sentinel-ingestion-report

name

sentinel-ingestion-report

description

Sentinel Ingestion Report — YAML-driven Python pipeline gathers all data via az monitor/az rest/Graph API, writes a deterministic scratchpad, LLM renders the report inline. Covers table-level volume breakdown, tier classification (Analytics/Basic/Data Lake), SecurityEvent/Syslog/CommonSecurityLog deep dives, ingestion anomaly detection (24h and WoW), analytic rule inventory via REST API, rule health via SentinelHealth, detection coverage cross-reference, tier migration candidates with DL-eligibility lookup, license benefit analysis (DfS P2 500MB/server/day, M365 E5 data grant). Output always inline; optional md/html export on request.

Sentinel Ingestion Analysis Report — Instructions

⚠️ Environment Constraints — Tool Availability

This skill operates in an environment where the following MCP servers are not integrated with Azure SRE Agent and therefore cannot be used for direct querying:

❌ Not Integrated	Scope	Replacement	Note
Sentinel MCP Server (mcp_microsoft_se2_*)	Data Lake Exploration, query_lake, search_tables, analyze_*	az monitor log-analytics query (KQL via Azure CLI)	Cannot currently be connected to Azure SRE Agent. Direct API access to Sentinel Data Lake not yet implemented.
MTP MCP Server (mcp_mtp_mcp_servi_*)	Advanced Hunting, Defender Triage	az monitor log-analytics query (KQL via Azure CLI)	Cannot currently be connected to Azure SRE Agent. Direct API access to Sentinel Data Lake not yet implemented.
Microsoft Graph MCP (mcp_microsoft_ent_*)	Graph API queries	az rest with Graph API endpoints	Cannot currently be connected to Azure SRE Agent. Direct API access to Microsoft Graph not yet implemented.

The following tools ARE available and can be used:

✅ Available	Scope	Use Case
Azure MCP Server (mcp_azure_mcp_ser_*)	Azure resource management, Kusto, monitoring	Infrastructure queries, resource health
KQL Search MCP (mcp_kql-search-mc_*)	KQL query examples, table schema lookup, ASIM parser info	Query validation, schema discovery, ASIM verification
Microsoft Learn MCP (mcp_microsoft_le*_*)	Documentation search, code samples	Reference lookups, best practice guidance
Direct Log Analytics	az monitor log-analytics query	All KQL query execution

All KQL queries, REST API calls, and Graph API calls are pre-defined in YAML files and executed by invoke_ingestion_scan.py via Azure CLI. No MCP server is needed for data gathering or report generation. The queries are permanent, pre-tested, and stored in queries.yaml.

---

Purpose

This skill generates a comprehensive Sentinel Ingestion Analysis Report covering workspace data volume, table-level breakdown, tier classification, ingestion anomalies, detection coverage, and optimization opportunities.

Entity Type: Sentinel workspace (from config.json)

Scope	Primary Tables	Use Case
Workspace-wide (default)	Usage, SentinelHealth, SentinelAudit	Full ingestion and cost analysis
Per-table deep dive	SecurityEvent, Syslog, CommonSecurityLog + any table	Granular breakdown of high-volume tables

What this report covers: Table-level volume breakdown with tier classification (Analytics/Basic/Data Lake), SecurityEvent/Syslog/CommonSecurityLog deep dives, ingestion anomaly detection (24h and week-over-week), analytic rule inventory with detection coverage cross-reference, rule health monitoring, tier migration candidates with DL-eligibility assessment, and license benefit analysis (DfS P2 and M365 E5).

---

Architecture

 ┌───────────────────────────────────────────────────────────────────┐
 │  queries.yaml        Python script               LLM render      │
 │  (23 queries)   ──→  invoke_ingestion_scan.py ──→  Phase 6       │
 │                      • az monitor (KQL)            (SKILL-        │
 │                      • az rest (REST + Graph)       report.md)   │
 │                      • az monitor table list                     │
 │                      ↓                                           │
 │            tmp/sentinel-ingestion-report/                        │
 │              scratchpad_<ws>_<ts>.md                             │
 │              (~50 KB, 64 sections)                               │
 └───────────────────────────────────────────────────────────────────┘

Execution model:

• Phases 1-5 (data gathering): Fully automated by invoke_ingestion_scan.py. KQL queries run via az monitor log-analytics query. Non-KQL data (analytic rules, tier classifications, custom detections) is gathered via REST API (az rest) and Azure CLI.
• Phase 6 (rendering): LLM reads the scratchpad + SKILL-report.md and renders the report inline. This is the only phase requiring LLM involvement.
• HTML export (optional): generate_html_report.py converts the scratchpad to a self-contained dark-theme HTML report.

File materialization: All output files (scratchpad, optional exports) are written to tmp/sentinel-ingestion-report/ under the workspace root, following the same pattern as mitre-coverage-report.

Design decision — TopRecommendations: The Top 3 Recommendations are computed by the LLM at render time (Phase 6), not pre-computed. Three of the seven Rule E categories (Data loss, DCR filter, Split ingestion) require cross-section reasoning that spans multiple scratchpad sections — this is precisely what the LLM excels at. The Python script provides all the raw data; the LLM applies Rule E scoring across it.

---

Companion Files — When to Load

This skill spans 4 files. Load only the file(s) needed for the current phase:

File	Purpose	When to Load
SKILL.md (this file)	Architecture, workflow, rendering rules, domain reference	Always — primary entry point
SKILL-report.md	Report templates (§1-§8), section-to-scratchpad mapping, formatting rules	Phase 6 rendering only
SKILL-drilldown.md	Post-report drill-down — rule cross-referencing (AR + CD via Graph API), ASIM parser verification, known pitfalls, error handling	After report is generated, when user asks follow-up questions (see §13 summary)
invoke_ingestion_scan.py	Python data-gathering pipeline (Phases 1-5)	Execution only — no need to read unless debugging
generate_html_report.py	Scratchpad → HTML report converter (dark theme)	Optional — when user requests HTML export

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/sentinel-ingestion-report/<filename>
   → If found: use/execute directly from this path (companion files are co-located here)
2. sentinel-ingestion-report/<filename>
   → If found in the repo folder: use/execute from this path
3. tmp/sentinel-ingestion-report/<filename>
   → If found: use from this path
4. None of the above:
   → read_skill_file("sentinel-ingestion-report", "<filename>") from Builder
   → CreateFile("tmp/sentinel-ingestion-report/<filename>", <content>)
   → Repeat for ALL companion files referenced by the script (queries.yaml, etc.)

Rules:

• When a file is found in codeRefs/ or the repo folder, execute it directly from there — do NOT copy it to tmp/.
• When materializing from Builder (step 4), materialize ALL companion files the script depends on, not just the script itself.
• This cascade applies to invoke_ingestion_scan.py, generate_html_report.py, and queries.yaml.

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:

1. 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.

2. 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": {}
   }
   

3. 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.

