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Enterprise AI Security Architecture

A 12-layer view of securing enterprise AI, from data, models and agents to runtime, infrastructure, governance and resilience.

28 topics•6 categories•Updated November 2026

Protect the AI Core

4 entries

Layers 1 to 4: the data, models, agents and prompts at the heart of AI systems.

01Data SecurityControl sensitive data, lineage, encryption, and lifecycle management. 02Model SecurityProtect model integrity, outputs, guardrails, and drift. 03Agent SecurityGovern agent identities, permissions, tools, and autonomous actions. 04Prompt & Context ProtectionDefend against prompt injection, context poisoning, and untrusted retrieval.

Secure Applications, Supply Chain & Runtime

3 entries

Layers 5 to 7: how AI is built, shipped and run.

05AI Application SecuritySecure AI services, user interactions, shadow AI, and application access. 06AI Supply Chain SecurityValidate models, dependencies, third-party components, SBOMs, and CI/CD pipelines. 07Runtime SecurityDetect behavioral anomalies and enforce policies while AI systems execute.

Secure the Platform

2 entries

Layers 8 and 9: the compute and network that AI workloads depend on.

08Infrastructure SecurityProtect containers, GPUs, cloud environments, and compute resources. 09Network SecuritySegment AI workloads and defend against lateral movement and network threats.

Govern, Observe & Recover

3 entries

Layers 10 to 12: accountability, continuous assurance and business continuity.

10AI GovernanceEnforce policies, regulatory controls, accountability, and audit requirements. 11Observability & AssuranceTrack decisions, confidence, performance, and AI behavior continuously. 12Resilience & RecoveryContain failures, automate rollback, and maintain business continuity.

Frameworks & Standards

7 entries

Reference material for building and auditing the architecture.

13NIST AI Risk Management FrameworkNIST framework for managing risks of AI systems across govern, map, measure and manage functions. 14OWASP Top 10 for LLM ApplicationsOWASP list of the most critical security risks for applications built on large language models. 15MITRE ATLASKnowledge base of adversary tactics and techniques against AI-enabled systems. 16ISO/IEC 42001International standard for an AI management system. 17Google Secure AI Framework (SAIF)Google's conceptual framework for securing AI systems. 18Coalition for Secure AI (CoSAI)Open industry initiative producing guidance and tools for secure AI. 19EU AI ActResource on the European Union regulation for artificial intelligence.

Related Pages

9 entries

Related directories on this site.

20AI Agent Security SolutionsTools that protect AI agents. 21AI Security Posture Management (AI-SPM)Posture management for AI. 22AI Agent GuardrailsGuardrails for AI agents. 23AI Agent Governance SolutionsGoverning AI agents. 24AI Governance FrameworksFrameworks for governing AI. 25Zero Trust Security SolutionsZero trust platforms. 26Secrets Management ToolsManaging secrets and credentials. 27AI-Driven Cybersecurity Use-CasesHow AI is used in cybersecurity. 28CybersecurityHub of cybersecurity directories: SIEM, XDR, zero trust, cloud, application and AI security.

Frequently Asked Questions

What is an enterprise AI security architecture?

A layered set of controls that protects AI systems end to end. A mature architecture spans 12 layers: data, model, agent, prompt and context, application, supply chain, runtime, infrastructure, network, governance, observability and resilience.

Why does AI need its own security layers?

AI adds risks that traditional controls do not cover, such as prompt injection, context poisoning, model drift, autonomous agent actions and shadow AI, alongside the usual infrastructure and data risks.

What is prompt injection?

An attack where untrusted input, including retrieved documents, manipulates a model into ignoring instructions or taking unintended actions. The Prompt and Context Protection layer addresses it.

How do you secure AI agents?

Give each agent its own identity, limit permissions and tools to what the task needs, monitor and log actions, and require approval for high-risk autonomous actions. See the Agent Security layer.

What is the AI supply chain?

The models, datasets, libraries, third-party components and pipelines used to build and deploy AI. Validate them, track them with SBOMs and secure the CI/CD pipelines.

What is shadow AI?

Use of AI tools and services by employees or teams without approval or oversight, which can leak data and bypass controls. The AI Application Security layer covers discovery and access control.

Which frameworks should guide the architecture?

The NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, MITRE ATLAS, ISO/IEC 42001 and, where relevant, the EU AI Act.

Where should I start?

Inventory AI systems and data flows, apply governance and data controls first, then add runtime monitoring, agent permissions and resilience. Prioritise layers by the risk of each system.

How were these links chosen?

Each layer links to the most relevant directory on this site, and the frameworks are from their official publishers. Check current guidance, since AI security practice changes quickly.