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sica-fondt/knowledge/cyber-skills/skills/detecting-container-drift-at-runtime/references/workflows.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

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Workflows - Container Drift Detection

Detection Workflow

  1. Container image deployed with known-good state
  2. Runtime monitor (Falco/Sysdig) tracks all process executions and file changes
  3. Events compared against baseline: original image manifest + expected runtime behavior
  4. Drift events classified by severity (binary drift = HIGH, config drift = MEDIUM)
  5. Alerts sent to SIEM/SOC with full container context
  6. Automated response: isolate pod network, capture forensics, evict pod

Implementation Phases

Phase 1: Visibility (Weeks 1-2)

  • Deploy Falco with drift detection rules in alert-only mode
  • Collect baseline of normal container behavior per workload
  • Identify legitimate runtime changes (log files, temp files, caches)
  • Create allowlists for expected runtime modifications

Phase 2: Detection (Weeks 3-4)

  • Enable drift detection alerts with tuned thresholds
  • Integrate with SIEM for correlation and dashboarding
  • Build runbooks for drift investigation
  • Conduct tabletop exercises with container drift scenarios

Phase 3: Prevention (Weeks 5-8)

  • Enable readOnlyRootFilesystem on all production workloads
  • Deploy Pod Security Standards in enforce mode
  • Implement image digest pinning in all manifests
  • Enable automated pod eviction for confirmed drift events

Incident Response for Drift Events

  1. Triage: Is the drift from a legitimate operation or potential compromise?
  2. Contain: Apply NetworkPolicy deny-all to affected pod
  3. Collect: Capture container filesystem diff, process tree, network connections
  4. Analyze: Compare drifted files against malware signatures and IoCs
  5. Remediate: Delete compromised pod, scan all pods in namespace
  6. Recover: Deploy clean image, verify no persistence mechanisms