secure AI architecture
AI Security Control Plane
A production-oriented design for routing model traffic through identity-aware policy, retrieval checks, tool permissions, eval gates, logs, and replayable incidents.
- AI security
- LLMOps
- Threat modeling
- Policy gates
Problem
AI features often reach users before the surrounding operating model is mature. The risk is not only hallucination; it is unreviewed tool access, confused identity, weak auditability, prompt-injection exposure, and unclear rollback behavior.
Approach
- Mapped the request lifecycle across user input, retrieved context, model output, tool calls, downstream actions, and human review.
- Separated product UX from security enforcement so policy checks, logging, and eval rules can evolve without rewriting every feature.
- Defined controls for prompt injection, data leakage, tool misuse, unsafe escalation, refusal regression, and suspicious model behavior.
- Wrote an incident replay path so teams can reconstruct what the model saw, what tools it touched, and why a response was allowed.
Artifacts
- artifactAI feature threat model
- artifactLLM request lifecycle map
- artifactPrompt, retrieval, tool, and output control matrix
- artifactSuspicious model behavior replay worksheet
What this proves
- Trust boundaries are explicit instead of hidden inside AI language.
- Security controls are attached to concrete request stages.
- The artifact is useful for engineering, security review, and operations.
Tools and surfaces
- OpenAI API
- Agent orchestration
- RAG
- Policy checks
- Audit logging
Boundary
Architecture is representative and sanitized. It does not publish private prompts, datasets, customer data, internal controls, or unsafe abuse detail.