---

📑 TABLE OF CONTENTS

1. Quick Start - 3-step execution pattern
2. Critical Workflow Rules - Prerequisites and prohibitions
3. Execution Workflow - Phases 0-6
4. Query File Reference - All 23 YAML files
5. Output Modes - Inline chat vs. Markdown file
6. Deterministic Rendering Rules - Rules A-G (mandatory for Phase 6)
7. Domain Reference - SecurityEvent, Syslog, CommonSecurityLog interpretation
8. Tier Classification - Analytics vs Basic vs Data Lake background
9. Migration Classification - Zero-rule table categorization for §7a
10. Reference: Data Lake Migration - DL-eligible tables, decision matrix, trade-off analysis
11. Reference: License Benefits - DfS P2 / E5 pool calculations
12. Report Template - JIT pointer → SKILL-report.md
13. Post-Report Drill-Down Reference - Rule cross-referencing, Custom Detection API, ASIM verification, error handling
14. SVG Dashboard Generation - Visual dashboard from completed report

---

Quick Start (TL;DR)

3-step execution pattern:

Step 1:  Run invoke_ingestion_scan.py (Phases 1-5 — data gathering)
Step 2:  Read scratchpad + SKILL-report.md (Phase 6 prep)
Step 3:  Render report inline (§1-§8) — always inline, optional md/html export

Step 1: Run Data Gathering

Resolve invoke_ingestion_scan.py via the File Resolution cascade, then run:

# From the resolved path — run all phases (default: 30 days):
python <resolved_path>/invoke_ingestion_scan.py

# Specify a custom window (1, 7, 30, 60, or 90 days):
python <resolved_path>/invoke_ingestion_scan.py --days 7

# Run a specific phase (for re-runs / debugging):
python <resolved_path>/invoke_ingestion_scan.py --phase 3

# Force re-execution (ignore cached scratchpad):
python <resolved_path>/invoke_ingestion_scan.py --redo

Note: The script looks for config.json by walking up from its location. The agent auto-generates this file at the workspace root from its platform settings before first execution. See Config Auto-Generation.

Output: Scratchpad file at tmp/sentinel-ingestion-report/scratchpad_<workspace>_<timestamp>.md (~50 KB, 64 sections).

Timing: Full run takes ~20-25 seconds. Individual phases: 3-8 seconds each.

Cache Behavior

• If a scratchpad exists with age < 4 hours, ask the user whether to use cached results or re-run
• If the user says "redo", "rifai", "re-run", or passes --redo → always re-execute regardless of cache age
• If cache is ≥ 4 hours old or no scratchpad exists → always re-execute

Step 2: Load Rendering Context

1. Read the scratchpad file (path printed by the script at completion)
2. Read SKILL-report.md for rendering templates

Step 3: Render Report (Always Inline)

Render the complete report (§1-§8) inline in chat. This is the default and only initial output mode. Apply SKILL-report.md templates to scratchpad data, following Rules A-G.

Optional exports (only when user explicitly requests):

• Markdown file: python invoke_ingestion_scan.py --export md or user says "export as markdown"
• HTML file: Resolve generate_html_report.py via the File Resolution cascade, then run: python <resolved_path>/generate_html_report.py <scratchpad_path> or user says "export as html" / "generate html"
• HTML export generates a self-contained dark-theme HTML file in tmp/sentinel-ingestion-report/

⛔ Do NOT ask the user for output mode. Always render inline. Only generate md/html files when explicitly requested.

---

Config Auto-Generation

The agent auto-generates config.json at the workspace root before running any script. This replaces the need for a manually-maintained config file.

When to generate

• First execution in a session: If config.json does not exist at the workspace root, or exists but has an empty sentinel_workspace_id, generate it immediately.
• Subsequent executions: Skip — the file persists for the duration of the session.
• Never regenerate if the file already has a non-empty sentinel_workspace_id (it may have been customized by the user).

How to generate

Use CreateFile to write the following to the workspace root config.json:

{
  "tenant_name": "<from agent memory (overview.md) or ask user if unknown>",
  "sentinel_workspace_id": "<from <log_analytics_access> workspace GUID>",
  "subscription_id": "<from <azure_resource_access> subscription ID>",
  "azure_mcp": {
    "subscription_id": "<same as subscription_id above>",
    "resource_group": "<from <log_analytics_access> or agent memory — NOTE: this is the workspace RG, which may differ from the agent RG>",
    "workspace_name": "<from <log_analytics_access> workspace name>"
  },
  "api_tokens": {}
}

Field sources at runtime:

Field	Source
tenant_name	Agent memory (overview.md) or user prompt
sentinel_workspace_id	<log_analytics_access> → workspace GUID
subscription_id	<azure_resource_access> → subscription ID
azure_mcp.resource_group	<log_analytics_access> or agent memory — verify via Resource Graph if uncertain
azure_mcp.workspace_name	<log_analytics_access> → workspace name
api_tokens	Empty object — enrich_ips.py reads tokens from Key Vault / env vars independently

Path

Write to the workspace root (the top-level directory of the agent workspace). All Python scripts find it by walking up from their execution directory (up to 6–10 levels).

---

⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

Before starting ANY ingestion report:

1. Run invoke_ingestion_scan.py — this single script handles ALL data gathering (Phases 1-5). The LLM does NOT run queries, transcribe output, or write scratchpad sections
2. Ensure config.json exists at the workspace root — the agent auto-generates it from its <agent_settings> and <log_analytics_access> platform configuration. If the file already exists with a non-empty sentinel_workspace_id, skip regeneration. See Config Auto-Generation for the exact procedure.
3. Output is always inline — do NOT ask the user for output mode. Render the report inline in chat. Only generate md/html files when the user explicitly requests export
4. ALWAYS ask the user for timeframe if not specified: supported values are 1, 7, 30 (default), 60, or 90 days. The --days parameter controls the primary window; deep-dive and comparison windows are derived automatically

Date Window Model

The -Days parameter drives three time windows used across all queries:

Window	Token	Derivation	Purpose
Primary	{days}	= --days value	Usage overview (Q1-Q3), alert firing (Q12), license benefits (Q17/Q17b), tier summary (Q10b)
Deep-dive	{deepDiveDays}	≤7→Days, ≤30→7, ≤60→14, ≤90→30	Table breakdowns (Q4-Q8), rule health (Q11/Q11d), cross-ref (Q13), migration candidates (Q16), WoW "this period" (Q15)
Comparison	{wowTotalDays}	= deepDiveDays × 2	Period-over-period total lookback (Q15)

Example: -Days 60 → primary=60d, deep-dive=14d, comparison=28d

Dynamic period labels: Report column headers adapt automatically ("This Week"/"Last Week" for 7d deep-dive, "This Month"/"Last Month" for 30d, "This Period"/"Last Period" for 14d).

Exception: Q14 (24h anomaly detection) is unaffected by -Days — it uses fixed algorithmic constants (P30D lookback, 29-day weekday baseline). 5. ALWAYS use create_file for markdown reports (NEVER use terminal commands for file output) 6. ALWAYS sanitize PII from saved reports — use generic placeholders for real hostnames, workspace names, and tenant GUIDs in committed files 7. Read scratchpad + SKILL-report.md before rendering — the scratchpad is the sole data source for the report 8. Tier display convention — Azure CLI reports Data Lake tier tables as plan Auxiliary internally, but always refer to this tier as "Data Lake" in output — never use "Auxiliary"

Prerequisites

Dependency	Required By	Setup
Python 3.9+	invoke_ingestion_scan.py, generate_html_report.py	Standard library only — no external packages required
Azure CLI (az)	All KQL queries (az monitor log-analytics query), analytic rule inventory (az rest), tier classification (az monitor log-analytics workspace table list), custom detections (az rest + Graph API)	Install: aka.ms/installazurecli. Authenticate: az login --tenant <tenant_id> then az account set --subscription <subscription_id>
log-analytics extension	az monitor log-analytics query (all KQL queries in Phases 1-5)	Install: az extension add --name log-analytics. Verify: az extension list --query "[?name=='log-analytics']"
Azure RBAC	Azure CLI calls above	Log Analytics Reader on the workspace (KQL queries + table list). Microsoft Sentinel Reader on the workspace (analytic rule inventory via az rest)

