Secure, Govern, and Deploy AI Solutions
Security and governance are design requirements, not a final checklist. Start with identity and least privilege: the AI service should access only the data and tools it needs, using managed identities and scoped roles. Separate the model's permissions from the user's - enforce authorization at the data layer, not in the prompt. Apply defense in depth against prompt injection: treat retrieved content and user input as untrusted, constrain tool permissions, and require confirmation for high-impact actions.
Data protection covers classification, PII detection and redaction, encryption in transit and at rest, retention limits, and residency. Know where data flows: prompts, logs, retrieval indexes, and third-party model endpoints. Governance includes an inventory of AI use cases, risk assessment, human oversight for consequential decisions, audit trails, and clear accountability.
Deployment patterns: managed endpoints vs. self-hosted, blue/green or canary rollout with rollback, environment separation (dev/test/prod with distinct credentials), and infrastructure-as-code for reproducibility. Use canary releases to limit blast radius when prompts or models change.
Traps:
- Relying on prompt instructions as a security control.
- Logging full prompts containing sensitive data.
- Granting the agent broad write access for convenience.
- Deploying prompt changes without canary or rollback.
Rule of thumb: assume the model can be manipulated; enforce every critical control outside the model. Worked example: for an agent that issues refunds, cap refund amount in the tool implementation, require human approval above a threshold, log every action with actor identity, and scope credentials to the refund API only.