q1 returns 0 results, verify you queried ThreatIntelIndicators (NOT the legacy table)

Q2. IP Address — Network Connection Activity

Table: DeviceNetworkEvents (Log Analytics) IoC types: IP Replaces: GetDefenderIpStatistics MCP tool

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip or LocalIP == target_ip
| extend Direction = iff(RemoteIP == target_ip, "Outbound", "Inbound")
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniquePorts = dcount(RemotePort),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    Ports = make_set(RemotePort, 20),
    Protocols = make_set(Protocol),
    ActionTypes = make_set(ActionType),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10),
    Directions = make_set(Direction, 2)

Q3. IP Address — Detailed Connection Timeline (top 20)

Table: DeviceNetworkEvents (Log Analytics) IoC types: IP Replaces: FindDefenderMachinesByIp MCP tool (partial)

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip or LocalIP == target_ip
| project 
    Timestamp,
    DeviceName,
    DeviceId,
    ActionType,
    RemoteIP,
    RemotePort,
    RemoteUrl,
    LocalIP,
    LocalPort,
    Protocol,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    InitiatingProcessAccountName
| order by Timestamp desc
| take 20

Q4. Domain — DNS and HTTP Connection Activity

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniqueUsers = dcount(InitiatingProcessAccountName),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    URLs = make_set(RemoteUrl, 20),
    Ports = make_set(RemotePort),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10)

Q5. Domain — Detailed Connection Timeline (top 20)

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| project 
    Timestamp,
    DeviceName,
    InitiatingProcessAccountName,
    ActionType,
    RemoteUrl,
    RemoteIP,
    RemotePort,
    Protocol,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine
| order by Timestamp desc
| take 20

Q6. URL — Email Delivery Analysis

Table: EmailUrlInfo (Log Analytics) IoC types: Domain, URL

let target_url = '<URL>';
let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
EmailUrlInfo
| where Timestamp between (start .. end)
| where Url == target_url or Url has target_domain or UrlDomain =~ target_domain
| summarize 
    EmailCount = dcount(NetworkMessageId),
    UniqueURLs = make_set(Url, 10),
    UrlLocations = make_set(UrlLocation),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp)
    by UrlDomain
| order by EmailCount desc

Q7. File Hash — Device File Events

Table: DeviceFileEvents (Log Analytics) IoC types: Hash

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceFileEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    FileNames = make_set(FileName, 10),
    FolderPaths = make_set(FolderPath, 10),
    ActionTypes = make_set(ActionType),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10)

Q7b. File Hash — Process Events

Table: DeviceProcessEvents (Log Analytics) IoC types: Hash Replaces: GetDefenderFileRelatedMachines MCP tool (partial)

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceProcessEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash 
    or InitiatingProcessSHA256 =~ target_hash or InitiatingProcessSHA1 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    FileNames = make_set(FileName, 10),
    FolderPaths = make_set(FolderPath, 10),
    ProcessCmds = make_set(ProcessCommandLine, 5),
    ActionTypes = make_set(ActionType)

Q7c. File Hash — Cross-Table Search (comprehensive)

Tables: Multiple Device* tables (Log Analytics) IoC types: Hash

⚠️ Note: The union operator may not validate correctly with the KQL Search MCP validator, but is valid KQL and executes correctly.

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
union withsource=SourceTable DeviceProcessEvents, DeviceNetworkEvents, DeviceFileEvents, DeviceRegistryEvents, DeviceLogonEvents, DeviceImageLoadEvents, DeviceEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash or InitiatingProcessSHA256 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    FileNames = make_set(FileName),
    FolderPaths = make_set(FolderPath, 5)
    by SourceTable, ActionType
| order by EventCount desc

Q8. Alert Evidence — IoC in Alerts (top 20)

Table: AlertEvidence (Log Analytics) IoC types: All Replaces: GetDefenderIpAlerts / GetDefenderFileAlerts MCP tools (partial)

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| project 
    Timestamp,
    AlertId,
    Title,
    Severity,
    Categories,
    ServiceSource,
    EntityType,
    EvidenceRole,
    RemoteIP,
    RemoteUrl,
    FileName,
    SHA1,
    SHA256,
    DeviceName,
    AccountName
| order by Timestamp desc
| take 20

⚠️ Schema note: AlertEvidence uses Timestamp (NOT TimeGenerated).

Q9. Security Alerts — Full Alert Correlation

Tables: AlertEvidence + AlertInfo (Log Analytics) IoC types: All Replaces: GetDefenderIpAlerts / GetDefenderFileAlerts MCP tools (with full context)

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| join kind=inner AlertInfo on AlertId
| summarize 
    AlertCount = dcount(AlertId),
    Alerts = make_set(Title, 10),
    Severities = make_set(Severity),
    Categories = make_set(Category),
    AttackTechniques = make_set(AttackTechniques),
    AffectedDevices = make_set(DeviceName, 10)

Q10. Security Alert — Sentinel Native Alerts

Table: SecurityAlert (Log Analytics) IoC types: All

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
SecurityAlert
| where TimeGenerated between (start .. end)
| where Entities has ioc_value 
    or ExtendedProperties has ioc_value 
    or Description has ioc_value
| summarize 
    AlertCount = count(),
    UniqueAlerts = dcount(AlertName),
    AlertNames = make_set(AlertName, 10),
    Severities = make_set(AlertSeverity),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    Providers = make_set(ProviderName),
    Tactics = make_set(Tactics)
| extend SummaryNote = strcat("Total: ", AlertCount, " alerts, ", UniqueAlerts, " unique types")

Q11. IP Address — Sign-in Analysis (Azure AD)

Table: SigninLogs + AADNonInteractiveUserSignInLogs (Log Analytics) IoC types: IP

⚠️ Note: The union isfuzzy=true operator may not validate correctly with the KQL Search MCP validator, but is valid KQL and executes correctly. If validation fails, run each table separately.

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
union isfuzzy=true SigninLogs, AADNonInteractiveUserSignInLogs
| where TimeGenerated between (start .. end)
| where IPAddress == target_ip
| summarize 
    SignInCount = count(),
    UniqueUsers = dcount(UserPrincipalName),
    SuccessCount = countif(ResultType == '0'),
    FailureCount = countif(ResultType != '0'),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    Users = make_set(UserPrincipalName, 10),
    Apps = make_set(AppDisplayName, 10),
    ResultTypes = make_set(ResultType)
| extend SuccessRate = round(100.0 * SuccessCount / SignInCount, 2)

Fallback (if union fails): Run against SigninLogs only.

Q12. CVE Extraction from Alerts

Table: AlertEvidence (Log Analytics) IoC types: All

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| extend CVEs = extract_all(@"(CVE-\d{4}-\d{4,})", tostring(AttackTechniques))
| mv-expand CVE = CVEs
| where isnotempty(CVE)
| summarize 
    CVECount = dcount(tostring(CVE)),
    CVEs = make_set(tostring(CVE)),
    AlertCount = dcount(AlertId),
    Alerts = make_set(Title, 5)

Q13. KQL IP Context Fallback (when enrich_ips.py is unavailable)

Table: DeviceNetworkEvents + SigninLogs (Log Analytics) IoC types: IP Use when: enrich_ips.py is not available (no Python or no API tokens)

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniquePorts = dcount(RemotePort),
    UniqueProcesses = dcount(InitiatingProcessFileName),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    TopDevices = make_set(DeviceName, 5),
    TopPorts = make_set(RemotePort, 10),
    TopProcesses = make_set(InitiatingProcessFileName, 5),
    TopURLs = make_set(RemoteUrl, 5)

Q14. Domain — Resolved IPs Extraction

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain Use for: Extracting resolved IPs from domain connections for subsequent IP enrichment

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| where isnotempty(RemoteIP)
| summarize 
    ConnectionCount = count(),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp)
    by RemoteIP
| order by ConnectionCount desc

Follow-up: Use the extracted RemoteIP values to run enrich_ips.py for 3rd-party enrichment.

MDE API via CLI

For Defender for Endpoint API calls that cannot be replaced by KQL queries, use RunAzCliReadCommands with az rest.

