siem-detection

name	siem-detection
description	Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows. Use when the user mentions 'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development,' or needs help building or improving security detections.
allowed-tools	Read, Write, Bash, Grep, Glob, WebSearch

SIEM Detection — Detection Engineering

Build, audit, and maintain SIEM detection content — the rules that fire alerts. Distinct from incident-triage (responds when alerts fire) and from soc-operations (runs the SOC that triages alerts). This skill is the engineering layer: log coverage, rule authoring, tuning, and detection-as-code workflows.

Cross-references: incident-triage for what happens after the alert, threat-hunting for proactive hypothesis-driven hunts that often graduate into detection rules, breach-patterns for detection ideas pulled from public breach disclosures, soc-operations for the alert-triage operations on top of the detections engineered here.

Scope

This skill covers:

• Log source coverage assessment ("are we even collecting the events we'd need to detect X?")
• Rule authoring across major SIEM query languages (Sigma, KQL, SPL, Elastic ES|QL, Chronicle YARA-L)
• MITRE ATT&CK mapping — every rule tagged with technique IDs for coverage analysis
• Detection-as-code workflows (rules in Git, CI tests, deployment automation)
• Alert tuning workflow — reducing false positives without losing true positives
• Coverage gap analysis using ATT&CK Navigator

This skill does NOT cover:

• Live alert triage (that's incident-triage)
• Building a SOC team or alert escalation criteria (soc-operations)
• Active threat hunting (threat-hunting)

Methodology

Step 1: Map log sources to ATT&CK coverage

Before writing any rule, audit what you can detect.

Categorize log sources by what they observe:

Category	Sources	Observes
Endpoint	EDR (CrowdStrike, SentinelOne, Defender), Sysmon, osquery	Process exec, file write, network, registry, parent-child
Network	Zeek/Bro, Suricata, NSM, firewall, DNS query logs	Connections, protocols, DNS queries, TLS metadata
Identity	Okta, Entra ID, AD, Auth0, GCP/AWS sign-in	Authentications, MFA, group changes, role assignments
Cloud	CloudTrail (AWS), Audit Logs (GCP), Activity Log (Azure)	API calls — what was created/changed/deleted
Application	App logs, WAF logs, load balancer logs, gateway logs	Request URLs, status codes, auth outcomes
SaaS	Google Workspace, M365, Salesforce, GitHub audit	Admin actions, sharing, sensitive doc access

Run a gap check:

• Pull the MITRE ATT&CK Enterprise matrix
• For each technique relevant to your environment, ask: which of my log sources would surface this?
• Techniques with NO source mapped are blind spots — write them down before writing any rules

Common blind spots:

• Endpoint logs but no command-line argument capture (most Windows event logs default to logging only the binary, not the args)
• Cloud audit logs collected but ReadOnly: true events filtered out — pre-attack recon invisible
• No SaaS audit logs — every modern attack involves a SaaS pivot at some point
• App logs without correlation IDs — can't connect "WAF saw payload" to "app processed payload"

Step 2: Pick the right detection model per case

Not every threat needs a SIEM rule. Match the detection model to what you're detecting.

Threat character	Best model	Example
Known IOC (hash, IP, domain)	Threat-intel lookup	Sysmon hash matches known malware
Known pattern (specific command, specific path)	Signature rule	powershell.exe -enc <base64>
Known anomaly (behavior outside baseline)	Statistical detection	Service account suddenly authenticating from new geography
Sequence of events	Correlation rule	Failed logon → success → privilege change in 5 min
Novel / never-seen-before	Threat hunting (see threat-hunting)	Hypothesis-driven SIEM search
Insider abuse	UEBA / risk scoring	Cumulative risky behaviors weighted over time

Signature rules are cheapest to write and easiest to tune; statistical detections need baseline data and produce more false positives in the first month.

Step 3: Write the rule

Use Sigma as the source of truth where possible

Sigma is the cross-SIEM detection format. Write the rule in Sigma; auto-convert to your backend via sigmac / sigma-cli / pySigma. Even if you only target Splunk today, future-you will thank you.

title: AWS IAM CreateUser Followed by AttachUserPolicy
id: <UUID>
status: experimental
description: Detects an identity creating a new IAM user and immediately attaching an admin policy
references:
  - https://attack.mitre.org/techniques/T1136/003/
author: <name>
date: 2026-05-26
tags:
  - attack.persistence
  - attack.t1136.003
logsource:
  product: aws
  service: cloudtrail
detection:
  create_user:
    eventName: CreateUser
  attach_policy:
    eventName: AttachUserPolicy
    requestParameters.policyArn|contains: 'Administrator'
  timeframe: 10m
  condition: create_user  

           
 

KQL (Microsoft Sentinel / Defender / Azure Monitor)

SigninLogs
| where TimeGenerated > ago(1h)
| where ResultType != 0
| summarize FailureCount = count() by UserPrincipalName, IPAddress, bin(TimeGenerated, 5m)
| where FailureCount > 10
| join kind=inner (
    SigninLogs
    | where TimeGenerated > ago(1h)
    | where ResultType == 0
) on UserPrincipalName, IPAddress
| project TimeGenerated, UserPrincipalName, IPAddress, FailureCount

(Failed logons spike on one user/IP, then a success on the same user/IP — classic password spray success.)

