AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Monitor newly registered domains without broad collateral blocking
Use domain novelty to enrich risk and apply stricter treatment to sensitive workloads. Allow reviewed exceptions without whitelisting an entire hosting provider.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
A recently created domain can be malicious or a legitimate supplier launch.
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
Collect domain age from a trusted enrichment source, first-seen traffic, business owner and authentication or download events connected to the destination.
Validate with an authorized exercise
Test a fictional new supplier domain record and an unowned destination fixture; verify the approved business exception is narrow and the unowned case remains reviewable.
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.