See defender-api-via-cli.md for the complete reference with:

Custom IOC list search
IP alerts and statistics
File info, statistics, alerts, and related machines
Find machines by IP
CVE → affected devices lookup
Error handling and fallback strategies

JSON Export Structure

Create file: temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{timestamp}.json

{
  "investigation_metadata": {
    "ioc_type": "ip|domain|url|hash",
    "ioc_value": "<normalized_value>",
    "ioc_original": "<user_input>",
    "investigation_timestamp": "<ISO8601>",
    "date_range_start": "<StartDate>",
    "date_range_end": "<EndDate>",
    "elapsed_time_seconds": 45
  },
  "threat_intelligence": {
    "sentinel_ti_matches": [],
    "defender_ioc_matches": [],
    "defender_alerts": [],
    "threat_families": [],
    "confidence_score": 0,
    "verdict": "Malicious|Suspicious|Clean|Unknown"
  },
  "ip_enrichment": {
    "geo": { "city": "", "country": "", "org": "", "isp": "" },
    "vpn_proxy_tor": { "is_vpn": false, "is_proxy": false, "is_tor": false },
    "abuseipdb": { "abuse_confidence_score": 0, "total_reports": 0, "last_reported": "", "recent_categories": [] },
    "shodan": { "ports": [], "services": [], "vulns": [], "tags": [], "os": "", "hostnames": [], "cpes": [] }
  },
  "activity_analysis": {
    "network_connections": {
      "total_connections": 0,
      "unique_devices": 0,
      "unique_users": 0,
      "first_seen": "<datetime>",
      "last_seen": "<datetime>",
      "top_devices": [],
      "top_ports": [],
      "top_processes": []
    },
    "email_delivery": {
      "email_count": 0,
      "unique_urls": [],
      "delivery_locations": []
    },
    "file_activity": {
      "event_count": 0,
      "unique_devices": 0,
      "file_names": [],
      "folder_paths": [],
      "action_types": []
    },
    "signin_activity": {
      "signin_count": 0,
      "unique_users": 0,
      "success_rate": 0,
      "affected_users": []
    }
  },
  "alert_correlation": {
    "total_alerts": 0,
    "severity_breakdown": {
      "high": 0,
      "medium": 0,
      "low": 0,
      "informational": 0
    },
    "alert_titles": [],
    "attack_techniques": [],
    "affected_entities": []
  },
  "cve_correlation": {
    "cve_ids_found": [],
    "affected_devices_by_cve": {},
    "total_unique_affected_devices": 0,
    "cve_severity_breakdown": {
      "critical": 0,
      "high": 0,
      "medium": 0,
      "low": 0
    }
  },
  "organizational_exposure": {
    "total_affected_devices": 0,
    "affected_device_list": [],
    "exposure_level": "High|Medium|Low|None",
    "recommended_actions": []
  },
  "risk_assessment": {
    "overall_risk": "Critical|High|Medium|Low|Informational",
    "risk_factors": [],
    "mitigating_factors": [],
    "confidence": "High|Medium|Low"
  }
}

Error Handling

Common Issues and Solutions

Issue	Solution
No TI matches found	IoC may be unknown; proceed with activity analysis
Azure Monitor MCP query fails	Check workspace ID, subscription, and table availability. Retry with corrected params
`az rest` to MDE API returns 403	Managed identity may lack MDE API permissions. Skip MDE enrichment, note in report
`az rest` to MDE API returns 404	IoC not in MDE scope; rely on KQL data from Log Analytics
Empty DeviceNetworkEvents	Expand date range or check if MDE data connector is active
CVE not found in vulnerability DB	CVE may be too new or not applicable to org assets
Multiple IoC types detected	Investigate each separately, correlate results
Rate limiting on MDE API	Add delays between `az rest` calls, batch where possible
`enrich_ips.py` missing or no tokens	Use Q13 (KQL IP Context Fallback) instead
`union` query fails	Run queries against individual tables separately

Required Field Defaults

If queries return no results, use these defaults:

{
  "threat_intelligence": {
    "sentinel_ti_matches": [],
    "defender_alerts": [],
    "verdict": "Unknown",
    "confidence_score": 0
  },
  "activity_analysis": {
    "network_connections": {
      "total_connections": 0,
      "unique_devices": 0
    }
  },
  "cve_correlation": {
    "cve_ids_found": [],
    "affected_devices_by_cve": {},
    "total_unique_affected_devices": 0
  }
}

Example Workflows

Example 1: IP Address Investigation

User says: "Investigate IP 203.0.113.42 for the last 7 days"

Workflow:

Identify IoC: IPv4 Address, normalized: 203.0.113.42
3rd-Party Enrichment:
```
python3 tmp/ioc-investigation/enrich_ips.py 203.0.113.42
```
→ Get geo, ISP, VPN/proxy/Tor flags, AbuseIPDB score, Shodan ports/CVEs/tags
Phase 1 — Threat Intel (parallel via Azure Monitor MCP + az rest):
- Q1: ThreatIntelIndicators query (Azure Monitor MCP)
- MDE-IOC: Custom IOC list search (az rest)
- MDE-IP-ALERTS: IP alerts from MDE API (az rest)
Phase 2 — Activity Analysis (parallel via Azure Monitor MCP):
- Q2: DeviceNetworkEvents connection summary
- Q11: SigninLogs sign-in analysis
- Q8: AlertEvidence IoC in alerts
- Q10: SecurityAlert mentions
Phase 3 — CVE Correlation:
- Q12: Extract CVEs from alerts
- Extract CVEs from Shodan enrichment
- For each CVE: MDE-CVE-MACHINES (az rest)
Export JSON and summarize findings (include enrichment data in JSON export)

Example 2: Domain Investigation

User says: "Is evil-malware.com in our environment?"

Workflow:

Identify IoC: Domain, normalized: evil-malware.com
Phase 1 — Threat Intel (parallel):
- Q1: ThreatIntelIndicators query
- MDE-IOC: Custom IOC list search (az rest)
Phase 2 — Activity Analysis (parallel):
- Q4: DeviceNetworkEvents domain connections
- Q6: EmailUrlInfo email delivery
- Q8: AlertEvidence IoC in alerts
Phase 3 — Resolved IP Enrichment:
- Q14: Extract resolved IPs from domain connections
- Run enrich_ips.py on resolved IPs
Phase 4 — Exposure Assessment:
- List all devices that connected
- Identify affected users
Export JSON and summarize findings

Example 3: File Hash Investigation with CVE Correlation

User says: "Investigate SHA256 a1b2c3... and check which devices are vulnerable"

Workflow:

Identify IoC: SHA256 Hash, normalized: a1b2c3...
Phase 1 — Threat Intel (parallel):
- Q1: ThreatIntelIndicators query
- MDE-FILE-INFO: File info from MDE API (az rest)
- MDE-FILE-ALERTS: File alerts from MDE API (az rest)
- MDE-FILE-STATS: File statistics from MDE API (az rest)
Phase 2 — Device Exposure (parallel):
- MDE-FILE-MACHINES: Devices with file from MDE API (az rest)
- Q7: DeviceFileEvents query
- Q7b: DeviceProcessEvents query
Phase 3 — CVE Correlation:
- Q12: Extract CVEs from alerts
- For each CVE: MDE-CVE-MACHINES (az rest)
- Cross-reference with devices that have the file
Export JSON and summarize with remediation priorities

Security Notes

All investigations are logged for audit purposes
IoC values may be sensitive — handle with care
Follow organizational data classification policies
Consider threat actor attribution implications
Document investigation actions for incident timeline

Integration with Other Skills

This skill can be combined with:

user-investigation: When IoC is found in user's sign-in logs
computer-investigation: When IoC is found on specific device
kql-query-authoring: For custom KQL queries beyond the templates

Cross-skill pivot example: "Investigate IP 203.0.113.42" → Found in user sign-ins → "Investigate user@domain.com" using user-investigation skill

name	ioc-investigation
description	IoC (Indicator of Compromise) investigation skill for environments with Azure Monitor MCP (Log Analytics workspace queries) and Azure CLI access — currently without Sentinel Data Lake MCP, Sentinel Triage MCP, or Microsoft Graph MCP (not yet connectable to Azure SRE Agent; direct API access to Sentinel Data Lake and Microsoft Graph not yet implemented). KQL queries run against Log Analytics tables through the Azure Monitor MCP tool. MDE API calls (custom IOC list, TVM) are executed via RunAzCliReadCommands (az rest). 3rd-party IP enrichment is provided by enrich_ips.py (ipinfo.io, vpnapi.io, AbuseIPDB, Shodan).
threat_pulse_domains	["identity","endpoint","email","exposure"]
drill_down_prompt	Investigate IoC {entity} — threat intel, organizational exposure, affected devices

⚠️ CRITICAL TOOL RULE — ALWAYS PASS --subscription TO MCP MONITOR

When calling monitor-client_monitor_workspace_log_query, the subscription parameter is MANDATORY. Without it, the tool returns a 400 error. Always pass it.

IoC (Indicator of Compromise) Investigation — Monitor MCP + Azure CLI

Purpose

This skill performs comprehensive security investigations on Indicators of Compromise (IoCs) including:

IP Addresses: Network connections, threat intel matches, geographic analysis, organizational exposure
DNS Domains: Domain reputation, connection events, email-based threats, URL analysis
URLs: URL reputation, phishing detection, email delivery, browser activity
File Hashes: Malware analysis, file prevalence, related alerts, affected devices

The investigation correlates IoCs with threat intelligence, identifies associated CVEs, and enumerates organizational assets affected by those vulnerabilities.

