AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Use egress volume relative to data access context
Create workload-specific transfer budgets and destination controls. Escalate unusual exports when the principal also reads data outside its normal purpose.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
A large upload can be a backup or an unauthorized transfer; byte count alone is not enough.
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
Combine proxy or flow volume with user role, destination approval and preceding access to sensitive datasets.
Validate with an authorized exercise
Compare a scheduled backup fixture with a synthetic unscheduled export by a low-volume identity; verify both the destination and data-access rationale are visible.
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.