Secure AI architecture
Reference designs for RAG, agents and copilots with clear trust boundaries and least privilege.
AI Security
Secure AI Engineering
Bring security into your AI architecture from day one. We work with your engineers to design guardrails, isolate tools, protect data and monitor AI systems in production.
The challenge
Teams under pressure to ship AI features often connect models directly to sensitive data and powerful tools. Retrofitting security later is expensive, and some design decisions are very hard to undo.
Typical risks we uncover
- 01Models with broad access to data and tools
- 02No separation between trusted and untrusted content
- 03Little visibility into what AI systems do in production
AI Security
What we do
Design and build AI applications, RAG pipelines and agents that are secure by design.
01
02
Guardrails & filtering
Layered input and output controls, content classification and policy enforcement.
03
Data protection for AI
Access-aware retrieval, PII redaction, encryption and tenant isolation for vector stores.
04
Tool & agent sandboxing
Scoped permissions, human-in-the-loop approvals and isolation for actions with real-world impact.
05
MLOps security
Protect training data, model artefacts and registries, and verify model provenance and integrity.
06
Monitoring & evaluation
Logging, abuse detection and continuous security evaluations integrated into your release process.
Secure AI Engineering
How the engagement runs
011/4
Architecture review
Review designs and code for your AI use cases and identify risks early.
022/4
Secure patterns
Agree reusable patterns for retrieval, tools, identity and guardrails.
033/4
Co-build
Implement controls alongside your engineers, in your codebase and pipelines.
044/4
Evaluate
Add automated security evaluations so regressions are caught before release.
01
What you receive
- AI security architecture review
- Secure reference patterns for RAG and agents
- Guardrail and policy implementation
- Data access model for AI workloads
- Security evaluation suite in CI
- Engineering playbook and training
02
Frameworks & standards
FAQ
Frequently asked questions
Talk to a consultant about your environment. The first call is free and under NDA if you need it.
Talk to an expert01Which frameworks and platforms do you work with?
We are framework-agnostic: LangChain, LlamaIndex, Semantic Kernel or custom code, and managed AI platforms from AWS, Microsoft and Google.
02Can you help secure MCP servers and agent tools?
Yes. We review tool permissions, authentication, input handling and isolation for MCP servers, plugins and function-calling integrations.
03Do you build AI products for us?
We build the security-critical parts with your team (guardrails, data access, sandboxing and evaluations) rather than replacing your product engineers.
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Learn moreNext step
Let's map your cloud and AI risk in a single conversation.
Book a free 30-minute scoping call with a senior consultant. No sales script: we listen, ask the right questions and tell you honestly where we can help.