Environment: This skill operates in a constrained environment where:

✅ Azure Monitor MCP tool is available (monitor-client_monitor_workspace_log_query) for KQL queries against Log Analytics
✅ RunAzCliReadCommands tool is available for Azure CLI read operations (including az rest for MDE API and Graph API)
✅ KQL Search MCP (mcp_kql-search-mc_*) is available for schema validation and query examples
✅ Microsoft Learn MCP (mcp_microsoft_lea_* / mcp_microsoft_le2_*) is available for documentation
✅ Azure MCP Server (mcp_azure_mcp_ser_*) is available for Azure resource management
✅ enrich_ips.py is included in this skill folder for 3rd-party IP enrichment
❌ Sentinel Data Lake MCP — not integrated (no query_lake, list_sentinel_workspaces, search_tables)
❌ Sentinel Triage MCP — not integrated (no RunAdvancedHuntingQuery, GetDefenderIpAlerts, GetDefenderFileInfo, ListDefenderIndicators, etc.)
❌ Microsoft Graph MCP — not integrated (no microsoft_graph_get, suggest_queries)

Why these MCP servers are absent: Sentinel Data Lake MCP, Sentinel Triage MCP, and Microsoft Graph MCP cannot currently be connected to Azure SRE Agent. This does not mean the underlying data is inaccessible — the data exposed by these servers (Sentinel Data Lake, Defender XDR / Advanced Hunting, Microsoft Graph) can be reached via direct API calls. However, direct API access to Sentinel Data Lake and Microsoft Graph as a replacement for these MCP servers has not yet been studied and implemented in this skill.

Data sources (MDE API via az rest): Custom IOC list, IP alerts/statistics, File info/stats/alerts/machines, CVE→affected devices (TVM).

NOT directly available (require Advanced Hunting in portal): DeviceTvmSoftwareVulnerabilities, DeviceTvmSoftwareInventory.

📑 TABLE OF CONTENTS

Critical Workflow Rules - Start here!
Prerequisites
Environment Configuration
Phase 0: Investigation Cache Check — Cache reuse logic
Investigation Types - By IoC type
Quick Start - 5-step investigation pattern
Execution Workflow - Complete process
KQL Execution Reference - How to run queries
Sample KQL Queries - Validated query patterns
MDE API via CLI - Defender API calls via az rest
JSON Export Structure - Required fields
Error Handling - Troubleshooting guide

Investigation shortcuts:

Suspicious IP from spray/brute-force: Q2 (network connections) → Q11 (sign-in analysis) → Q8 (alert evidence) → Q1 (TI match)
IP from user risk event: Q11 (sign-in analysis) → Q2 (device connections) → Q9 (security alerts) → enrich_ips.py
Phishing domain/URL: Q4 (DNS/HTTP connections) → Q6 (email delivery) → Q8 (alert evidence) → Q1 (TI match)
File hash from incident: Q7 (file events across all tables) → Q9 (security alerts) → MDE-IOC (custom indicator check) → Q12 (CVE extraction)
IoC organizational exposure: Q2/Q4 (affected devices) → Q9 (alert correlation) → Q12 (CVEs from alerts)

⛔ Shortcut Default Rule: When a matching shortcut exists for the investigation context, use it — don't run the full workflow. Only run the full query set when the user explicitly requests "full investigation", "comprehensive", or "deep dive". Shortcuts render only the report sections relevant to their query chain (plus Executive Summary and Recommendations, always).

⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Before starting ANY IoC investigation:

ALWAYS identify the IoC type FIRST (IP, Domain, URL, or File Hash)
ALWAYS normalize the IoC (lowercase domains, validate IP format, extract domain from URL)
ALWAYS complete Phase 0 (Cache Check) after normalization — Before any data collection, check for cached investigation results. See Phase 0 for full logic.
ALWAYS calculate date ranges correctly (use current date from context — see Date Range section)
ALWAYS track and report time after each major step (mandatory)
ALWAYS run independent queries in parallel (drastically faster execution)
ALWAYS use create_file for JSON export (NEVER use PowerShell terminal commands)
ALWAYS use Azure Monitor MCP for KQL execution (see KQL Execution Reference)
ALWAYS use RunAzCliReadCommands for MDE API calls (see defender-api-via-cli.md)

Prerequisites

Dependency	Required	Fallback	Notes
Azure Monitor MCP (`monitor-client_monitor_workspace_log_query`)	✅ Yes	None — core dependency	Must be configured and connected to the target Log Analytics workspace
`RunAzCliReadCommands` tool	⚠️ Optional	KQL-only mode	Used for MDE API calls via `az rest`. If unavailable, skip MDE API enrichment
KQL Search MCP (`mcp_kql-search-mc_*`)	⚠️ Optional	Use queries from this skill directly	Schema validation, query examples
Microsoft Learn MCP (`mcp_microsoft_lea_*`)	⚠️ Optional	N/A	Documentation reference
Python 3.x	⚠️ Optional	Q13 (KQL IP context)	`enrich_ips.py` requires Python + API tokens. If unavailable, use Q13 KQL fallback

Skill Files

File	Purpose
`SKILL.md`	This file — skill instructions, KQL queries, workflow
`enrich_ips.py`	3rd-party IP enrichment (ipinfo.io, vpnapi.io, AbuseIPDB, Shodan)
`generate_html_report.py`	HTML report generator — reads JSON export, produces styled HTML
`defender-api-via-cli.md`	MDE API reference for `az rest` calls

File Resolution (codeRefs-first)

Before executing any skill file (scripts, data files, companion files), resolve its location using this mandatory cascade:

1. codeRefs/sec-sre-ag/ioc-investigation/<filename>
   → If found: use/execute directly from this path (companion files are co-located here)
2. tmp/ioc-investigation/<filename>
   → If found: use from this path
3. Neither found:
   → read_skill_file("ioc-investigation", "<filename>") from Builder
   → CreateFile("tmp/ioc-investigation/<filename>", <content>)
   → Repeat for ALL companion files referenced by the script

Rules:

When a file is found in codeRefs/, execute it directly from there — do NOT copy it to tmp/.
When materializing from Builder (step 3), materialize ALL companion files the script depends on, not just the script itself.
This cascade applies to every file listed in the Skill Files table above.

Pre-requisite: Environment Configuration (config.json)

Procedure:

Check: Verify that config.json exists at the workspace root and contains a non-empty sentinel_workspace_id value. If it does, skip to step 3.
If config.json is missing or incomplete, create it: a. Ask the user for the tenant name using AskUserQuestion with header "Tenant" and question: "What is your tenant name? (e.g., contoso.onmicrosoft.com or contoso.it)?" b. Extract from agent system prompt settings:
- subscription_id → from the <azure_resource_access> section (the subscription ID the agent has access to)
- sentinel_workspace_id → from the <log_analytics_access> section (the workspace GUID after workspace=)
- workspace_name → from the <log_analytics_access> section (the workspace name before the colon) c. Discover the workspace resource group by running:
```
az monitor log-analytics workspace show --workspace-name <workspace_name> --subscription <subscription_id> --query resourceGroup -o tsv
```
d. Create config.json at the workspace root with this structure:
```
{
  "tenant_name": "<tenant_name>",
  "sentinel_workspace_id": "<workspace_guid>",
  "subscription_id": "<subscription_id>",
  "azure_mcp": {
    "subscription_id": "<subscription_id>",
    "resource_group": "<discovered_resource_group>",
    "workspace_name": "<workspace_name>"
  },
  "api_tokens": {}
}
```
Proceed with the skill workflow. All Python scripts find config.json by walking up from their own directory (max 6 levels), so the workspace root is the correct and expected location.

Rules:

Do NOT write config.json inside codeRefs/ or inside skill-specific directories.
Do NOT hardcode any environment-specific values in this skill file — all values are derived at runtime from the agent's own settings and user input.
The api_tokens object is left empty — API tokens are loaded from Key Vault or environment variables at runtime.

Output Modes

Mode	When	What
Inline (DEFAULT — ALWAYS)	Every invocation	Present all findings, threat intel, activity, risk assessment inline in chat
Markdown file	Only if the user explicitly requests	Save investigation report as `.md` file
HTML report	Only if the user explicitly requests	Generate one HTML report per IoC investigated. Present only the HTML report links to the user
JSON export	Internal only — created automatically as intermediate step for HTML generation	Save `ioc_investigation_.json` to `temp/`. NEVER show JSON file links to the user* unless they explicitly ask for JSON export

Rule: Always start with inline presentation. Never skip inline output. The other modes are additive, triggered only by explicit user request. Rule: JSON files are internal artifacts consumed by generate_html_report.py. Do NOT present JSON links in the final output — only present HTML report links.

HTML Report — Conditional Resolution (codeRefs-first)

When the user requests an HTML report:

Export investigation data to JSON: temp/ioc_investigation_{type}_{value}_{ts}.json
Resolve generate_html_report.py via the File Resolution cascade:
- Check codeRefs/sec-sre-ag/ioc-investigation/generate_html_report.py → if found, use that path.
- Else check tmp/ioc-investigation/generate_html_report.py → if found, use that path.
- Else: read_skill_file("ioc-investigation", "generate_html_report.py") → CreateFile("tmp/ioc-investigation/generate_html_report.py", <content>)
Execute: python3 <resolved_path>/generate_html_report.py temp/ioc_investigation_*.json --output-dir reports/ioc-investigation/

Do NOT resolve the script unless the user explicitly requests an HTML report.

Multi-IoC HTML Reports

When multiple IoCs are investigated together (e.g., an IP + a domain), generate one separate JSON export and one separate HTML report per IoC.

