AI Cyber Defense / Threat modeling and exposure reduction
AI Cyber Defense: Measure detection coverage for critical paths
Prioritize missing identity and destination evidence before adding more generic alerts. Assign a collection owner and a failure alarm for each indispensable data source.
Environment and inputs: Enterprise attack-surface management, asset inventory and trust-boundary analysis; vendor-neutral.
Scenario and goal
A dashboard full of controls may hide an unmonitored hop between remote access and privileged data.
Why it matters
For this scenario, plan for a shorter interval between an observable exposure and an attempt to use it. Improving the response queue is useful only if the team can also change the reachable access path. Work from the resource an adversary could reach and the authority that resource confers; a product label or vulnerability total does not describe that consequence.
Evidence to collect
For each critical path, list the event emitted at every trust boundary and the join keys available to connect those events.
Validate with an authorized exercise
Replay one benign event at each boundary and confirm it can be joined into a single timeline with correct identities and bounded timestamp uncertainty.
Rollout and checks
Review the asset owner, effective reachability and business dependency before changing exposure. Preserve a dated inventory snapshot and compare the effective state after the change. If a required dependency fails, use the approved rollback while keeping a narrower temporary boundary wherever feasible. An unverified asset or path remains an investigation item rather than evidence of safety.
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.