What is an AI agent risk assessment?
An AI agent risk assessment identifies what the agent can access, what actions it can perform, what information it processes and what could happen if its behavior is manipulated or simply wrong.
Risk depends on impact as well as likelihood. An agent that only drafts public text has a very different risk profile from one that can send payments, modify customer records or execute operational workflows.
Inventory the agent first
Document the model, tools, APIs, data sources, users, authentication methods and environments connected to the agent. Hidden dependencies make security reviews unreliable.
Identify high-impact capabilities
- Write or delete data.
- Send external messages.
- Access confidential records.
- Trigger financial or operational actions.
- Execute code or commands.
- Change account permissions or configuration.
Evaluate abuse paths
Consider prompt injection, compromised external content, stolen user sessions, excessive permissions, secret leakage, unsafe tool parameters and failures in human approval workflows.
Prioritize remediation
Fix issues that combine broad permissions with high business impact first. Reducing privileges, adding deterministic validation and requiring approval for high-risk actions can often lower risk more effectively than adding another instruction to the model prompt.
Frequently asked questions
What makes an AI agent high risk?
Risk increases when an agent can access sensitive information or perform high-impact actions with broad permissions and weak independent validation.
Should risk be reassessed after deployment?
Yes. Changes to models, tools, prompts, users, data sources and permissions can materially change the risk profile.