Each IoC gets its own temp/ioc_investigation_{type}_{value}_{ts}.json file
Each IoC gets its own HTML report via generate_html_report.py
Do NOT combine multiple IoCs into a single JSON/HTML — the report generator is designed for one IoC per file

Run the generator once per IoC:

python3 tmp/ioc-investigation/generate_html_report.py temp/ioc_investigation_ip_144.24.28.121_*.json --output-dir reports/ioc-investigation/
python3 tmp/ioc-investigation/generate_html_report.py temp/ioc_investigation_domain_ms-teams.us.com_*.json --output-dir reports/ioc-investigation/

Present all generated HTML report links to the user together at the end
NEVER present JSON file links to the user — JSON is an internal intermediate artifact

enrich_ips.py — Conditional Resolution (codeRefs-first)

Before running IP enrichment, resolve enrich_ips.py via the File Resolution cascade:

Check codeRefs/sec-sre-ag/ioc-investigation/enrich_ips.py → if found, run from there.
Else check tmp/ioc-investigation/enrich_ips.py → if found, run from there.
Else: read_skill_file("ioc-investigation", "enrich_ips.py") → CreateFile("tmp/ioc-investigation/enrich_ips.py", <content>) → run from tmp/.
Run: ABUSEIPDB_TOKEN=<value> python3 <resolved_path>/enrich_ips.py <ip1> <ip2> ...

Environment Configuration

Primary: Agent Settings Auto-Discovery (Recommended)

Workspace parameters are automatically available from the agent's system context:

<log_analytics_access> section provides:
- Workspace name (display name)
- Workspace ID (GUID) — use as the workspace parameter
<azure_resource_access> section provides:
- Subscription ID
<agent_identity> section provides:
- Resource group (extractable from the agent's ARM resource ID)

How to extract parameters from agent context:

Parameter	Source	Example
`workspace`	`<log_analytics_access>` → workspace GUID	the workspace ID from `<log_analytics_access>`
`subscription`	`<azure_resource_access>` → subscription ID	the agent's subscription from `<azure_resource_access>`
`resource-group`	`<agent_identity>` → extract from ARM resource ID	the agent's resource group, extracted from its ARM resource ID in `<agent_identity>`

When making Monitor MCP calls, always pass subscription from agent settings. The workspace parameter accepts the workspace GUID directly.

Secondary: config.json (Optional)

If a config.json file exists at the workspace root, it can provide additional configuration:

Field	Used By	Purpose
`azure_mcp.resource_group`	Monitor MCP	Resource group containing the Log Analytics workspace
`azure_mcp.workspace_name`	Monitor MCP	Log Analytics workspace display name
`azure_mcp.tenant`	Monitor MCP, `az rest`	Entra ID tenant
`azure_mcp.subscription`	Monitor MCP	Target Azure subscription
`sentinel_workspace_id`	CLI fallback	Log Analytics workspace GUID
`tenant_id`	Portal URLs	Entra ID tenant ID

Configuration Resolution Order

Agent settings (<log_analytics_access>, <azure_resource_access>) — always available
config.json — read if present, skip if absent
Never prompt the user for workspace parameters if either source is available

Secrets Management (API Tokens)

enrich_ips.py uses 4 external threat intelligence APIs. Each requires an API token. Tokens are resolved by the script in this order of precedence:

Environment variables (highest priority) — always checked
.env file in the script directory — auto-loaded by python-dotenv
config.json at the workspace root — JSON key/value pairs

API	Environment Variable	config.json Key	Purpose
ipinfo.io	`IPINFO_TOKEN`	`ipinfo_token`	Geolocation, ISP/ASN, VPN detection
vpnapi.io	`VPNAPI_TOKEN`	`vpnapi_token`	VPN/proxy/Tor detection
AbuseIPDB	`ABUSEIPDB_TOKEN`	`abuseipdb_token`	Abuse confidence score, reports
Shodan	`SHODAN_TOKEN`	`shodan_token`	Open ports, services, CVEs, tags

IoC Type Detection Rules:

Pattern	IoC Type	Normalization
`\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}`	IPv4 Address	Validate octets ≤255
`[a-fA-F0-9:]+` (with multiple colons)	IPv6 Address	Lowercase, expand if needed
`[a-zA-Z0-9][-a-zA-Z0-9]*\.[a-zA-Z]{2,}`	Domain	Lowercase, remove trailing dot
`https?://.*` or starts with `www.`	URL	Extract domain for separate analysis
32 hex chars	MD5 Hash	Lowercase
40 hex chars	SHA1 Hash	Lowercase
64 hex chars	SHA256 Hash	Lowercase

Date Range Rules:

Real-time/recent searches: Add +2 days to current date for end range
Historical ranges: Add +1 day to user's specified end date
Example: Current date = Jan 23; "Last 7 days" → datetime(2026-01-16) to datetime(2026-01-25)

Available Investigation Types

IP Address Investigation

When to use: Suspicious inbound/outbound connections, firewall alerts, sign-in anomalies

Example prompts:

"Investigate IP 203.0.113.42"
"Is 198.51.100.10 malicious?"
"Check threat intel for 192.0.2.1"

Data sources:

ThreatIntelIndicators (new STIX table via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
DeviceNetworkEvents (connection history via Log Analytics)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)
SecurityAlert (security alerts via Log Analytics)
SigninLogs (if used for authentication via Log Analytics)
MDE Custom IOC list (via az rest — see defender-api-via-cli.md)
MDE IP alerts/statistics (via az rest — see defender-api-via-cli.md)
enrich_ips.py (3rd-party enrichment: ipinfo.io geo/ISP, vpnapi.io VPN/proxy/Tor, AbuseIPDB abuse score & reports, Shodan ports/services/CVEs/tags)

Domain Investigation

When to use: Suspicious DNS queries, phishing domains, C2 communication

Example prompts:

"Investigate domain malware-c2.example.com"
"Is evil.com in our threat intel?"
"Check if any devices connected to suspicious.net"

Data sources:

DeviceNetworkEvents (DNS queries, HTTP connections via Log Analytics)
EmailUrlInfo (email-delivered URLs via Log Analytics)
ThreatIntelIndicators (domain indicators via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
MDE Custom IOC list (via az rest — see defender-api-via-cli.md)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)

URL Investigation

When to use: Phishing links, malicious downloads, suspicious redirects

Example prompts:

"Investigate URL https://phishing.example.com/login"
"Was this URL clicked by anyone?"
"Check threat intel for http://malware.site/payload.exe"

Data sources:

EmailUrlInfo (URLs in emails via Log Analytics)
DeviceNetworkEvents (HTTP/HTTPS connections via Log Analytics)
DeviceFileEvents (downloads from URL via Log Analytics)
ThreatIntelIndicators (URL patterns via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)

File Hash Investigation

When to use: Malware analysis, suspicious executables, file reputation

Example prompts:

"Investigate hash a1b2c3d4e5f6..."
"Is this SHA256 known malware?"
"Which devices have this file?"

Data sources:

DeviceFileEvents (file creation/modification via Log Analytics)
DeviceProcessEvents (process execution with hash via Log Analytics)
AlertEvidence + AlertInfo (alert correlation via Log Analytics)
ThreatIntelIndicators (file hash indicators via Log Analytics — ⚠️ NOT legacy ThreatIntelligenceIndicator)
MDE File info/stats/alerts/machines (via az rest — see defender-api-via-cli.md)

Quick Start (TL;DR)

When a user requests an IoC investigation:

Identify & Normalize IoC:

- Detect IoC type (IP/Domain/URL/Hash)
- Normalize format (lowercase, validate)
- Extract embedded IoCs (domain from URL)

Run Parallel KQL Queries (Batch 1 — Threat Intel):
- Q1: ThreatIntelIndicators query (via Azure Monitor MCP — ⚠️ NOT legacy ThreatIntelligenceIndicator)
- MDE-IOC: Custom IOC list search (via RunAzCliReadCommands + az rest)
- MDE-ALERTS: IP/File alerts from MDE API (via RunAzCliReadCommands + az rest)
Run 3rd-Party IP Enrichment (IP IoCs only):
```
python3 tmp/ioc-investigation/enrich_ips.py <IP_ADDRESS>
```
- ipinfo.io: Geolocation, ISP/ASN, hosting provider
- vpnapi.io: VPN, proxy, Tor exit node detection
- AbuseIPDB: Abuse confidence score, recent attack reports
- Shodan: Open ports, services/banners, CVEs, tags (e.g., c2, eol-os, self-signed)
Run Parallel KQL Queries (Batch 2 — Activity):
- Q2/Q3: DeviceNetworkEvents (connections involving IoC)
- Q8: AlertEvidence (alerts with IoC as evidence)
- Q9: AlertEvidence + AlertInfo (full alert correlation)
- Q10: SecurityAlert (mentions of IoC)
- Q6: EmailUrlInfo (if domain/URL)
- Q11: SigninLogs (if IP)
CVE & Vulnerability Correlation:
- Extract CVE IDs from alert results AND Shodan enrichment
- For each CVE: query affected devices via MDE API (az rest)
- Aggregate affected devices

Export to JSON & Generate Summary:

temp/ioc_investigation_{ioc_normalized}_{timestamp}.json

Phase 0: Investigation Cache Check (MANDATORY)

This phase MUST execute BEFORE any data collection. It determines whether to reuse cached investigation data or start a fresh investigation.

