system-infrastructure-security

To audit AI infrastructure, you evaluate secret management, runtime isolation, and IAM policies to identify misconfigurations. You can generate a prioritized hardening plan by validating configurations and enforcing structured audit logs before deployment.

Preventing secret leakage requires implementing deterministic controls like edge rate limiting and tightly scoped IAM policies. You must apply configuration validation to ensure secrets are not logged and access is restricted to minimum necessary permissions across cloud and self-hosted deployments.

Runtime isolation limits blast radius by restricting network access and enforcing tightly scoped IAM policies. This ensures that if a component is compromised, the attacker cannot pivot to other services or escalate privileges within the AI infrastructure.

Yes, infrastructure threat modeling can be applied across both cloud and self-hosted AI deployments. The process identifies supply-chain risks and deployment misconfigurations, ensuring consistent security controls like audit logging and network isolation are enforced.

Structured audit logs and edge rate limiting are deterministic controls that strengthen runtime security by preventing automated exploitation and providing traceable activity records. They help detect misconfigurations and restrict unauthorized access to AI services.