AI security is fundamentally about observability with context. We track inputs, outputs, and model behavior patterns, then compare against established baselines to detect anomalies; logged to a per-integration API with per-deploy specifics and end-to-end configuration details (Yay! SR11-7v2!! Go Governance!!).
- Track prompts, responses, and processing patterns
- Establish baselines of normal vs. abnormal behavior
- Apply data classification (PICR) for appropriate handling
- Compare current behavior against established patterns
- Alert on deviations that exceed thresholds
- Apply lightweight checks broadly, heavier verification selectively
- Validate models against known attack patterns
- Implement continuous security testing
- Maintain audit trails for compliance
Each team has clear responsibilities:
- Enterprise: Set security standards, define classifications
- IT/Ops: Configure runtime environments, validation parameters
- Application Teams: Implement controls, monitor business metrics
| View | Description | Link |
|---|---|---|
| Context | High-level system context | View |
| Container | Deployment components | View |
| Component | Key functional components | View |
| Code | Implementation details | View |
| Personas | User/stakeholder roles | View |
- Compliance: Meet regulatory requirements with audit trails
- Security: Detect and mitigate novel AI-specific threats
- Operational: Faster incident response with clear accountability
- Define your data classification scheme
- Establish baseline metrics for normal model behavior
- Implement logging and monitoring with appropriate alerts
- Create clear response procedures for anomalies