LLM & RAG security
Assess prompt injection, retrieval boundaries, data leakage and untrusted content in AI application designs.
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AI security
Make AI useful, controlled and accountable.
Bring security architecture and governance to LLM applications, RAG systems and AI agents — from design to operation.
Discuss ai security ↗Scope of support
From assessment to an actionable design.
Agent permissions
Define tool access, delegated identity, approval boundaries and monitoring for agent workflows.
AI architecture
Design data flows, retrieval services, model interfaces and control points for enterprise AI adoption.
AI governance
Connect model inventories, risk assessment, testing, ownership and assurance evidence to business use cases.
Practical outputs
Deliverables your teams can use.
Scope and depth are agreed around your environment, constraints and priorities.
- AI application threat model and security assessment
- LLM, RAG or agent reference architecture
- Identity, tool permission and data-control requirements
- Model risk register and governance roadmap
Explore the supporting work.
Start with your current challenge.
Discuss your environment, objectives and the decisions you need to make.
Discuss a project ↗