0.1 Cache File Convention

Investigation results are stored as JSON files following this naming pattern:

temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{YYYYMMDD_HHMMSS}.json

0.2 Cache Check Workflow

Step 0.1: After IoC normalization (Phase 1), search for existing cache files
          → Use: ls temp/ioc_investigation_{ioc_type}_{ioc_normalized}_*.json
          → If NO cache file exists → proceed to Phase 2 (fresh investigation)

Step 0.2: If one or more cache files exist, select the MOST RECENT one (latest timestamp)

Step 0.3: Calculate the cache age:
          → Extract timestamp from filename (YYYYMMDD_HHMMSS format)
          → age = current_UTC_time − cache_file_timestamp
          → If age > 4 hours → IGNORE cache entirely, proceed to Phase 2 (fresh investigation)
          → If age ≤ 4 hours → proceed to Step 0.4

Step 0.4: Analyze the user's ORIGINAL prompt for implicit intent:

          REDO KEYWORDS (triggers fresh investigation, any language):
            "ripeti", "aggiorna", "rifai", "repeat", "redo", "refresh",
            "update", "re-investigate", "start over", "da capo",
            "from scratch", "ricomincia", "nuovo", "nuova analisi"
          → If ANY redo keyword is detected → IGNORE cache, proceed to Phase 2

          USE-CACHE KEYWORDS (triggers cache reuse, any language):
            "completa", "continua", "complete", "continue", "finish",
            "usa i dati", "use cached", "use existing", "prosegui",
            "riprendi", "resume", "genera report", "generate report",
            "genera il report", "crea report"
          → If ANY use-cache keyword is detected → LOAD cache, skip to summary

          NO IMPLICIT INTENT DETECTED:
          → Proceed to Step 0.5 (ask the user)

Step 0.5: ASK the user:

          Question: "Ho trovato risultati di un'investigazione precedente per
                     l'IoC <IOC_VALUE> (<IOC_TYPE>), completata <TIME_AGO> fa
                     (alle <HH:MM> UTC).
                     Vuoi utilizzare questi dati o preferisci ripetere
                     l'investigazione da zero?"
          Options:
            1. "Usa i dati esistenti" — Riprende dall'investigazione precedente
            2. "Ripeti da zero" — Ignora la cache e ricomincia

          → If user selects "Usa i dati esistenti" → LOAD cache, present summary
          → If user selects "Ripeti da zero" → proceed to Phase 2

Step 0.6: LOAD cached data:
          → Read the JSON file
          → Present a brief inline summary of cached findings
          → Offer HTML/report generation or further analysis

0.3 Cache Decision Summary

Cache Exists?	Age	User Prompt	Action
No	—	—	Fresh investigation
Yes	> 4 hours	—	Fresh investigation — cache expired
Yes	≤ 4 hours	Contains REDO keyword	Fresh investigation
Yes	≤ 4 hours	Contains USE-CACHE keyword	Load cache
Yes	≤ 4 hours	No implicit intent	ASK user

0.4 Important Rules

NEVER silently reuse cached data — always either detect explicit intent from the prompt or ask the user.
NEVER ask the user if the prompt already contains an implicit answer — detect keywords first.
When loading cache, always show what was already completed — the user must understand what data is from cache vs. new queries.
Cache files from a DIFFERENT thread/session are still valid — the 4-hour TTL is the only expiration criterion.
If the user later requests a fresh investigation after loading cache — discard all cached data and restart.

Execution Workflow

🚨 MANDATORY: Time Tracking Pattern

YOU MUST TRACK AND REPORT TIME AFTER EVERY MAJOR STEP:

[MM:SS] ✓ Step description (XX seconds)

Required Reporting Points:

After IoC normalization and type detection
After 3rd-party IP enrichment (IP IoCs)
After threat intelligence lookup (KQL + MDE API)
After activity/connection analysis
After CVE correlation and device enumeration
After JSON file creation
Final: Total elapsed time

Phase 1: IoC Identification and Normalization (REQUIRED FIRST)

Step 1.1: Detect IoC Type

# Regex patterns for IoC detection
IPv4: r'^(\d{1,3}\.){3}\d{1,3}$'
IPv6: r'^([0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}$'
Domain: r'^([a-zA-Z0-9]([a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,}$'
URL: r'^https?://'
MD5: r'^[a-fA-F0-9]{32}$'
SHA1: r'^[a-fA-F0-9]{40}$'
SHA256: r'^[a-fA-F0-9]{64}$'

Step 1.2: Normalize IoC

IP Address: Validate octets, detect IPv4 vs IPv6
Domain: Lowercase, remove trailing dots, extract from URL if needed
URL: Keep full URL, also extract domain for parallel investigation
Hash: Lowercase

Step 1.3: Create Investigation Context

{
  "ioc_type": "ip|domain|url|hash",
  "ioc_value": "<normalized_value>",
  "ioc_original": "<user_provided_value>",
  "extracted_domain": "<if_url>",
  "investigation_start": "<timestamp>",
  "date_range_start": "<StartDate>",
  "date_range_end": "<EndDate>"
}

Phase 2: 3rd-Party IP Enrichment (IP Address IoCs)

MANDATORY for all IP address investigations. Run enrich_ips.py to get external threat intelligence context that is NOT available from Defender/Sentinel native tools.

Resolve and run:

Resolve enrich_ips.py via the File Resolution cascade:
- Check codeRefs/sec-sre-ag/ioc-investigation/enrich_ips.py → if found, run from there.
- Else check tmp/ioc-investigation/enrich_ips.py → if found, run from there.
- Else: read_skill_file("ioc-investigation", "enrich_ips.py") → CreateFile("tmp/ioc-investigation/enrich_ips.py", <content>) → run from tmp/.
Run:

ABUSEIPDB_TOKEN=<value> python3 <resolved_path>/enrich_ips.py <IP_ADDRESS_1> <IP_ADDRESS_2> ...

What it provides:

Source	Intelligence
ipinfo.io	Geolocation (city, country, coordinates), ISP/ASN, organization, hosting provider detection
vpnapi.io	VPN, proxy, Tor exit node, relay detection
AbuseIPDB	Abuse confidence score (0-100), total reports, last reported date, recent reporter comments with attack categories
Shodan	Open ports, service/banner details, OS detection, known CVEs, tags (e.g., `c2`, `eol-os`, `self-signed`, `honeypot`), CPEs, hostnames

Output: Per-IP detailed results printed to terminal + JSON export saved to temp/.

Integration with investigation:

AbuseIPDB score ≥ 75: 🔴 Strong indicator of malicious activity — flag as high risk
VPN/Proxy/Tor detected: 🟠 Potential evasion — note in risk assessment
Shodan tags contain c2: 🔴 Known C2 infrastructure — escalate immediately
Shodan CVEs found: Cross-reference with Phase 5 CVE correlation for organizational exposure
Hosting provider (not residential ISP): 🟡 May indicate attacker infrastructure

Note: For domain and URL IoCs, extract the resolved IP(s) from DeviceNetworkEvents results and run enrichment on those IPs as a follow-up step.

If enrich_ips.py is not available (no Python, no API tokens): Use Q13 (KQL IP Context Fallback) to get whatever context is available from Log Analytics tables.

Phase 3: Parallel Threat Intelligence Collection (KQL + MDE API)

CRITICAL: Run ALL threat intel queries in parallel for speed!

Pre-requisite: Read MDE API Reference (MANDATORY)

Before executing ANY MDE API call (az rest), you MUST read the reference file:

read_skill_file(skill_name="ioc-investigation", file_path="defender-api-via-cli.md")

If the file has already been read in this session, skip this step.
Do NOT attempt MDE API calls from memory — the URLs and --resource parameter are precise and must match exactly.
If the first MDE API call returns 403 → skip ALL remaining MDE API calls and note "MDE API not accessible — KQL-only mode" in the report.

Batch 1: KQL Queries via Azure Monitor MCP (Run ALL in parallel)

Query	Table	IoC Types	Details
Q1	ThreatIntelIndicators	All	TI indicator match (STIX schema)
Q8	AlertEvidence	All	IoC in alert evidence
Q10	SecurityAlert	All	Security alerts mentioning IoC

Batch 2: MDE API Calls via `RunAzCliReadCommands` (Run ALL in parallel)

Call	Endpoint	IoC Types	Details
MDE-IOC	`/api/indicators`	All	Custom IOC list match
MDE-IP-ALERTS	`/api/ips/{ip}/alerts`	IP	Alerts for IP
MDE-IP-STATS	`/api/ips/{ip}/stats`	IP	IP organization prevalence
MDE-FILE-INFO	`/api/files/{hash}`	Hash	File details, threat determination
MDE-FILE-ALERTS	`/api/files/{hash}/alerts`	Hash	File-related alerts
MDE-FILE-STATS	`/api/files/{hash}/stats`	Hash	File organization statistics
MDE-FILE-MACHINES	`/api/files/{hash}/machines`	Hash	Devices with file

See defender-api-via-cli.md for exact az rest commands.

Phase 4: Activity and Connection Analysis (KQL via Azure Monitor MCP)

Run ALL activity queries in parallel!

