ai-risk-management
Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance,…
Conduct proactive, hypothesis-driven threat hunts — search SIEM / EDR / logs for adversaries who haven't tripped an alert yet. ATT&CK-driven, hypothesis-based methodology. Use when the user mentions 'threat hunting,' 'proactive hunt,' 'TaHiTI,' 'PEAK framework,' 'MITRE ATT&CK
$ npx -y skills add briiirussell/cybersecurity-skills --skill threat-hunting --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/threat-huntingContext preview
The summary Claude sees to decide when to auto-load this skill.
Conduct proactive, hypothesis-driven threat hunts — search SIEM / EDR / logs for adversaries who haven't tripped an alert yet. ATT&CK-driven, hypothesis-based methodology. Use when the user mentions 'threat hunting,' 'proactive hunt,' 'TaHiTI,' 'PEAK framework,' 'MITRE ATT&CK
name: threat-hunting description: "Conduct proactive, hypothesis-driven threat hunts — search SIEM / EDR / logs for adversaries who haven't tripped an alert yet. ATT&CK-driven, hypothesis-based methodology. Use when the user mentions 'threat hunting,' 'proactive hunt,' 'TaHiTI,' 'PEAK framework,' 'MITRE ATT&CK hunt,' 'hypothesis-driven hunt,' 'hunt hypothesis,' 'living off the land,' 'LOLBins,' 'beaconing,' 'lateral movement detection,' 'data staging,' 'persistence hunting,' or wants to find threats that have evaded existing detections." allowed-tools: Read, Write, Bash, Grep, Glob, WebSearch
Hunt for adversaries who are already inside but haven't tripped an alert. Distinct from `incident-triage` (reactive, alert is firing) and from `siem-detection` (engineer rules so future alerts fire). This skill is the *proactive* layer — assume something has slipped through, look for it.
Hunting is hypothesis-driven, not browse-driven. "Let's look around the SIEM" is not hunting; "let's check for the specific pattern of T1059.001 (PowerShell) being launched by Office processes" is.
Cross-references: `siem-detection` (queries you write here often graduate to detection rules), `incident-triage` (what to do if a hunt confirms a finding), `breach-patterns` (a rich source of hunt hypotheses), `disk-forensics` (deeper analysis on confirmed hits).
The PEAK (Prepare, Execute, Act, Knowledge) framework from Splunk SURGe — the most actionable hunting methodology I've seen.
Form the hypothesis. Strong hypotheses share three properties:
1. **Specific** — names a technique, log source, and expected artifact 2. **Testable** — describes what evidence would confirm or deny 3. **Bounded** — has a defined time window and scope
**Bad hypothesis:** "Look for anomalies in the SIEM" **Good hypothesis:** "Within the last 30 days, no service account should have run interactive PowerShell with `-encodedCommand` flag (T1059.001 + T1027). Search Sysmon event 1 for parent process = service-account-launched scheduled task, child = `powershell.exe`, command line contains `-enc` or `-encodedcommand`."
Hunt hypothesis sources, ranked by yield:
| Source | Yield | Effort | |---|---|---| | Recent incident (yours or peer's) | High | Low — pattern is concrete | | `breach-patterns` skill catalog | High | Low — generalizes from public breaches | | MITRE ATT&CK technique you don't have a detection for | Medium | Medium — read the technique, design the hunt | | Threat intel report (CrowdStrike, Mandiant, vendor reports) | Medium | Medium — current patterns | | Anomaly: "this number went up — why" | Low | Low — often FP, occasionally gold |
Run the hunt. Three execution patterns:
**Pattern A — Pivot from indicator.** Start with a specific IOC (IP, hash, domain) and look for any host or user that touched it.
// Sentinel — pivot from a suspicious IP across all log sources
union *
| where TimeGenerated > ago(90d)
| where contains("198.51.100.42")
| project TimeGenerated, Type, Computer, _ResourceId**Pattern B — Pivot from technique.** Start with an ATT&CK technique and look for any host doing that.
// Splunk — T1547.001 Registry Run Keys persistence index=sysmon EventCode=13 TargetObject="*\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\\*" | stats values(Details) by Computer, User | where len(values(Details)) > 1
**Pattern C — Anomaly hunt.** Establish a baseline; look for outliers.
// Sentinel — service accounts authenticating from new geographies SigninLogs | where TimeGenerated > ago(30d) | where UserType == "Service" | summarize Countries = make_set(Location) by UserPrincipalName | where array_length(Countries) > 1
For every hit, three possible outcomes:
| Outcome | Action | |---|---| | Confirmed malicious | Escalate to `incident-triage` immediately | | Confirmed benign | Document and move on | | Unknown / unable to confirm | Deepen investigation (host artifacts, network traffic, user interview) |
Don't leave hits in the "unknown" state. Either resolve, or hand off with a documented next-step.
The hunt's value isn't the one hit — it's the artifacts.
For each hunt:
Hunts that don't produce artifacts are work without compounding return. The whole point of the methodology is to turn every hunt into either a rule, a documented dead-end, or a coverage improvement.
A collection of cybersecurity skills for AI coding agents. The AI does the heavy lifting; you bring the context about your stack and your priorities.
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