An AI agent becomes operationally useful when it can access knowledge and tools. That same access creates risk. Security must be designed around the agent's job, the sensitivity of data and the impact of each permitted action.
Use least-privilege access
Give the agent only the minimum systems, records and actions required. Separate read and write permissions and avoid shared administrator credentials.
Treat external content as untrusted
Emails, documents and web content can contain instructions that conflict with the agent's rules. Tool calls and sensitive actions should not be controlled directly by untrusted text.
Validate every structured action
Check identifiers, amounts, recipients, dates and required fields before an action reaches a business system. Model output should not bypass deterministic validation.
Create human approval boundaries
Require approval for money movement, contracts, sensitive customer communication, account changes and other high-impact actions.
Log and review behavior
Record model version, input source, tool calls, outputs, errors and approvals. Monitoring helps identify prompt failures, data problems and unexpected usage.
Choose one workflow and document its volume, current time, systems, owner and common exceptions. That information is enough to begin a useful automation assessment.