Query	Table	IoC Types	Details
Q2	DeviceNetworkEvents	IP	Connection summary
Q3	DeviceNetworkEvents	IP	Connection timeline (top 20)
Q4	DeviceNetworkEvents	Domain	DNS/HTTP connection activity
Q5	DeviceNetworkEvents	Domain	Connection timeline (top 20)
Q6	EmailUrlInfo	Domain, URL	Email delivery analysis
Q7	DeviceFileEvents	Hash	File events across tables
Q7b	DeviceProcessEvents	Hash	Process events with hash
Q9	AlertEvidence + AlertInfo	All	Full alert correlation with details
Q11	SigninLogs	IP	Sign-in analysis (Azure AD)

Phase 5: CVE Correlation and Vulnerability Management

Step 5.1: Extract CVE IDs from Results AND Enrichment

Parse alert results for CVE references (pattern: CVE-\d{4}-\d{4,})
Extract from: alert descriptions, attack techniques, MITRE info
Extract from Shodan enrichment (shodan_vulns field from enrich_ips.py output)
Run Q12 (CVE Extraction from Alerts) to collect all CVE IDs

Step 5.2: Query Affected Devices per CVE

Use the MDE API via RunAzCliReadCommands:

For each CVE_ID found:
  → MDE-CVE-MACHINES: az rest to /api/vulnerabilities/{cveId}/machineReferences
  → Collect: deviceId, deviceName, osPlatform, rbacGroupName

See defender-api-via-cli.md for exact az rest commands.

Step 5.3: Aggregate Device Exposure

{
  "cve_correlation": {
    "cve_ids_found": ["CVE-2024-1234", "CVE-2024-5678"],
    "affected_devices_by_cve": {
      "CVE-2024-1234": [
        {"deviceId": "...", "deviceName": "...", "osPlatform": "..."}
      ]
    },
    "total_unique_affected_devices": 15,
    "critical_cves": 2,
    "high_cves": 3
  }
}

Phase 6: Export to JSON

Create single JSON file: temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{timestamp}.json

KQL Execution Reference

How to Run KQL Queries

All KQL queries in this skill are executed via the Azure Monitor MCP tool:

Tool: monitor-client_monitor_workspace_log_query
Parameters:
  workspace: <WORKSPACE_GUID>          # From agent settings <log_analytics_access>
  subscription: <SUBSCRIPTION_ID>      # From agent settings <azure_resource_access>
  query: "<KQL_QUERY>"                 # Query from templates below
  timespan: "P7D"                      # ISO 8601 duration (P7D = 7 days, P30D = 30 days)

Important notes:

The workspace parameter accepts the Log Analytics workspace GUID
The subscription parameter is REQUIRED
Date ranges in the query (datetime(...) / between) take precedence over timespan
Always use let variables for IoC values and dates at the start of each query

Table Availability

Table	Available in Log Analytics	Notes
ThreatIntelIndicators	✅ Yes	New STIX table (active since Apr 2025). ⚠️ Do NOT use legacy `ThreatIntelligenceIndicator` (deprecated July 2025, empty)
DeviceNetworkEvents	✅ Yes	MDE data connector
DeviceProcessEvents	✅ Yes	MDE data connector
DeviceFileEvents	✅ Yes	MDE data connector
DeviceRegistryEvents	✅ Yes	MDE data connector
DeviceLogonEvents	✅ Yes	MDE data connector
DeviceImageLoadEvents	✅ Yes	MDE data connector
DeviceEvents	✅ Yes	MDE data connector
AlertEvidence	✅ Yes	M365 Defender connector
AlertInfo	✅ Yes	M365 Defender connector
SecurityAlert	✅ Yes	Sentinel native
SigninLogs	✅ Yes	Entra ID connector
AADNonInteractiveUserSignInLogs	✅ Yes	Entra ID connector
EmailUrlInfo	✅ Yes	MDO connector
DeviceTvmSoftwareVulnerabilities	❌ No	Use MDE API via `az rest`
DeviceTvmSoftwareInventory	❌ No	Use MDE API via `az rest`

Sample KQL Queries

Use these exact patterns with Azure Monitor MCP. Replace <IOC_VALUE>, <StartDate>, <EndDate>.

⚠️ CRITICAL: These queries have been validated against the KQL schema. Use them as your PRIMARY reference.

📅 Date Range Quick Reference

🔴 STEP 0: GET CURRENT DATE FIRST (MANDATORY) 🔴

ALWAYS check the current date from the context header BEFORE calculating date ranges
NEVER use hardcoded years — the year changes and you WILL query the wrong timeframe

RULE 1: Real-Time/Recent Searches (Current Activity)

Add +2 days to current date for end range
Why +2? +1 for timezone offset + +1 for inclusive end-of-day
Pattern: Today is Jan 23 → Use datetime(2026-01-25) as end date

RULE 2: Historical Searches (User-Specified Dates)

Add +1 day to user's specified end date
Why +1? To include all 24 hours of the final day

Q1. Threat Intelligence Indicator Match

⚠️ CRITICAL — Table Migration (June 2026):

ALWAYS use ThreatIntelIndicators (new STIX table, active since April 2025, fed by Premium MDTI Connector).

NEVER use ThreatIntelligenceIndicator (legacy table, deprecated July 2025, no longer receives data — empty in most workspaces).

Schema is completely different: legacy used NetworkIP/DomainName/Active/ExpirationDateTime; new table uses ObservableKey/ObservableValue/IsActive/ValidUntil/Confidence/Data (STIX JSON).

ThreatIntelObjects is a companion table for STIX objects (threat actors, attack patterns) — query it for attribution context.

Table: ThreatIntelIndicators (Log Analytics — new STIX schema) IoC types: All (IP, Domain, URL, Hash) Replaces: Sentinel query_lake TI lookup + legacy ThreatIntelligenceIndicator

let ioc_value = '<IOC_VALUE>';
ThreatIntelIndicators
| where IsActive and (ValidUntil > now() or isempty(ValidUntil))
| where ObservableValue =~ ioc_value
    or ObservableValue has ioc_value
| extend ParsedData = parse_json(Data)
| extend Description = tostring(ParsedData.description)
| extend IndicatorTypes = tostring(ParsedData.indicator_types)
| extend ThreatLabels = tostring(ParsedData.labels)
| where Description !contains_cs "State: inactive;" and Description !contains_cs "State: falsepos;"
| summarize arg_max(TimeGenerated, *) by ObservableValue
| project
    TimeGenerated,
    IoC = ObservableValue,
    IoC_Type = ObservableKey,
    Confidence,
    Tags,
    SourceSystem,
    ValidFrom,
    ValidUntil,
    Description,
    IndicatorTypes,
    ThreatLabels,
    IsActive
| order by Confidence desc
| take 20

⚠️ Schema notes (validated against live workspace 2026-06-01):

Table name is ThreatIntelIndicators (NOT legacy ThreatIntelligenceIndicator)
Use IsActive (NOT Active), ValidUntil (NOT ExpirationDateTime)
Use ObservableKey + ObservableValue for IoC matching (NOT NetworkIP, DomainName, Url, FileHashValue)
Use Confidence (NOT ConfidenceScore), Data for full STIX JSON (NOT individual fields)
Tags contains activity group labels (e.g., activitygroup:storm-2785,type:ipv4)
SourceSystem identifies the feed (e.g., Premium Microsoft Defender Threat Intelligence)
Zero-results sanity check: if Q1 returns 0 results, verify you queried ThreatIntelIndicators (NOT the legacy table)

Q2. IP Address — Network Connection Activity

Table: DeviceNetworkEvents (Log Analytics) IoC types: IP Replaces: GetDefenderIpStatistics MCP tool

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip or LocalIP == target_ip
| extend Direction = iff(RemoteIP == target_ip, "Outbound", "Inbound")
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniquePorts = dcount(RemotePort),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    Ports = make_set(RemotePort, 20),
    Protocols = make_set(Protocol),
    ActionTypes = make_set(ActionType),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10),
    Directions = make_set(Direction, 2)

Q3. IP Address — Detailed Connection Timeline (top 20)

Table: DeviceNetworkEvents (Log Analytics) IoC types: IP Replaces: FindDefenderMachinesByIp MCP tool (partial)

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip or LocalIP == target_ip
| project 
    Timestamp,
    DeviceName,
    DeviceId,
    ActionType,
    RemoteIP,
    RemotePort,
    RemoteUrl,
    LocalIP,
    LocalPort,
    Protocol,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    InitiatingProcessAccountName
| order by Timestamp desc
| take 20

Q4. Domain — DNS and HTTP Connection Activity

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniqueUsers = dcount(InitiatingProcessAccountName),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    URLs = make_set(RemoteUrl, 20),
    Ports = make_set(RemotePort),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10)

Q5. Domain — Detailed Connection Timeline (top 20)

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| project 
    Timestamp,
    DeviceName,
    InitiatingProcessAccountName,
    ActionType,
    RemoteUrl,
    RemoteIP,
    RemotePort,
    Protocol,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine
| order by Timestamp desc
| take 20

Q6. URL — Email Delivery Analysis

Table: EmailUrlInfo (Log Analytics) IoC types: Domain, URL

let target_url = '<URL>';
let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
EmailUrlInfo
| where Timestamp between (start .. end)
| where Url == target_url or Url has target_domain or UrlDomain =~ target_domain
| summarize 
    EmailCount = dcount(NetworkMessageId),
    UniqueURLs = make_set(Url, 10),
    UrlLocations = make_set(UrlLocation),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp)
    by UrlDomain
| order by EmailCount desc

