AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Account for NAT when attributing network activity
Document the join path from public tuple to internal asset and user. Avoid automatic endpoint isolation when attribution lacks the necessary translation record.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
Many endpoints can share one public address, making an IP-only incident response target the wrong device.
Why it matters
For this scenario, expect behavior to vary across destinations, timing and client software. Network metadata supports a hypothesis, while endpoint and identity records often explain the actor and purpose. Account for encryption, sampling and sensor placement before interpreting absence. A statistical anomaly, protocol fingerprint or unfamiliar domain should remain a supporting observation until its consequence and business context are established.
Evidence to collect
Retain source ports, translation timestamps, internal addresses and DHCP or VPN assignment history with clock uncertainty.
Validate with an authorized exercise
Use two synthetic clients sharing a translated address and confirm the records identify the correct internal source; missing mappings must produce an unresolved attribution result.
Rollout and checks
Preserve event time, source tuple, sensor identity and the original record identifier for every decisive observation. Verify clock alignment and collection health before joining sources. Compare a representative benign workload with the suspected behavior in a lab or stored-event replay. Adjust a detection only after documenting which observed distinction justifies the change.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.