AI Cyber Defense / Cloud, Kubernetes and software supply chain
AI Cyber Defense: Enforce Kubernetes API access by workload purpose
Assign dedicated service accounts, avoid unnecessary token mounting and reduce verbs and resources to actual need. Review aggregated roles and cluster-wide bindings.
Environment and inputs: Cloud control planes, workload identity, Kubernetes clusters, CI/CD pipelines and immutable deployment artifacts; provider support varies.
Scenario and goal
A container with broad service-account rights can turn an application compromise into cluster administration.
Why it matters
For this scenario, an attacker can automate permitted API operations after gaining a workload or pipeline identity. Review who can change trust, who can assume the resulting identity and what resources it can reach. Keep source review, build provenance, deployment authorization and runtime permissions as distinct checks. A configuration object or successful deployment does not prove the intended runtime boundary is enforced.
Evidence to collect
Review effective RBAC, service-account token use, API audit records and namespace ownership.
Validate with an authorized exercise
Use a test workload identity to perform its required read and attempt an unrelated secret read; verify the latter is denied and attributable in API audit evidence.
Rollout and checks
Evaluate effective permissions and runtime behavior in a disposable account or namespace before production rollout. Retain the approved source revision, resulting artifact and independent audit trail. Plan recovery if identity, policy or admission dependencies fail. Reconcile running assets after the change because resources created outside the normal pipeline can otherwise remain outside its checks.
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.