AI Cyber Defense / Threat modeling and exposure reduction
AI Cyber Defense: Inventory nonhuman access before automation expands
Separate experimental automation identities from production jobs. Grant bounded resource access and remove credentials that have no active deployment and owner.
Environment and inputs: Enterprise attack-surface management, asset inventory and trust-boundary analysis; vendor-neutral.
Scenario and goal
Service identities created for experiments can accumulate privileges that an adversary reuses at machine speed.
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
Collect token issuers, service account owners, last-use records, accessible resources and credential storage locations.
Validate with an authorized exercise
Run a synthetic inventory reconciliation that includes an orphan account; confirm its privileges are flagged and a controlled disablement plan identifies dependent jobs.
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.