Security
Security architecture for agentic AI
Threat-informed design for AI platforms — prompt risks, access control, observability, and sovereign deployment patterns across every layer.
AI security architecture
Threat-informed architectures for AI platforms spanning models, agents, data pipelines, and integration layers.
Secure agentic AI
Isolation, least-privilege tool access, and execution boundaries for multi-agent systems in production.
MCP security
Securing Model Context Protocol servers and clients — authentication, authorisation, context leakage prevention, and supply-chain considerations.
AI observability
Logging, tracing, and evaluation pipelines that make AI behaviour inspectable for security and operations teams.
- Prompt and response audit trails
- Tool invocation monitoring
- Anomaly detection foundations
Prompt injection mitigation
Layered defences including input validation, policy enforcement, output filtering, and architectural separation of untrusted content.
Data sovereignty & private AI
Deployment patterns for on-premises, private cloud, and sovereign regions — with governance controls throughout.
Review your AI security posture
Identify gaps across agents, MCP integrations, data flows, and operational monitoring.