AI Security Architecture Review
Threat-model and review the end-to-end AI stack—from datasets and model pipelines through RAG, inference gateways, agents, tools and monitoring.
What this capability solves
AI expands the attack surface beyond conventional application security. Data poisoning, model supply chain, prompt injection, retrieval abuse, tool misuse and autonomous privilege need explicit architecture controls.
Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.
Capability model
Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.
Threat Modeling
Map assets, trust boundaries, attack paths and abuse cases across AI components.
Data / Pipeline Security
Provenance, access, poisoning controls, artifact integrity and model registry protections.
RAG Security
Retrieval authorization, source trust, prompt boundary controls and data leakage prevention.
Inference/API Security
Authentication, rate limits, content controls, abuse detection and secrets protection.
Agentic Security
Tool identity, least privilege, action validation, sandboxing and human gates.
Monitoring / Response
AI-specific telemetry, attack indicators, incident playbooks and kill switches.
How the capability fits together
Final topology, control placement and deployment model are validated during discovery and detailed design.
Controls & governance
- Named business and technical owner
- Use-case risk classification and approval gates
- Data provenance, minimization and access control
- Human accountability for high-impact outcomes
- Security and privacy-by-design controls
- Versioned model/prompt/agent configuration
- Pre-release evaluation and red-team gates
- Continuous monitoring, incident and change control
- Audit-ready evidence and management reporting
Priority use cases
- LLM application design review
- RAG architecture review
- Agentic AI review
- AI platform review
- Pre-production security gate
- Model supply-chain review
Key deliverables
- AI threat model
- Architecture risk findings
- Control recommendations
- Secure reference architecture
- Abuse-case test plan
- Remediation backlog
Integration considerations
- Enterprise IAM and workload identity
- Data lake/warehouse and vector/RAG platforms
- Model/API providers and private models
- Application/API integration layer
- MLOps/LLMOps/AgentOps and observability
- SIEM/SOAR and security tooling
- GRC, privacy and evidence repositories
- ITSM/BPM and business workflow systems
Phased delivery
Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.