AI agent risk identification
Key Takeaway
Agents take real actions, so identify where an agent could cause harm while you plan it, and plan a mitigation strategy for each risk before the harm reaches the business.
AI agent risk identification means finding where an agent could cause harm, so you can plan guardrails and controls before the harm reaches the business. Agents need this extra care because they take real actions, such as changing records or sending messages, rather than only analyzing. Teams find risks in two ways: they brainstorm across four categories (People, Business, Technology, and Data), and they threat model the business workflows the agent touches.
Why agents create different risks than traditional AI
AI agents create a different set of risks than traditional AI because agents act, not just analyze. Traditional AI risks such as data poisoning, prompt injection, and hallucinations affect data integrity and output quality. Agent risks go a step further. They involve what happens when that output drives real actions, such as sending messages, changing records, updating systems, or interacting with customers.
When an agent is compromised or misconfigured, the harm moves quickly into operations. For example, a client gets the wrong invoice, a department's payroll fails to run, or a compliance report is generated but never delivered. For this reason, understanding agent risks and setting up guardrails early matters as much as the technology itself.