AI Architecture
Security Architecture for AI Agent Deployments.
Design the security foundation before agents go live: defence-in-depth, identity boundaries, review gates, and network isolation.
8-layer defence-in-depth
Architecture patterns for AI workloads across model, prompt, tool, identity, network, data, monitoring, and governance layers.
Pre-production review gates
Repeatable review process for AI initiatives before they enter production.
Identity and permission scoping
Least-privilege design for agents with access to APIs, data stores, and internal systems.
AI security is different
This Is Not a Standard Penetration Test.
| Traditional Assessment | AI Security Assessment |
|---|---|
| Tests known vulnerability classes (CVEs, misconfigs) | Tests AI-specific attack surfaces (prompt injection, jailbreaks, context poisoning) |
| Static – tests a point in time | Dynamic – AI agent behaviour changes with context and model updates |
| Tool-driven scanning | Requires human adversarial reasoning plus AI-assisted tooling |
| Scope: infrastructure, applications, APIs | Scope: models, prompts, agent logic, tool access, output validation |
| Pass/fail against known signatures | Nuanced risk – same prompt can succeed or fail depending on framing |
| Covered by most pentest firms | Genuinely specialist – most firms have never tested an AI agent |