🔴 PROHIBITED

• ❌ Using Sentinel MCP Server (mcp_microsoft_se2_*) or MTP MCP Server (mcp_mtp_mcp_servi_*) — not integrated with Azure SRE Agent (cannot currently be connected). Use az monitor log-analytics query for KQL queries instead
• ❌ Using Microsoft Graph MCP (mcp_microsoft_ent_*) — not integrated with Azure SRE Agent (cannot currently be connected). Use az rest for Graph/REST API queries instead
• ❌ Running KQL queries via any MCP tool during data gathering — the Python script handles all queries via az monitor log-analytics query
• ❌ Writing or modifying scratchpad sections manually — invoke_ingestion_scan.py is the sole writer
• ❌ Reporting cost in dollar amounts — always use GB savings (e.g., "~78.7 GB/month savings")
• ❌ Fabricating ingestion volumes, device names, or anomaly percentages
• ❌ Overriding DL eligibility classification from script output based on LLM knowledge
• ❌ Rendering the report without first reading the scratchpad file
• ❌ Asking the user for output mode — always render inline; only export when explicitly requested

✅ ALLOWED MCP tools for supplementary tasks (NOT for data gathering):

• KQL Search MCP (mcp_kql-search-mc_*): Query validation, table schema lookup, ASIM parser info
• Microsoft Learn MCP (mcp_microsoft_le*_*): Documentation search, best practice guidance
• Azure MCP Server (mcp_azure_mcp_ser_*): Azure resource management, infrastructure queries

---

Execution Workflow

Phase 0: Initialization

1. Auto-generate config.json at the workspace root if missing or empty (see Config Auto-Generation)
2. Confirm output mode and timeframe with user (pass --days to the script; default 30)
3. Verify prerequisites: az login session active, correct subscription set

Phases 1-5: Data Gathering (automated by Python)

Run invoke_ingestion_scan.py — it handles all 5 phases automatically:

Phase	Queries	Description	Execution Type
1	Q1, Q2, Q3	Core ingestion overview — Usage by DataType, daily trend, workspace summary	KQL (parallel)
2	Q4, Q5, Q6a, Q6b, Q6c, Q7, Q8	Table deep dives — SecurityEvent, Syslog, CommonSecurityLog breakdowns	KQL (parallel)
3	Q9, Q9b, Q10, Q10b	External data — analytic rule inventory (REST), custom detections (Graph via az rest), tier classification (CLI), tier summary (KQL)	REST + CLI + KQL (sequential, with depends_on)
4	Q11, Q11d, Q12, Q13	Detection coverage — rule health (SentinelHealth), alert firing (SecurityAlert), cross-reference (all tables with data vs. rule inventory)	KQL (parallel) + post-processing
5	Q14, Q15, Q16, Q17, Q17b	Anomaly detection + cost analysis — 24h anomaly, WoW comparison, migration candidates, license benefits, E5 per-table	KQL (parallel) + post-processing

Post-processing (automated by the script, Phases 4-5):

Task	Phase	Description
Table cross-reference	4	For each table with data (Q13), regex-search all enabled rule queries for that table name
ASIM parser detection	4	Search all rule queries for ASIM function patterns (_Im_, _ASim_, imDns, etc.)
Value-level rule verification	4	For each EventID/Facility/ProcessName/Activity/Vendor from deep dives, check if any rules reference it
Detection gap detection	4	Identify tables on DL/Basic tier that have enabled rules (🔴 critical finding)
Anomaly severity classification	5	Apply Rule A thresholds to Q14/Q15 results
DL eligibility classification	5	Classify all tables using hardcoded $dlYes/$dlNo reference arrays
Migration table assembly	5	Cross-reference volume × rule count × tier × DL eligibility → category assignment
License benefit computation	5	Compute DfS P2 pool, E5 grant breakdown

Scratchpad output: The script writes all results to tmp/sentinel-ingestion-report/scratchpad_<workspace>_<timestamp>.md (~50 KB, ~64 named sections including PHASE_, PRERENDERED, and META blocks). See SKILL-report.md for the Section-to-Scratchpad Mapping.

Phase 6: Render Output (LLM)

🔴 MANDATORY — Load scratchpad + report template before rendering:

1. Read the scratchpad file (path printed by the script). This single file contains ALL data from Phases 1-5.
2. Read SKILL-report.md for the complete rendering templates and formatting rules.

Pre-render validation:

1. Verify scratchpad has all 5 phase sections (PHASE_1 through PHASE_5)
2. Check that PHASE_5.DL_Script_Output is populated (proof of DL classification execution)
3. Cross-validate: Q11 TotalRulesInHealth against Q9 AR_Enabled — if >10% gap, note it

Render inline — Section-by-Section Checklist:

Render the report section by section per SKILL-report.md templates. Do NOT skip any section. If a section's data returned 0 results, render the section header with a "✅ No anomalies/items found" note.

Section	Data Source (scratchpad keys)	Required
§1	All phases	✅ Workspace at a Glance, Cost Waterfall, Detection Posture, Top 3
§2	PHASE_1.Tables, PHASE_3.TierSummary	✅ Table breakdown + tier summary
§3	PRERENDERED.SE_, PRERENDERED.Syslog, PRERENDERED.CSL_*	✅ Deep dives (skip sub-section only if table not in top 20)
§4	PHASE_5.Anomaly24h/AnomalyWoW, PHASE_1.DailyTrend	✅ Anomaly table + daily trend chart
§5	PHASE_3.RuleInventory, PHASE_4.*	✅ Rule inventory + cross-ref + health
§6	PHASE_5.LicenseBenefits/E5_Tables	✅ DfS P2 + E5 analysis
§7	PHASE_5.Migration, PHASE_4.CrossRef	✅ Migration candidates + recommendations
§8	All	✅ Appendix (query reference, methodology)

Compute Top 3 Recommendations using Rule E: scan all scratchpad sections, score each candidate, select the top 3 by score.

Post-render:

• The inline report is the primary output — no further action needed unless user requests export
• If user requests markdown or HTML export, generate accordingly

---

Query File Reference

All queries are defined in queries.yaml (single consolidated file). The script discovers, parses, and executes them automatically.

YAML Format

id: ingestion-q1                              # Unique identifier
name: Usage by DataType with Billing Breakdown # Human-readable name
description: Top 20 tables ranked by volume    # What it does
phase: 1                                       # Which phase (1-5)
type: kql                                      # kql | rest | cli | graph
timespan: P{days}D                             # Placeholder — the script substitutes at runtime
query: |                                       # KQL query (multiline block scalar)
  Usage
  | where TimeGenerated > ago({days}d)
  ...

Non-KQL types have additional fields:

Type	Additional Fields	Description
rest	url, method, jmespath	Sentinel REST API via az rest
cli	command	Azure CLI command (e.g., az monitor log-analytics workspace table list)
graph	uri, method	Microsoft Graph API via az rest

