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Securing AI Agents: Permissions, Boundaries, and Safe Tool Use

7 chapters · 31 lessons

1. 1. Agent Architecture and Attack Surface Analysis

   4 lessons

   Map the components where AI agents process mixed-trust data and identify where manipulation risks emerge in the execution pipeline.

2. 2. Least Privilege Tool Access Patterns

   4 lessons

   Implement permission models that limit agent capabilities to only what each workflow requires, reducing blast radius of manipulation.

3. 3. Data Boundary Separation Techniques

   5 lessons

   Architect systems that clearly distinguish between trusted instructions and untrusted external content throughout the processing pipeline.

4. 4. Confirmation Gates and Human Approval Workflows

   4 lessons

   Build human-in-the-loop controls that require verification before agents execute sensitive, irreversible, or high-impact actions.

5. 5. Monitoring, Logging, and Anomaly Detection

   5 lessons

   Implement observability systems that track agent behavior, detect unusual patterns, and provide audit trails for security review.

6. 6. Input Validation and Output Constraints

   4 lessons

   Apply filtering, parsing, and type enforcement to limit what agents can read and how they can respond when interacting with tools.

7. 7. Production Deployment and Security Hardening

   5 lessons

   Apply defense-in-depth strategies, incident response planning, and continuous security testing for agent systems in production.