Q7. File Hash — Device File Events

Table: DeviceFileEvents (Log Analytics) IoC types: Hash

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceFileEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    FileNames = make_set(FileName, 10),
    FolderPaths = make_set(FolderPath, 10),
    ActionTypes = make_set(ActionType),
    InitiatingProcesses = make_set(InitiatingProcessFileName, 10)

Q7b. File Hash — Process Events

Table: DeviceProcessEvents (Log Analytics) IoC types: Hash Replaces: GetDefenderFileRelatedMachines MCP tool (partial)

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceProcessEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash 
    or InitiatingProcessSHA256 =~ target_hash or InitiatingProcessSHA1 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    Devices = make_set(DeviceName, 10),
    FileNames = make_set(FileName, 10),
    FolderPaths = make_set(FolderPath, 10),
    ProcessCmds = make_set(ProcessCommandLine, 5),
    ActionTypes = make_set(ActionType)

Q7c. File Hash — Cross-Table Search (comprehensive)

Tables: Multiple Device* tables (Log Analytics) IoC types: Hash

⚠️ Note: The union operator may not validate correctly with the KQL Search MCP validator, but is valid KQL and executes correctly.

let target_hash = '<HASH>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
union withsource=SourceTable DeviceProcessEvents, DeviceNetworkEvents, DeviceFileEvents, DeviceRegistryEvents, DeviceLogonEvents, DeviceImageLoadEvents, DeviceEvents
| where Timestamp between (start .. end)
| where SHA1 =~ target_hash or SHA256 =~ target_hash or MD5 =~ target_hash or InitiatingProcessSHA256 =~ target_hash
| summarize 
    EventCount = count(),
    UniqueDevices = dcount(DeviceId),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    FileNames = make_set(FileName),
    FolderPaths = make_set(FolderPath, 5)
    by SourceTable, ActionType
| order by EventCount desc

Q8. Alert Evidence — IoC in Alerts (top 20)

Table: AlertEvidence (Log Analytics) IoC types: All Replaces: GetDefenderIpAlerts / GetDefenderFileAlerts MCP tools (partial)

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| project 
    Timestamp,
    AlertId,
    Title,
    Severity,
    Categories,
    ServiceSource,
    EntityType,
    EvidenceRole,
    RemoteIP,
    RemoteUrl,
    FileName,
    SHA1,
    SHA256,
    DeviceName,
    AccountName
| order by Timestamp desc
| take 20

⚠️ Schema note: AlertEvidence uses Timestamp (NOT TimeGenerated).

Q9. Security Alerts — Full Alert Correlation

Tables: AlertEvidence + AlertInfo (Log Analytics) IoC types: All Replaces: GetDefenderIpAlerts / GetDefenderFileAlerts MCP tools (with full context)

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| join kind=inner AlertInfo on AlertId
| summarize 
    AlertCount = dcount(AlertId),
    Alerts = make_set(Title, 10),
    Severities = make_set(Severity),
    Categories = make_set(Category),
    AttackTechniques = make_set(AttackTechniques),
    AffectedDevices = make_set(DeviceName, 10)

Q10. Security Alert — Sentinel Native Alerts

Table: SecurityAlert (Log Analytics) IoC types: All

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
SecurityAlert
| where TimeGenerated between (start .. end)
| where Entities has ioc_value 
    or ExtendedProperties has ioc_value 
    or Description has ioc_value
| summarize 
    AlertCount = count(),
    UniqueAlerts = dcount(AlertName),
    AlertNames = make_set(AlertName, 10),
    Severities = make_set(AlertSeverity),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    Providers = make_set(ProviderName),
    Tactics = make_set(Tactics)
| extend SummaryNote = strcat("Total: ", AlertCount, " alerts, ", UniqueAlerts, " unique types")

Q11. IP Address — Sign-in Analysis (Azure AD)

Table: SigninLogs + AADNonInteractiveUserSignInLogs (Log Analytics) IoC types: IP

⚠️ Note: The union isfuzzy=true operator may not validate correctly with the KQL Search MCP validator, but is valid KQL and executes correctly. If validation fails, run each table separately.

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
union isfuzzy=true SigninLogs, AADNonInteractiveUserSignInLogs
| where TimeGenerated between (start .. end)
| where IPAddress == target_ip
| summarize 
    SignInCount = count(),
    UniqueUsers = dcount(UserPrincipalName),
    SuccessCount = countif(ResultType == '0'),
    FailureCount = countif(ResultType != '0'),
    FirstSeen = min(TimeGenerated),
    LastSeen = max(TimeGenerated),
    Users = make_set(UserPrincipalName, 10),
    Apps = make_set(AppDisplayName, 10),
    ResultTypes = make_set(ResultType)
| extend SuccessRate = round(100.0 * SuccessCount / SignInCount, 2)

Fallback (if union fails): Run against SigninLogs only.

Q12. CVE Extraction from Alerts

Table: AlertEvidence (Log Analytics) IoC types: All

let ioc_value = '<IOC_VALUE>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
AlertEvidence
| where Timestamp between (start .. end)
| where RemoteIP == ioc_value 
    or RemoteUrl has ioc_value 
    or SHA1 =~ ioc_value 
    or SHA256 =~ ioc_value
    or FileName has ioc_value
    or Title has ioc_value
| extend CVEs = extract_all(@"(CVE-\d{4}-\d{4,})", tostring(AttackTechniques))
| mv-expand CVE = CVEs
| where isnotempty(CVE)
| summarize 
    CVECount = dcount(tostring(CVE)),
    CVEs = make_set(tostring(CVE)),
    AlertCount = dcount(AlertId),
    Alerts = make_set(Title, 5)

Q13. KQL IP Context Fallback (when enrich_ips.py is unavailable)

Table: DeviceNetworkEvents + SigninLogs (Log Analytics) IoC types: IP Use when: enrich_ips.py is not available (no Python or no API tokens)

let target_ip = '<IP_ADDRESS>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteIP == target_ip
| summarize 
    TotalConnections = count(),
    UniqueDevices = dcount(DeviceId),
    UniquePorts = dcount(RemotePort),
    UniqueProcesses = dcount(InitiatingProcessFileName),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    TopDevices = make_set(DeviceName, 5),
    TopPorts = make_set(RemotePort, 10),
    TopProcesses = make_set(InitiatingProcessFileName, 5),
    TopURLs = make_set(RemoteUrl, 5)

Q14. Domain — Resolved IPs Extraction

Table: DeviceNetworkEvents (Log Analytics) IoC types: Domain Use for: Extracting resolved IPs from domain connections for subsequent IP enrichment

let target_domain = '<DOMAIN>';
let start = datetime(<StartDate>);
let end = datetime(<EndDate>);
DeviceNetworkEvents
| where Timestamp between (start .. end)
| where RemoteUrl has target_domain
| where isnotempty(RemoteIP)
| summarize 
    ConnectionCount = count(),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp)
    by RemoteIP
| order by ConnectionCount desc

Follow-up: Use the extracted RemoteIP values to run enrich_ips.py for 3rd-party enrichment.

MDE API via CLI

For Defender for Endpoint API calls that cannot be replaced by KQL queries, use RunAzCliReadCommands with az rest.

See defender-api-via-cli.md for the complete reference with:

Custom IOC list search
IP alerts and statistics
File info, statistics, alerts, and related machines
Find machines by IP
CVE → affected devices lookup
Error handling and fallback strategies

JSON Export Structure

Create file: temp/ioc_investigation_{ioc_type}_{ioc_normalized}_{timestamp}.json

{
  "investigation_metadata": {
    "ioc_type": "ip|domain|url|hash",
    "ioc_value": "<normalized_value>",
    "ioc_original": "<user_input>",
    "investigation_timestamp": "<ISO8601>",
    "date_range_start": "<StartDate>",
    "date_range_end": "<EndDate>",
    "elapsed_time_seconds": 45
  },
  "threat_intelligence": {
    "sentinel_ti_matches": [],
    "defender_ioc_matches": [],
    "defender_alerts": [],
    "threat_families": [],
    "confidence_score": 0,
    "verdict": "Malicious|Suspicious|Clean|Unknown"
  },
  "ip_enrichment": {
    "geo": { "city": "", "country": "", "org": "", "isp": "" },
    "vpn_proxy_tor": { "is_vpn": false, "is_proxy": false, "is_tor": false },
    "abuseipdb": { "abuse_confidence_score": 0, "total_reports": 0, "last_reported": "", "recent_categories": [] },
    "shodan": { "ports": [], "services": [], "vulns": [], "tags": [], "os": "", "hostnames": [], "cpes": [] }
  },
  "activity_analysis": {
    "network_connections": {
      "total_connections": 0,
      "unique_devices": 0,
      "unique_users": 0,
      "first_seen": "<datetime>",
      "last_seen": "<datetime>",
      "top_devices": [],
      "top_ports": [],
      "top_processes": []
    },
    "email_delivery": {
      "email_count": 0,
      "unique_urls": [],
      "delivery_locations": []
    },
    "file_activity": {
      "event_count": 0,
      "unique_devices": 0,
      "file_names": [],
      "folder_paths": [],
      "action_types": []
    },
    "signin_activity": {
      "signin_count": 0,
      "unique_users": 0,
      "success_rate": 0,
      "affected_users": []
    }
  },
  "alert_correlation": {
    "total_alerts": 0,
    "severity_breakdown": {
      "high": 0,
      "medium": 0,
      "low": 0,
      "informational": 0
    },
    "alert_titles": [],
    "attack_techniques": [],
    "affected_entities": []
  },
  "cve_correlation": {
    "cve_ids_found": [],
    "affected_devices_by_cve": {},
    "total_unique_affected_devices": 0,
    "cve_severity_breakdown": {
      "critical": 0,
      "high": 0,
      "medium": 0,
      "low": 0
    }
  },
  "organizational_exposure": {
    "total_affected_devices": 0,
    "affected_device_list": [],
    "exposure_level": "High|Medium|Low|None",
    "recommended_actions": []
  },
  "risk_assessment": {
    "overall_risk": "Critical|High|Medium|Low|Informational",
    "risk_factors": [],
    "mitigating_factors": [],
    "confidence": "High|Medium|Low"
  }
}