Complete Query Inventory

Phase	File	ID	Type	Description
1	Q1-UsageByDataType.yaml	ingestion-q1	kql	Top 20 tables by billable volume with solution mapping
1	Q2-DailyIngestionTrend.yaml	ingestion-q2	kql	Daily ingestion trend
1	Q3-WorkspaceSummary.yaml	ingestion-q3	kql	Executive summary: table count, billable totals, daily average
2	Q4-SecurityEventByComputer.yaml	ingestion-q4	kql	SecurityEvent by Computer (top 25)
2	Q5-SecurityEventByEventID.yaml	ingestion-q5	kql	SecurityEvent by EventID (top 20)
2	Q6a-SyslogByHost.yaml	ingestion-q6a	kql	Syslog by source host (top 25)
2	Q6b-SyslogByFacilitySeverity.yaml	ingestion-q6b	kql	Syslog by Facility × SeverityLevel (top 30)
2	Q6c-SyslogByProcess.yaml	ingestion-q6c	kql	Syslog top ProcessName by Facility (top 30)
2	Q7-CSLByVendor.yaml	ingestion-q7	kql	CommonSecurityLog by DeviceVendor/DeviceProduct (top 20)
2	Q8-CSLByActivity.yaml	ingestion-q8	kql	CommonSecurityLog by Activity/LogSeverity/DeviceAction (top 30)
3	Q9-AnalyticRuleInventory.yaml	ingestion-q9	rest	Analytic rules (Scheduled + NRT) via Sentinel REST API
3	Q9b-CustomDetectionRules.yaml	ingestion-q9b	graph	Custom Detection rules via Microsoft Graph SDK
3	Q10-TableTierClassification.yaml	ingestion-q10	cli	Table tier classification via Azure CLI
3	Q10b-TierSummary.yaml	ingestion-q10b	kql	Per-tier volume summary (depends_on: Q10)
4	Q11-RuleHealthSummary.yaml	ingestion-q11	kql	SentinelHealth — rule execution health summary
4	Q11d-FailingRuleDetail.yaml	ingestion-q11d	kql	SentinelHealth — top 20 failing rules detail
4	Q12-SecurityAlertFiring.yaml	ingestion-q12	kql	SecurityAlert — top 30 alert-producing rules
4	Q13-AllTablesWithData.yaml	ingestion-q13	kql	All tables with data in deep-dive window (for cross-reference)
5	Q14-IngestionAnomaly24h.yaml	ingestion-q14	kql	24h vs same-weekday avg anomaly detection (29d lookback, fallback to flat 7d, >50%, ≥0.01 GB)
5	Q15-WeekOverWeek.yaml	ingestion-q15	kql	Period-over-period volume comparison
5	Q16-MigrationCandidates.yaml	ingestion-q16	kql	Billable tables with deep-dive volume (for migration analysis)
5	Q17-LicenseBenefitAnalysis.yaml	ingestion-q17	kql	DfS P2 + E5 daily ingestion breakdown
5	Q17b-E5PerTableBreakdown.yaml	ingestion-q17b	kql	E5-eligible per-table volume

---

Output Modes

Default: Inline Chat (always)

The report is always rendered inline in chat. This is the primary and default output.

Optional: Markdown Export

When the user says "export as markdown", "save as md", or passes --export md, save the report to tmp/sentinel-ingestion-report/sentinel_ingestion_report_<YYYYMMDD_HHMMSS>.md.

Optional: HTML Export

When the user says "export as html", "generate html", or "html report":

python generate_html_report.py <scratchpad_path> --output-dir tmp/sentinel-ingestion-report/

Generates a self-contained, dark-theme HTML file with all tables, charts, and badges rendered.

⛔ Do NOT ask the user for output mode. Always render inline first. Only generate file exports when explicitly requested.

---

Deterministic Rendering Rules

These rules eliminate LLM interpretation variance. Apply them EXACTLY during report rendering (Phase 6). No discretion allowed — the thresholds and formulas below are the sole authority.

Rule A: Anomaly Severity Classification

⚙️ Pre-computed by the script → PRERENDERED.AnomalyTable. Thresholds below retained for §8 methodology reference and manual verification.

Assign severity to each anomaly row deterministically based on absolute deviation AND volume.

Condition (both must be true)	Severity	Emoji
abs(Deviation%) ≥ 200 AND max(Last24hGB, Avg7dGB) ≥ 0.05 GB	High	🟠
abs(Deviation%) ≥ 100 AND max(Last24hGB, Avg7dGB) ≥ 0.01 GB	Medium	🟡
abs(Deviation%) ≥ 50 AND max(Last24hGB, Avg7dGB) ≥ 0.01 GB	Low	⚪
Below thresholds OR both periods < 0.01 GB volume	Excluded	—

Volume floor: The 0.01 GB minimum is enforced by the KQL queries. Tables below this floor are noise and MUST NOT appear in the anomaly table regardless of deviation percentage.

Override 1 — Rule-count: ANY table with ≥5 enabled rules AND an absolute change ≥40% (in either 24h or WoW) is automatically 🟠 regardless of base thresholds — a significant drop on a table feeding multiple rules signals potential connector or TI feed health issues that affect detection coverage. The 24h override catches same-day connector outages; the WoW override catches gradual multi-day degradation.

Override 2 — Near-zero: ANY table with deviation ≤ −95% AND max(volume) ≥ 0.05 GB is automatically 🟠 regardless of rule count — a near-complete signal loss on a significant table is an operational emergency (e.g., connector failure, API key expiry) even if no rules reference it directly.

⛔ PROHIBITED: Assigning severity based on "judgment", "context", or "this table is important" UNLESS the high-rule-count override above applies. Outside that specific override, the emoji MUST match the threshold table above — no discretionary overrides.

Rule B: Risk Rating Definition

In the Top 3 Recommendations table (§1), the "Risk" column means:

Risk = the security or operational impact of NOT acting on this recommendation.

Risk Level	Definition	Examples
High	Active detection gap or data loss if not addressed	Rules silently failing on DL tier; connector dropping data; 0% detection coverage on critical table
Medium	Missed optimization with measurable cost/posture impact	Zero-rule high-volume table on Analytics tier; noisy EventID with no detection value
Low	Minor improvement, no immediate security or cost impact	Small-volume table tier change; informational tuning

⛔ PROHIBITED: Interpreting "Risk" as implementation difficulty, effort, or change management complexity. Those concerns belong in prose recommendations (§7b-d), NOT the Risk column.

Rule C: Weekday Average Computation

⚙️ Pre-computed by the script → PRERENDERED.DailyChart. Logic below retained for §8 methodology reference.

When computing per-weekday averages for the §4b daily trend chart:

1. Exclude the report-generation day: If the last day in PHASE_1.DailyTrend matches META.Generated date, always exclude it from weekday averages — the report was generated mid-day so this is a partial day regardless of its volume. This prevents the partial day from non-deterministically dragging down whichever weekday it falls on.
2. Exclude ingestion gaps: Any remaining day with total ingestion < 0.1 GB is also excluded. These are ingestion reporting gaps, not representative of normal patterns.
3. Formula: Weekday Avg = sum(GB for that weekday, excluding days per rules 1–2) / count(qualifying days for that weekday)
4. Round to 2 decimal places.

⛔ PROHIBITED: Including the report-generation partial day or days with < 0.1 GB in averages — they drag down specific weekdays non-deterministically.

Rule D: Cross-Validation Denominator

In §5b cross-validation (Q11 vs Q9), always use AR-only enabled count from Q9 as the denominator:

Gap% = (Q9_AR_Enabled - Q11_DistinctRules) / Q9_AR_Enabled × 100

Do NOT use Combined_Enabled (AR+CD) as the denominator. SentinelHealth only tracks AR executions (Scheduled + NRT), not Custom Detection executions. Comparing Q11 against combined AR+CD inflates the gap percentage.

Rule E: Top 3 Recommendation Ranking

Rank ALL candidate recommendations using this scoring formula. The top 3 by score become the Top 3 in §1. Computed by the LLM at render time by cross-referencing all scratchpad sections.

Category	SeverityWeight	ImpactValue	Scratchpad Source
🔴 Detection gap (rules on wrong tier)	10	Number of affected rules	PHASE_4.DetectionGaps — the script emits Detection gap (XDR) or Detection gap (non-XDR) in §7a Category column
🔴 Data loss / connector failure	10	Affected volume in GB/day	PHASE_5.Anomaly24h (large negative deviations)
🟠 DL-eligible migration (zero rules)	5	BillableGB from §2a (or deep-dive GB / deepDiveDays × Days if only in §7a)	PHASE_5.Migration (Strong DL-eligible rows)
🟠 DL + KQL Job promotion	4	BillableGB (primary window)	High-volume 🟣/🟢 table — can complement split ingestion or stand alone; present both options and note they are combinable
🟠 License benefit activation	4	Eligible unclaimed GB/day	PRERENDERED.BenefitSummary + PRERENDERED.E5Tables + PRERENDERED.DfSP2Detail (volume eligible but benefit not yet activated)
🟠 DCR filter / EventID pruning	4	Estimated saveable GB (deep dive % × table BillableGB)	PHASE_2.SE_EventID + PHASE_4.ValueRef_EventID
🟠 Health fix (failing rules)	4	Number of failing rules	PHASE_4.FailingRules
🟡 Volume spike / cost anomaly	3	Spike GB on zero-rule tables	PHASE_5.Anomaly24h (large positive deviations on zero-rule tables — cost spike with no detection value)
🟡 Duplicate ingestion	3	Duplicate GB	Cross-ref PRERENDERED.SyslogFacility × PRERENDERED.CSL_Vendor (same-appliance overlap emitting both Syslog and CEF/ASA = double billing)
🟡 Split ingestion	3	BillableGB × estimated non-detection fraction	PHASE_2 deep dives + PHASE_4.ValueRef_* (zero-rule values)
🟡 Tier review / unknown eligibility	2	BillableGB	PHASE_5.Migration (Unknown rows)

Score = SeverityWeight × ImpactValue

Sorting: severity-first, then score. 🔴 items always rank above 🟠 items, which always rank above 🟡 items, regardless of score. Within the same severity tier, rank by descending score. This ensures detection gaps and data loss signals are never buried below cost optimizations.

