An AI prototype can be built quickly. A secure production system requires deliberate engineering around the model. The largest risks often come from permissions, integrations, data leakage, and unchecked actions—not from the model alone.
1. Minimize data exposure
Send only the information required for the task. Classify sensitive data, redact where possible, encrypt in transit and at rest, and document retention policies for prompts, files, and outputs.
2. Enforce identity and authorization
The AI must never bypass application permissions. Use the authenticated user's identity for every retrieval and tool call. Service accounts should have narrow roles and short-lived credentials.
3. Treat external content as untrusted
Documents, websites, and emails can contain prompt-injection instructions. Separate system policy from retrieved content, restrict available tools, validate arguments, and do not let untrusted text redefine permissions.
4. Validate outputs and actions
Use schemas for structured outputs. Apply business rules after generation. Require human approval for payments, account changes, legal communication, destructive operations, or high-impact customer decisions.
5. Monitor the full system
Record model version, prompt version, retrieved evidence, tool calls, latency, cost, errors, and user feedback. Create alerts for unusual volume, repeated denials, data-access anomalies, and unsafe output patterns.
6. Test continuously
- Prompt injection and data-exfiltration tests
- Authorization boundary tests
- Hallucination and citation evaluation
- Tool failure and timeout scenarios
- Incident response and rollback exercises
Security should make AI useful with confidence. Start with a threat model, keep autonomy bounded, and expand capabilities only after evidence shows the controls work.