AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Investigate lateral discovery through relationship changes
Alert on new peer relationships from restricted roles and correlate with remote-service authentication. Keep authorized discovery identities explicitly registered.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
A server connecting to many new internal peers may be performing inventory or unauthorized discovery.
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
Compare source service role, destination count, connection outcomes and approved maintenance jobs across a suitable baseline.
Validate with an authorized exercise
Replay a known inventory-job fixture and a synthetic new fan-out from an application server; confirm the latter is escalated with destination and authentication evidence.
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.