Tie-breaking within same severity: higher score wins. If scores are equal, higher SeverityWeight wins. If still tied, higher ImpactValue wins.

⛔ PROHIBITED: Selecting Top 3 recommendations based on narrative variety, "one from each category", or subjective importance. The formula determines ranking — the LLM renders, it does not curate.

License benefit activation: Surfaces when PRERENDERED.BenefitSummary or PRERENDERED.E5Tables show E5-eligible or DfS-P2-eligible volume that is not yet being claimed (benefit shows 0 or is absent while eligible tables are ingesting). ImpactValue = the eligible GB/day that could be offset.

Volume spike / cost anomaly: Surfaces when PHASE_5.Anomaly24h shows a large positive deviation (>50% above baseline) on a table with zero detection rules (per PHASE_4.CrossRef). A spiking table with no rules has cost impact but no detection value — a strong signal to investigate and potentially filter or move to DL.

Duplicate ingestion: Surfaces when the same network appliance sends data via both Syslog and CommonSecurityLog (CEF/ASA). Compare appliance names/IPs in PRERENDERED.SyslogFacility/PRERENDERED.SyslogHost against PRERENDERED.CSL_Vendor — overlapping sources indicate double billing for the same data. ImpactValue = the smaller of the two streams (the duplicate portion).

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Domain Reference

This section provides the domain knowledge needed during Phase 6 rendering. When writing deep dive sections (§3), anomaly analysis (§4), and recommendations (§7), consult these reference tables for interpretation guidance.

SecurityEvent — EventID Optimization

Which EventIDs generate the most volume and their detection vs. cost tradeoff:

EventID	Description	Optimization Potential
4663	Object access (file auditing)	🔴 High — often excessive. Consider DCR drop filter or scoping SACL
4624	Successful logon	🟡 Medium — valuable for hunting/forensics but rarely in analytic rules. Strong split ingestion candidate: send to Data Lake for retention, keep off Analytics tier
4688	Process creation	🟡 Medium — consider moving to MDE DeviceProcessEvents. If no rules reference it, split to Data Lake
4799	Security group membership enumeration	🟡 Medium — often noisy on domain controllers
4672	Special privileges assigned	🟡 Medium — high volume on DCs
4625	Failed logon	🟢 Low — usually valuable for security detection

🟣 Split ingestion tip: For any deep-dive table classified as 🟢 Keep Analytics (active detection rules), individual high-volume values with zero rule references (verified via Phase 4 value-level check) are strong candidates for sub-table split ingestion. Route those values to Data Lake via DCR transformation — they remain available for hunting while the detection-relevant values stay on Analytics tier. KQL jobs can also run against this split-routed DL data to surface aggregated insights back to Analytics if needed.

Syslog — Facility Reference

Optimization potential by Syslog facility:

Facility	Description	Optimization Potential
auth	Authentication events (login, su, getty)	🟢 Low — always security-relevant. Keep in Analytics tier
authpriv	Private authentication (PAM, sudo, sshd)	🟢 Low — critical for security detection. Always keep
kern	Kernel messages (hardware, driver, critical system)	🟡 Medium — security-relevant but can be noisy. Consider Error+ only for high-volume servers
cron	Scheduled task notifications	🔴 High — rarely security-relevant at Info/Notice. Keep Warning+ only
daemon	System daemon messages (systemd, sshd, named, httpd)	🔴 High — typically largest Syslog contributor (50-80% of volume). Contains both security-critical processes (sshd) and noisy infrastructure (systemd). Drill down with Q6c to identify filterable processes
syslog	Internal syslog daemon messages	🟡 Medium — mostly operational. Keep Warning+ in Analytics
user	User-space application messages	🟡 Medium — varies by application. Check ProcessName
mail	Mail subsystem (postfix, sendmail, dovecot)	🟡 Medium — relevant if mail is in scope; otherwise DL candidate
local0–local7	Custom application logs	🔴 High — most common cost optimization targets. Custom apps often log at Debug/Info verbosity
ftp	FTP daemon messages	🟢 Low volume; keep for auditing if FTP in use
lpr	Print subsystem	🔴 High — almost never security-relevant. Set to None in DCR
news	Network news (NNTP)	🔴 High — almost never security-relevant. Set to None in DCR
uucp	UUCP subsystem	🔴 High — almost never security-relevant. Set to None in DCR
mark	Internal timestamp marker	🔴 High — operational only. Set to None in DCR

Syslog — DCR Severity-per-Facility Recommendations

The Data Collection Rule allows setting a minimum severity level per facility — the single most impactful cost control for Syslog:

Facility	Recommended Minimum	Rationale
auth, authpriv	Debug (collect all)	Security-critical — never filter
kern	Notice	Kernel module loads (T1547.006) and promiscuous mode (T1040) are kern.notice. Volume impact is minimal
daemon	Warning or Error	Major volume reduction. Note: sshd auth events go to auth/authpriv, not daemon. Trade-off: loses systemd service stop events at Info (security service tampering) — acceptable if EDR covers this
cron	Warning	Trade-off: cron job execution events are cron.info (T1053.003 persistence). Acceptable if auditd or MDE covers cron file monitoring
syslog	Warning	Internal operational messages are low-value at Info
user	Warning	Unless specific apps produce security telemetry
mail	Warning	Info-level mail relay logs are very verbose
local0–local7	Assess per-app	No safe default — network appliances, security tools, and databases commonly use local facilities. Review Q6c (Process by Facility) before setting severity filters
lpr, news, uucp, mark	None	Disable collection entirely

Syslog — SeverityLevel Values

SeverityLevel (string)	Numeric	Meaning	Retention Priority
emerg	0	System unusable	🔴 Always keep
alert	1	Immediate action required	🔴 Always keep
crit	2	Critical condition	🔴 Always keep
err	3	Error condition	🟡 Keep for most facilities
warning	4	Warning condition	🟡 Keep for security-relevant facilities
notice	5	Normal but significant	🟡 Keep for auth/authpriv and kern
info	6	Informational	🟢 Filter for high-volume facilities
debug	7	Debug-level detail	🟢 Filter everywhere except auth/authpriv

