AI agents become risky when they move from demonstration into real workflows. They gain access to tools, identities, business data, approval paths, and human trust.
Before production, teams should answer five questions:
- What can the agent see?
- What can the agent change?
- Which tools can it call?
- Who approves sensitive actions?
- How will behavior be observed after launch?
The goal is not to block adoption. The goal is to make autonomous behavior bounded, testable, and accountable before it touches business-critical systems.
Start With Inventory
Map the agent, its tools, data sources, identities, prompts, human review points, and downstream systems. If the system cannot be mapped, it cannot be governed.
Threat Model the Workflow
Look beyond prompt injection. Enterprise agent risk also includes tool misuse, excessive permissions, stale context, indirect data exposure, approval bypass, and unclear ownership.
Validate Before Operation
Run focused adversarial tests against the actual workflow. Capture evidence, prioritize findings, and verify remediation before increasing autonomy.