SPL (Splunk)

index=aws sourcetype=aws:cloudtrail
  (eventName=CreateUser OR eventName=AttachUserPolicy)
| transaction userIdentity.arn maxspan=10m
| where like(eventName, "%CreateUser%") AND like(eventName, "%AttachUserPolicy%")
| table _time, userIdentity.arn, eventName, requestParameters

ES|QL (Elastic)

FROM logs-aws.cloudtrail-*
| WHERE event.action == "CreateUser" OR event.action == "AttachUserPolicy"
| STATS create_count = COUNT(*) BY user.arn, event.action
| WHERE create_count > 0

(Use the LookML / KQL / SPL / ES|QL that matches your SIEM, but author the canonical version in Sigma.)

Step 4: Map to MITRE ATT&CK

Every rule should tag at least one ATT&CK technique. Coverage maps roll up to ATT&CK Navigator (navigator.mitre-attack.org):

• Export your rules with their ATT&CK tags
• Render onto the Navigator matrix
• Identify coverage gaps by tactic — "we have nothing for Initial Access via Phishing" is more actionable than "we need more rules"

The Navigator JSON format is open; building this report from your rules-as-code repo is a few hundred lines of Python and pays for itself the first time someone asks "what do we detect?"

Step 5: Tune

The false-positive lifecycle:

1. Deploy the rule with level: experimental for 1-2 weeks
2. Review every fire — true positive, false positive, suppressible?
3. For each FP, ask: can I narrow the rule (more specific filter) or add a tuning exception (allow-list specific known-good)?
4. Track the ratio — if FPs are > 80% after tuning, the detection model is wrong (signature might need to be statistical, or vice versa). Don't paper over a bad model with 100 allow-list entries.
5. Promote to level: high / production only after FP rate is acceptable

Rules that have never fired are also a signal — either the log coverage is broken, the query is wrong, or the threat truly hasn't occurred. Verify which by running a deliberate-test event through the system.

Step 6: Detection-as-code

Rules live in Git, not in the SIEM console.

detections/
├── aws/
│   ├── credential-access/
│   │   └── iam-create-user-attach-admin.yml
│   └── ...
├── windows/
├── linux/
└── identity/
    └── okta-password-spray.yml
.github/workflows/
└── detection-ci.yml

CI checks:

• Sigma validates (sigma-cli check)
• ATT&CK tag present and resolvable
• Description and references fields non-empty
• Backend translation succeeds (sigma convert -t splunk etc.)
• Optional: replay the rule against a known-good event store and assert hit count

Deployment: post-merge, push rules to the SIEM via API. Roll back via Git revert.

Output Format

Coverage assessment:

# SIEM Detection Coverage
## Environment: [name]
## Date: [date]

### Log sources mapped
| Source | Status | Notes |
|---|---|---|

### ATT&CK coverage
| Tactic | Techniques covered / total | Blind spots |
|---|---|---|

### Rule inventory
| Rule | ATT&CK | Severity | Status | Last fired |
|------|--------|----------|--------|------------|

### Tuning queue
[Rules in experimental / needing FP triage]

### Recommended next 30 days
[Prioritized — usually 3-5 items]

Per-rule documentation lives with the rule (Sigma YAML), not in a separate runbook. The description, references, and falsepositives fields are the runbook.

Boundaries

• Detection content for your own environment, or environments where the user has explicit authorization
• Refuse to write evasion rules or detections designed to flag legitimate security tools
• Detections that intentionally surveil employees beyond what HR/legal have approved are out of scope — escalate to the user
• Provide enough context with each rule that the analyst who triages the alert understands what to do; rules without that context produce alert fatigue

References

• MITRE ATT&CK Enterprise matrix
• MITRE ATT&CK Navigator
• Sigma rules repo (SigmaHQ/sigma)
• Florian Roth's "Detection Engineering" writings
• Splunk Security Essentials / Microsoft Sentinel content hub / Elastic detection rules repo
• "Detection Engineering Maturity Matrix" (Florian Roth)
• "The Pyramid of Pain" (David Bianco) — IOC value hierarchy
• NIST SP 800-92 (Computer Security Log Management)