Syslog — ProcessName Security Relevance

ProcessName	Typical Facility	Security Relevance	Optimization
systemd	daemon	🟡 Low-Medium — unit start/stop events	🔴 Often 30-50% of daemon volume. Filter Info/Notice at DCR
systemd-logind	daemon	🟡 Medium — session/seat tracking	Keep Warning+
sshd	auth, authpriv, daemon	🟢 High — SSH login detection (brute force, lateral movement)	🟢 Always keep
sudo	authpriv	🟢 High — privilege escalation tracking	🟢 Always keep
su	auth, authpriv	🟢 High — user switching	🟢 Always keep
CRON / crond	cron	🟡 Low-Medium — scheduled tasks	Keep Warning+ unless monitoring for T1053
named / bind	daemon	🟡 Medium — DNS. Relevant for DNS tunneling	Keep if DNS rules exist; otherwise Warning+
httpd / nginx	daemon	🟡 Medium — web server logs	Assess overlap with WAF/CSL data
postfix / sendmail	mail	🟡 Low-Medium — mail relay	Keep Warning+
dhclient / NetworkManager	daemon	🟡 Low — DHCP/network changes	Filter Info/Notice
kernel	kern	🟢 Medium-High — kernel events, module loads	Keep Warning+
auditd	daemon, user	🟢 High — Linux Audit Framework	🟢 Always keep
polkitd	authpriv	🟡 Medium — PolicyKit authorization	Keep Warning+
dbus-daemon	daemon	🟡 Low — IPC. Rarely security-relevant	Filter all or keep Error+
rsyslogd / syslog-ng	syslog	🟡 Low — internal syslog ops	Keep Warning+

🟣 Split ingestion tip: If daemon facility accounts for >50% of Syslog and Q6c reveals systemd + systemd-logind + dbus-daemon dominate, consider a DCR transformation routing those processes to Data Lake while keeping sshd, auditd, and other security-critical processes in Analytics. KQL jobs can complement this by querying the DL-routed portion on schedule.

Syslog — Log Forwarding Architecture Note

In environments using centralized rsyslog/syslog-ng forwarders:

• Computer = the log forwarder hostname (many servers collapse to 1-2 forwarders)
• HostName = the actual originating device (from syslog header)
• HostIP = the originating device's IP address

The Q6a query uses SourceHost = iff(isnotempty(HostName) and HostName != Computer, HostName, Computer) to prefer the original source. If Q6a shows only 1-2 hosts despite expecting 100+ servers, the environment uses forwarding.

CommonSecurityLog — Vendor Reference

DeviceVendor	DeviceProduct	Optimization Potential
Palo Alto Networks	PAN-OS	🔴 High — filter TRAFFIC activity, keep THREAT in Analytics
Check Point	Firewall / VPN-1 & FireWall-1	🔴 High — filter routine Accept actions
Fortinet	Fortigate	🔴 High — filter traffic subtype, keep utm and event
Cisco	ASA	🟡 Medium — filter by message ID ranges
Zscaler	NSSWeblog	🟡 Medium — web proxy logs can be high volume
F5	BIG-IP ASM / LTM	🟡 Medium — WAF logs can spike during attacks
Trend Micro	Deep Security	🟢 Low — typically moderate volume

Firewall traffic/session logs often account for 60-80% of CSL volume. These are primarily TRAFFIC or Accept events with low detection value. Consider DCR transformation, Data Lake tier, split ingestion, or DL + KQL job promotion (these last two can be combined).

CommonSecurityLog — LogSeverity Values

Value (string)	Value (int)	Meaning	Retention Priority
Very-High	9-10	Critical security event	🔴 Always keep in Analytics
High	7-8	Significant security event	🔴 Keep in Analytics
Medium	4-6	Notable event	🟡 Review — may be filterable
Low	0-3	Informational event	🟢 Candidate for DL or DCR filter
(empty/Unknown)	—	Unmapped severity	⚠️ Check vendor documentation

DeviceAction optimization: If >70% of events have DeviceAction = "Allow" or "Accept", the table is dominated by permitted traffic. Filter at DCR level or move to Data Lake, keeping only denied/blocked/threat events in Analytics.

Anomaly Interpretation (Q14/Q15)

24h anomalies (Q14): Flags tables where last-24h ingestion deviates >50% from the same-weekday daily average AND at least one period has ≥0.01 GB volume. Q14 uses a fixed 29-day lookback (algorithmic constant, not affected by -Days).

• Positive spikes: May indicate attacks, misconfigured connectors, or bulk imports
• Negative drops: May indicate connector failures, agent issues, or collection gaps

Period-over-period (Q15): Compares total volume per table between current and prior period (period length = deep-dive window).

• New tables (100% change) → appeared only this period (new connector?)
• Growing tables → expanding collection scope or increased activity
• Shrinking tables → connector removal, collection changes, or seasonal patterns
• Stable high-volume tables → included via ThisWeekMB > 100 filter for visibility

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Tier Classification

Background

The Sentinel Usage table does NOT contain a TablePlan or Tier column. There is no KQL-native way to determine whether a table is on Analytics, Basic, or Data Lake tier.

The script handles this automatically: Q10 (CLI type) runs az monitor log-analytics workspace table list to fetch table plans, then Q10b (KQL, depends_on: Q10) computes per-tier volume summaries using the CLI output. The results are written to PHASE_3.Tiers and PHASE_3.TierSummary in the scratchpad.

Tier Display Convention

Azure CLI reports Data Lake tier tables as plan Auxiliary internally. Always refer to this tier as "Data Lake" in all output — never use "Auxiliary". The _CL suffix denotes a custom log table, not a copy — describe these as "Custom Data Lake table" (not "Auxiliary copy").

Q10b Cross-Reference Query

The script automatically populates the DataLakeTables and BasicTables arrays from CLI output and executes the tier summary KQL query. This computes per-tier TotalGB, BillableGB, TableCount, and PercentOfTotal using the full Usage table (not limited to Q1 top-20). These values are the authoritative source for PHASE_3.TierSummary and §2b rendering.

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Migration Classification

Used when rendering §7a (Tier Migration Candidates). The script computes the Category column using these criteria; the LLM uses this reference for rendering interpretation and recommendation prose.

Category	Criteria	Action
🔵 KQL Job output	Table name ends with _KQL_CL	NEVER migrate — promoted data from Data Lake, essential for detection pipeline
🔵 Already on Data Lake	Q10 tier = Data Lake AND zero rules	Already migrated — no action needed
🟢 Keep Analytics	≥1 enabled analytic rule AND healthy executions	Active detection coverage justifies Analytics cost
🟣 Split ingestion candidate	1-2 enabled rules AND high-volume (≥5 GB/week) AND DL-eligible	Few rules need only a subset of events. Route detection-relevant subset to Analytics via DCR, rest to Data Lake
❗ Detection gap (non-XDR)	≥1 enabled rule AND table is on Data Lake tier AND table is NOT an XDR table	Critical: Analytic rules cannot execute against DL tables — rules silently failing. Custom Detections also do NOT work because non-XDR tables are not available in Advanced Hunting on Data Lake. Remediation: (1) move table back to Analytics, OR (2) remove/disable the analytic rules referencing the table (accept DL tier). ⛔ PROHIBITED: Recommending "convert ARs to Custom Detections" for non-XDR tables — CDs run against Advanced Hunting which only retains Defender XDR tables for 30 days. Non-XDR tables on Data Lake are invisible to Advanced Hunting.
❗ Detection gap (XDR)	≥1 enabled rule AND table is on Data Lake tier AND table IS an XDR table	Partial gap: Sentinel Analytic Rules (AR) cannot execute against DL tables — ARs silently failing. However, XDR-native tables (Device*, Email*, CloudAppEvents, UrlClickEvents) are ALWAYS available in Advanced Hunting for 30 days regardless of Sentinel tier. Custom Detection rules run against Advanced Hunting, so CD rules continue to work. Only ARs are broken. Remediation: (1) move table back to Analytics, (2) convert affected ARs to Custom Detections, OR (3) remove/disable the ARs. See Advanced Hunting data retention
🔴 Strong candidate (DL-eligible)	0 rules AND DL classification = Yes	Evaluate DCR filtering to reduce unnecessary volume, then migrate remainder to Data Lake

LLM overlay checks (not separate emojis — flag as callout notes in §7b/7c prose):

• Execution issues: If a 🟢 table's rules appear in PHASE_4.FailingRules with 0 executions or failures, add a ⚠️ note: "Rules targeting [table] have execution issues — see §5b. Fix rules before relying on this coverage."
• ASIM dependency: If a 🔴 zero-rule table appears in PHASE_4.ASIM as consumed by ASIM parsers, add a ⚠️ note: "[table] is consumed by ASIM parsers ([parser names]) — migrating to Data Lake breaks these detections. Verify ASIM dependency before migrating." | 🟠 Not DL-eligible / unknown | 0 rules AND DL classification = No or Unknown | Optimize via DCR filtering or add analytic rules. Check MS docs for current eligibility |

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Reference: Data Lake Migration

This section contains lookup tables and background guidance for DL migration classification. Consult when rendering §7a recommendations and explaining Data Lake trade-offs.

