AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Detect sustained low-volume export patterns
Combine cumulative transfer monitoring with resource-level export controls. Retain sufficient history to compare the sequence with the principal's actual work pattern.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
A data leak may remain below a large-transfer threshold while accumulating across many small requests.
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
Aggregate destination, principal, exported object count and total bytes over multiple windows. Include failed transfers and approved synchronization schedules.
Validate with an authorized exercise
Replay a synthetic series of small exports and a benign synchronization batch; verify the cumulative case includes object access context and does not reset at each short window.
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.