AI Security Architecture
Architecture review and design for AI/ML systems and integrations.
What it is
AI systems have distinct architectural risks: training-data integrity, model supply chain, inference endpoints, plugin permissions, vector stores and prompt flows. AI security architecture reviews your AI designs and integrations, embedding controls across data, model, application and infrastructure layers.
Coverage
- AI system architecture review
- Data pipeline and vector-store security
- Model supply-chain assessment
- Integration and plugin permission review
- Secure reference patterns
How we deliver
- Map — AI components and data flows.
- Assess — threats across the AI stack.
- Design — controls per layer.
- Guide — support build teams.
- Review — verify before production.
Outcomes
- AI risks designed out early
- Secure patterns for builders
- Supply-chain confidence
- Production-ready assurance