Known DL-Eligible Tables

The script uses these lists as hardcoded DL_YES/DL_NO reference arrays. Keep this reference in sync with the script.

Category	DL-Eligible Tables	Notes
Defender XDR	CloudAppEvents, DeviceEvents, DeviceFileCertificateInfo, DeviceFileEvents, DeviceImageLoadEvents, DeviceInfo, DeviceLogonEvents, DeviceNetworkEvents, DeviceNetworkInfo, DeviceProcessEvents, DeviceRegistryEvents, EmailAttachmentInfo, EmailEvents, EmailPostDeliveryEvents, EmailUrlInfo, UrlClickEvents	GA Feb 2025
Verified LA tables	AADManagedIdentitySignInLogs, AADNonInteractiveUserSignInLogs, AADProvisioningLogs, AADServicePrincipalSignInLogs, AADUserRiskEvents, AuditLogs, AWSCloudTrail, AzureDiagnostics, CommonSecurityLog, Event, GCPAuditLogs, LAQueryLogs, McasShadowItReporting, MicrosoftGraphActivityLogs, OfficeActivity, Perf, SecurityAlert, SecurityEvent, SecurityIncident, SentinelHealth, SigninLogs, StorageBlobLogs, Syslog, W3CIISLog, WindowsEvent, WindowsFirewall	⚠️ Only these LA tables are verified DL-eligible. Unlisted → Unknown
Custom tables	Any table ending in _CL (except _KQL_CL)	Custom log tables are workspace-managed → DL-eligible

Known DL-Ineligible Tables (as of Feb 2026)

Category	Ineligible Tables	Notes
XDR — not yet supported	DeviceTvmSoftwareInventory, DeviceTvmSoftwareVulnerabilities, AlertEvidence, AlertInfo, IdentityDirectoryEvents, IdentityLogonEvents, IdentityQueryEvents	MDI tables announced for future DL support
Entra ID ❌	MicrosoftServicePrincipalSignInLogs, MicrosoftNonInteractiveUserSignInLogs, MicrosoftManagedIdentitySignInLogs	Not yet DL-eligible
Threat Intelligence ❌	ThreatIntelIndicators, ThreatIntelligenceIndicator	Required on Analytics for TI matching rules. Never recommend migration
Log Analytics ❌	AppDependencies, AppMetrics, AppPerformanceCounters, AppTraces, AzureActivity, AzureMetrics, ConfigurationChange, Heartbeat, SecurityRecommendation	Not yet DL-eligible

Fallback rule: If a table is not in either list, the script classifies it as Unknown. Render as ❓ Unknown with note: "Verify at Manage data tiers before migrating."

Decision Matrix

Enabled Rules	Executions (Health)	Alerts (Q12)	DL-Eligible?	Volume	Recommendation
0	N/A	0	✅ Yes	> 1 GB/week	🔴 Evaluate DCR filtering to reduce volume, then migrate remainder to Data Lake (confirm no ASIM dependency)
0	N/A	0	✅ Yes	< 1 GB/week	🔴 Migrate to Data Lake — minimal savings but cleaner tier alignment. DCR filtering optional at this volume
0	N/A	0	❌ No / ❓ Unknown	Any	🟠 Not eligible or unknown — review ingestion necessity, apply DCR filtering
0	N/A (on DL)	0	N/A — already DL	Any	🔵 Already on Data Lake — no action needed
0 (ASIM-dependent)	N/A	0	Any	Any	� Migrate — but LLM adds ⚠️ ASIM dependency callout in §7b
≥1	0 or failures	0	Any	Any	🟢 Keep — but LLM adds ⚠️ execution issues callout in §7b
≥1	0 (on DL)	Any	N/A — on DL	Any	🔴 Detection gap — ARs cannot execute against DL. The script emits Detection gap (XDR) or Detection gap (non-XDR). If XDR table: CDs still work via Advanced Hunting; recommend converting ARs→CDs or moving back to Analytics. If non-XDR table: move back to Analytics OR remove/disable rules. ⛔ NEVER recommend CD conversion for non-XDR tables
1-2	> 0, healthy	Any	✅ Yes	≥ 5 GB/week	🟣 Split ingestion candidate
≥1	> 0, healthy	0	Any	Any	🟢 Keep Analytics — rules executing, no matches (normal for TI rules)
≥1	> 0, healthy	> 0	Any	Any	🟢 Keep Analytics — active detections generating alerts

Data Lake Trade-Off

Capability	Analytics Tier	Data Lake Tier
Analytics rules, alerting, hunting	✅ Full support	❌ Not available (but see XDR exception below)
Custom Detection rules (Advanced Hunting)	✅ Full support	⚠️ XDR tables only: Still available — AH retains 30 days regardless of Sentinel tier. Non-XDR tables: ❌
Workbooks, playbooks, parsers, watchlists	✅ Full support	❌ Not available
KQL query performance	✅ High-performance	⚠️ Slower
Query cost	✅ Included in ingestion price	❌ Billed per query (data scanned)
KQL Jobs / Summary Rules / Search Jobs	✅	✅
Ingestion cost	Standard	Minimal
Default retention	90 days (Sentinel) / 30 days (XDR)	Matches analytics, extendable to 12 years

Primary vs secondary security data: Primary security data (EDR alerts, auth logs, audit trails) belongs on Analytics. Secondary data (NetFlow, storage access logs, firewall traffic, IoT logs) is ideal for Data Lake.

Filter before you migrate: For high-volume zero-rule tables, DL migration and DCR filtering are complementary — not mutually exclusive. Evaluate whether all ingested data serves a hunting, forensic, or compliance purpose. If a portion is noise (e.g., verbose diagnostics, routine health checks, debug-level telemetry), apply DCR transformations to drop or reduce that portion first, then migrate the meaningful remainder to Data Lake. This avoids simply shifting cost from Analytics to Data Lake query charges on data nobody uses.

Even when a table has zero rules, consider whether it serves hunting/forensic purposes. Tables like SigninLogs or AuditLogs should generally remain on Analytics regardless.

Data Lake Promotion via KQL Jobs

For high-volume tables on Data Lake — whether fully migrated or partially routed via split ingestion — that still need detection coverage:

1. Ingest raw logs into Data Lake tier (cheap)
2. Create KQL jobs to query Data Lake on schedule, writing aggregated results to Analytics-tier _KQL_CL tables
3. Point analytics rules at the _KQL_CL output table

KQL Job key facts: Full KQL (joins, unions, CTEs). Schedules: by-minute through monthly. Lookback up to 12 years. Limits: 3 concurrent / 100 enabled per tenant, 1hr query timeout. Data Lake has ~15-min ingestion latency — jobs should use now(-15m) as upper bound. TimeGenerated is overwritten if >2 days old — preserve source timestamps in a custom column.