Error Handling

Common Issues and Solutions

Issue	Solution
No TI matches found	IoC may be unknown; proceed with activity analysis
Azure Monitor MCP query fails	Check workspace ID, subscription, and table availability. Retry with corrected params
`az rest` to MDE API returns 403	Managed identity may lack MDE API permissions. Skip MDE enrichment, note in report
`az rest` to MDE API returns 404	IoC not in MDE scope; rely on KQL data from Log Analytics
Empty DeviceNetworkEvents	Expand date range or check if MDE data connector is active
CVE not found in vulnerability DB	CVE may be too new or not applicable to org assets
Multiple IoC types detected	Investigate each separately, correlate results
Rate limiting on MDE API	Add delays between `az rest` calls, batch where possible
`enrich_ips.py` missing or no tokens	Use Q13 (KQL IP Context Fallback) instead
`union` query fails	Run queries against individual tables separately

Required Field Defaults

If queries return no results, use these defaults:

{
  "threat_intelligence": {
    "sentinel_ti_matches": [],
    "defender_alerts": [],
    "verdict": "Unknown",
    "confidence_score": 0
  },
  "activity_analysis": {
    "network_connections": {
      "total_connections": 0,
      "unique_devices": 0
    }
  },
  "cve_correlation": {
    "cve_ids_found": [],
    "affected_devices_by_cve": {},
    "total_unique_affected_devices": 0
  }
}

Example Workflows

Example 1: IP Address Investigation

User says: "Investigate IP 203.0.113.42 for the last 7 days"

Workflow:

Identify IoC: IPv4 Address, normalized: 203.0.113.42
3rd-Party Enrichment:
```
python3 tmp/ioc-investigation/enrich_ips.py 203.0.113.42
```
→ Get geo, ISP, VPN/proxy/Tor flags, AbuseIPDB score, Shodan ports/CVEs/tags
Phase 1 — Threat Intel (parallel via Azure Monitor MCP + az rest):
- Q1: ThreatIntelIndicators query (Azure Monitor MCP)
- MDE-IOC: Custom IOC list search (az rest)
- MDE-IP-ALERTS: IP alerts from MDE API (az rest)
Phase 2 — Activity Analysis (parallel via Azure Monitor MCP):
- Q2: DeviceNetworkEvents connection summary
- Q11: SigninLogs sign-in analysis
- Q8: AlertEvidence IoC in alerts
- Q10: SecurityAlert mentions
Phase 3 — CVE Correlation:
- Q12: Extract CVEs from alerts
- Extract CVEs from Shodan enrichment
- For each CVE: MDE-CVE-MACHINES (az rest)
Export JSON and summarize findings (include enrichment data in JSON export)

Example 2: Domain Investigation

User says: "Is evil-malware.com in our environment?"

Workflow:

Identify IoC: Domain, normalized: evil-malware.com
Phase 1 — Threat Intel (parallel):
- Q1: ThreatIntelIndicators query
- MDE-IOC: Custom IOC list search (az rest)
Phase 2 — Activity Analysis (parallel):
- Q4: DeviceNetworkEvents domain connections
- Q6: EmailUrlInfo email delivery
- Q8: AlertEvidence IoC in alerts
Phase 3 — Resolved IP Enrichment:
- Q14: Extract resolved IPs from domain connections
- Run enrich_ips.py on resolved IPs
Phase 4 — Exposure Assessment:
- List all devices that connected
- Identify affected users
Export JSON and summarize findings

Example 3: File Hash Investigation with CVE Correlation

User says: "Investigate SHA256 a1b2c3... and check which devices are vulnerable"

Workflow:

Identify IoC: SHA256 Hash, normalized: a1b2c3...
Phase 1 — Threat Intel (parallel):
- Q1: ThreatIntelIndicators query
- MDE-FILE-INFO: File info from MDE API (az rest)
- MDE-FILE-ALERTS: File alerts from MDE API (az rest)
- MDE-FILE-STATS: File statistics from MDE API (az rest)
Phase 2 — Device Exposure (parallel):
- MDE-FILE-MACHINES: Devices with file from MDE API (az rest)
- Q7: DeviceFileEvents query
- Q7b: DeviceProcessEvents query
Phase 3 — CVE Correlation:
- Q12: Extract CVEs from alerts
- For each CVE: MDE-CVE-MACHINES (az rest)
- Cross-reference with devices that have the file
Export JSON and summarize with remediation priorities

Security Notes

All investigations are logged for audit purposes
IoC values may be sensitive — handle with care
Follow organizational data classification policies
Consider threat actor attribution implications
Document investigation actions for incident timeline

Integration with Other Skills

This skill can be combined with:

user-investigation: When IoC is found in user's sign-in logs
computer-investigation: When IoC is found on specific device
kql-query-authoring: For custom KQL queries beyond the templates

Cross-skill pivot example: "Investigate IP 203.0.113.42" → Found in user sign-ins → "Investigate user@domain.com" using user-investigation skill

ioc-investigation

이 저장소의 다른 Skills

이 저장소의 다른 Skills

IoC (Indicator of Compromise) Investigation — Monitor MCP + Azure CLI

Purpose

📑 TABLE OF CONTENTS

⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Prerequisites

Skill Files

File Resolution (codeRefs-first)

Pre-requisite: Environment Configuration (config.json)

Output Modes

HTML Report — Conditional Resolution (codeRefs-first)

Multi-IoC HTML Reports

enrich_ips.py — Conditional Resolution (codeRefs-first)

Environment Configuration

Primary: Agent Settings Auto-Discovery (Recommended)

Secondary: config.json (Optional)

Configuration Resolution Order

Secrets Management (API Tokens)

Available Investigation Types

IP Address Investigation

Domain Investigation

URL Investigation

File Hash Investigation

Quick Start (TL;DR)

Phase 0: Investigation Cache Check (MANDATORY)

0.1 Cache File Convention

0.2 Cache Check Workflow

0.3 Cache Decision Summary

0.4 Important Rules

Execution Workflow

🚨 MANDATORY: Time Tracking Pattern

Phase 1: IoC Identification and Normalization (REQUIRED FIRST)

Phase 2: 3rd-Party IP Enrichment (IP Address IoCs)

Phase 3: Parallel Threat Intelligence Collection (KQL + MDE API)

Pre-requisite: Read MDE API Reference (MANDATORY)

Batch 1: KQL Queries via Azure Monitor MCP (Run ALL in parallel)

Batch 2: MDE API Calls via RunAzCliReadCommands (Run ALL in parallel)

Phase 4: Activity and Connection Analysis (KQL via Azure Monitor MCP)

Phase 5: CVE Correlation and Vulnerability Management

Phase 6: Export to JSON

KQL Execution Reference

How to Run KQL Queries

Table Availability

Sample KQL Queries

📅 Date Range Quick Reference

Q1. Threat Intelligence Indicator Match

Q2. IP Address — Network Connection Activity

Q3. IP Address — Detailed Connection Timeline (top 20)

Q4. Domain — DNS and HTTP Connection Activity

Q5. Domain — Detailed Connection Timeline (top 20)

Q6. URL — Email Delivery Analysis

Q7. File Hash — Device File Events

Q7b. File Hash — Process Events

Q7c. File Hash — Cross-Table Search (comprehensive)

Q8. Alert Evidence — IoC in Alerts (top 20)

Q9. Security Alerts — Full Alert Correlation

Q10. Security Alert — Sentinel Native Alerts

Q11. IP Address — Sign-in Analysis (Azure AD)

Q12. CVE Extraction from Alerts

Q13. KQL IP Context Fallback (when enrich_ips.py is unavailable)

Q14. Domain — Resolved IPs Extraction

MDE API via CLI

JSON Export Structure

Error Handling

Common Issues and Solutions

Required Field Defaults

Example Workflows

Example 1: IP Address Investigation

Example 2: Domain Investigation

Example 3: File Hash Investigation with CVE Correlation

Security Notes

Integration with Other Skills

IoC (Indicator of Compromise) Investigation — Monitor MCP + Azure CLI

Purpose

📑 TABLE OF CONTENTS

⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Prerequisites

Skill Files

Batch 2: MDE API Calls via `RunAzCliReadCommands` (Run ALL in parallel)

Batch 2: MDE API Calls via `RunAzCliReadCommands` (Run ALL in parallel)