Autonomous systems need more than perimeter security. They need policy, traceability, verification, and failure containment.
Agents and engines access only the data and tools a task requires. Permissions are scoped per request and expire with the task, so a single compromised step cannot reach the wider system.
Important actions produce inspectable, replayable records of reasoning and execution. Each decision can be reconstructed step by step, turning debugging and audit into reading history, not guesswork.
Risky actions are constrained by policies, thresholds, approvals, and rollback paths before they run. Limits are enforced at execution time, not left as guidance, so unsafe actions are blocked, not just logged.
AI systems introduce new failure modes: prompt injection, tool misuse, unintended data disclosure, hallucinated actions, stale context, and unbounded autonomy.
Untrusted input attempts to manipulate model behavior or tool access.
An agent with excessive permissions can turn a small reasoning error into a real-world action.
Models, tools, packages, and connectors must be versioned, audited, and monitored.
Good autonomous infrastructure makes allowed actions explicit and unsafe actions difficult.