Split Ingestion and/or DL + KQL Job Promotion

The script auto-classifies 🟣 Split candidates (1-2 rules, ≥5 GB/week, DL-eligible). For these tables (and high-volume 🟢 Keep tables), the report should present both optimization paths so the operator can choose — or combine them — based on their knowledge of the rule queries:

	Split Ingestion (DCR)	DL + KQL Job
How it works	DCR routes a detection-relevant subset to Analytics, bulk to DL	Any data on DL (full table or split-routed portion); KQL job promotes aggregated results to _KQL_CL on Analytics
Detection latency	Real-time (subset stays on Analytics)	15+ min (DL ingestion lag + job schedule)
Rule rewrite needed	No — rules keep targeting original table	Yes — rules must target _KQL_CL output
Volume savings	Moderate (bulk to DL, subset stays)	Depends on scope — maximum if entire table goes to DL, incremental if applied to split-routed portion
Best when	Rules filter on specific raw events (EventIDs, facilities)	Rules use aggregation and tolerate latency

These approaches are complementary, not mutually exclusive. Split ingestion routes bulk data to DL while keeping detection-relevant events on Analytics. KQL jobs can then run against that DL portion to surface additional insights (e.g., aggregated anomalies) back to Analytics via _KQL_CL tables — giving you both real-time detection on the split subset AND scheduled analytics on the DL bulk.

Rendering guidance: The LLM does NOT have visibility into rule query text (aggregation vs raw filters), so it cannot definitively recommend one over the other. For 🟣 tables and high-volume 🟢 tables, present the comparison and note which approach fits which rule pattern. Do NOT change the script's Category emoji in §7a — express as prose in §7b/7c.

References:

• KQL jobs
• Sentinel data lake overview
• Manage data tiers and retention
• Log retention tiers

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Reference: License Benefits

Defender for Servers P2 — 500MB/Server/Day Benefit

• Each server protected by DfS P2 contributes 500 MB/day to a pooled daily allowance
• Pool = (number of protected servers) × 500 MB — aggregate across subscription, not per-machine
• Applies to security data types: SecurityAlert, SecurityBaseline, SecurityBaselineSummary, SecurityDetection, SecurityEvent, WindowsFirewall, MaliciousIPCommunication, SysmonEvent, ProtectionStatus, Update, UpdateSummary
• Applied automatically at workspace level — shows as zero cost

| Pool calculation from Q4:

Potential DfS P2 Pool = (Distinct servers from Q4) × 500 MB/day

Example: Q4 shows 12 servers → pool = 6 GB/day. If DFSP2-eligible avg is 4.2 GB/day → fully covered.

Scenario	Condition	Recommendation
Pool far exceeds usage	DfSP2_DailyGB < 50% of PoolGB	Highlight the unused headroom and recommend increasing SecurityEvent logging levels (e.g., "All Events" instead of "Common") to broaden detection coverage at no additional ingestion cost. Note that increased data volume may affect retention storage costs
Pool covers usage	DfSP2_DailyGB ≥ 50% and ≤ 100% of PoolGB	Pool covers current need — monitor growth and reference §3a if approaching ceiling
Usage exceeds pool	DfSP2_DailyGB > PoolGB	Overage is billed at standard rates — review §3a EventID breakdown for reduction opportunities, or consider onboarding more servers to DfS P2 to expand the pool

M365 E5 / Defender XDR Ingestion Benefit

• M365 E5 (or E5 Security, A5, F5, G5) provides 5 MB per user per day pooled data grant (offer page)
• Grant = (number of E5 licenses) × 5 MB/day
• Covers: Entra ID sign-in/audit logs, MCAS shadow IT, Purview info protection, M365 advanced hunting data (29 tables in Q17/Q17b)
• Applied automatically — Free Benefit - M365 Defender Data Ingestion
• Always-free (all Sentinel users): Azure Activity, Office 365 Audit Logs, Defender alerts

⚠️ Ask user for E5 license count — not discoverable from Sentinel telemetry.

Example: 500 E5 licenses → grant = 2.5 GB/day. If E5-eligible avg exceeds grant, overage billed at standard rates.

References:

• DfS P2 data ingestion benefit
• Sentinel free data sources
• View data allocation benefits
• M365 E5 offer details

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Report Template

📄 Just-in-time loading: Read SKILL-report.md at the start of Phase 6 rendering. It contains:

• Inline Chat Executive Summary template — Workspace at a Glance, Cost Waterfall, Detection Posture, Overall Assessment, Top 3 Recommendations
• Markdown File Structure — Complete §1–§8 rendering rules, mandatory format requirements, column specifications, validation checks
• Section-to-Scratchpad Mapping — Which scratchpad keys feed each report section

Load ONLY when entering Phase 6 — NOT during Phases 1–5. Combine with scratchpad data for rendering.

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Post-Report Drill-Down Reference

📄 Just-in-time loading: Read SKILL-drilldown.md for full instructions when any of these are needed.

Available Drill-Down Patterns

Use these when the user asks follow-up questions after a report is generated (e.g., "which rules use EventID 8002?", "look up custom detection rules", "do any ASIM parsers depend on this table?").

Pattern	Purpose	Tool / Method	Trigger Phrases
1. EventID cross-ref	Which analytic rules reference a specific EventID?	az rest (Sentinel REST API) + JMESPath contains()	"which rules use EventID X", "does any rule need this EventID"
2. Syslog facility/process	Which rules reference a Syslog facility, source, or process?	az rest + JMESPath	"which rules use sshd", "any rules for authpriv"
3. CSL vendor/activity	Which rules reference a CEF vendor, product, or activity?	az rest + JMESPath	"rules for Palo Alto TRAFFIC", "which rules use CommonSecurityLog"
4. Full rule query dump	Export all enabled rule queries for manual analysis	az rest → JSON file	"export all rule queries", "build EventID dependency map"
5. ASIM parser verification	Which ASIM parsers consume a table slated for migration?	az rest + regex match for _Im_/_ASim_ patterns	"ASIM dependency", "do parsers use this table"
6. Custom Detection rules	Inventory CD rules via Graph API (query text, schedule, last run)	az rest with Graph API endpoint	"custom detection rules", "CD rules", "lookup custom detections"

⚠️ Graph MCP not integrated: The Microsoft Graph MCP server is not integrated with Azure SRE Agent (cannot currently be connected; direct API access to Microsoft Graph as a replacement not yet implemented). For Custom Detection queries, always use az rest with the Graph API endpoint. For Analytic Rule queries, use az rest. See SKILL-drilldown.md for the exact endpoint and select fields.

Also in SKILL-drilldown.md

Section	Contents
Known Pitfalls	Usage table batching, _SPLT_CL naming, case-sensitive custom tables, LogSeverity types, value-level vs table-level coverage confusion
Error Handling	Common errors from az rest, Graph API via az rest; graceful degradation for missing tables; re-running individual phases
CloudAppEvents Appendix	Custom Detection management audit trail (EditCustomDetection events) — distinct from execution telemetry
Additional References	Microsoft Learn links for cost optimization, DCR configuration, data tiers, ASIM parsers

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SVG Dashboard Generation

📊 Optional post-report step. After a report is generated, the user can request a visual SVG dashboard.

Trigger phrases: "generate SVG dashboard", "create a visual dashboard", "visualize this report", "SVG from the report"

How to Request a Dashboard

• Same chat: "Generate an SVG dashboard from the report" — data is already in context.
• New chat: Attach or reference the report file, e.g. #file:reports/sentinel/sentinel_ingestion_report_<workspace>_<date>.md
• Customization: Edit svg-widgets.yaml before requesting — the renderer reads it at generation time.

Execution

Step 1:  Read svg-widgets.yaml (this skill's widget manifest)
Step 2:  Read the svg-dashboard SKILL.md (rendering rules — Manifest Mode) if available in the workspace
Step 3:  Read the completed report file (data source)
Step 4:  Render SVG → save to reports/sentinel/{report_name}_dashboard.svg

The YAML manifest is the single source of truth for layout, widgets, field mappings, colors, and data source documentation. All customization happens there.