Skill v1.0.0

version: "1.0.0"

agentic-workflows-blueprint.workflow.analyzing-kubernetes-audit-logs

Goal

Analyze Kubernetes API audit logs to detect high-risk behaviors (privilege escalation, secret access, lateral movement patterns) and produce actionable detection and response artifacts.

Scope

• Applies to: Kubernetes API server audit event streams and exported log files.
• Does not cover: host-level syscall telemetry or network packet-level forensics.

Triggers

• "Analyze Kubernetes audit logs"
• "Investigate suspicious Kubernetes API activity"
• "Create detections for exec/secrets/RBAC abuse"
• "Validate SOC coverage for Kubernetes attack techniques"

Inputs

• auditLogSource: file path, stream, or SIEM index containing audit events
• timeWindow: interval under investigation
• clusterContext: cluster/environment and namespace scope
• detectionProfile: event patterns and severity mapping
• knownIdentities (optional): approved users/service accounts for suppression baselines
• incidentContext (optional): case ID, IOC list, and investigation hypotheses

Invariants

• Parsing logic must preserve original event fields for traceability.
• High-risk event classes are explicitly monitored (pods/exec, secret access, RBAC mutations, privileged pod creation, unauthenticated access).
• Detection outputs must distinguish confirmed suspicious activity from expected admin operations.
• Investigation artifacts must be suitable for SOC handoff and replay.

Procedure

1. Ingest and normalize logs

• Parse JSON-lines audit entries and validate schema consistency.
• Normalize key fields (verb, resource, user, namespace, source IP, response code, stage).

1. Apply high-risk detection rules

• Detect shell access (pods/exec, pods/attach), secret enumeration/access, RBAC change events, and privileged workload creations.
• Detect anonymous/unauthenticated API access and unusual identity behaviors.

1. Correlate and enrich

• Correlate events by user, namespace, workload, and time sequence.
• Enrich with identity criticality, known admin windows, and threat indicators.

1. Classify findings

• Rank detections by severity and confidence.
• Separate expected operational actions from anomalous activity.

1. Generate detection artifacts

• Produce rule candidates or SIEM queries for recurring patterns.
• Document event signatures and false-positive tuning guidance.

1. Incident handoff and response support

• Build timeline of key suspicious actions.
• Provide recommended containment/investigation next steps.

Outputs

• Structured audit analysis report with severity-ranked findings.
• Event timeline highlighting suspicious activity chains.
• Detection rule/query package for SOC/SIEM integration.
• Tuning notes and suppression candidates for noisy expected behavior.

Review gate

• [ ] Core high-risk event categories are covered by explicit detections.
• [ ] Findings include enough context for analyst action.
• [ ] Event evidence is traceable back to raw audit records.
• [ ] Detection package is reproducible and suitable for automation.
• [ ] False-positive handling guidance is documented.

References

• ../../SKILL.md
• ../triaging-vulnerabilities-with-ssvc-framework/SKILL.md
• ../review/SKILL.md