⚡ Quick Summary
The AI Controls Matrix (AICM) v1.0, developed by the Cloud Security Alliance, is one of the most comprehensive attempts to operationalize AI security and governance in practice. Built on the well-known Cloud Controls Matrix (CCM), AICM extends cloud security logic into the GenAI and LLM domain, covering the full AI supply chain — from infrastructure and model development to orchestration, applications, and end-user consumption.
What makes AICM stand out is its explicit Shared Security Responsibility Model (SSRM), which clearly allocates control ownership between Cloud Service Providers, Model Providers, Orchestrated Service Providers, Application Providers, and AI Customers. Instead of abstract principles, AICM offers 243 concrete controls across 18 domains, each mapped to AI lifecycle phases, architectural layers, threat categories, and major regulatory frameworks.
This is not a high-level ethics document. It is a control framework designed for audits, procurement, risk management, and regulatory readiness in real GenAI deployments.
🧩 What’s Covered
AICM v1.0 is structured around 18 security and governance domains, including Model Security, Data Security and Privacy Lifecycle Management, Governance Risk and Compliance, Application and Interface Security, and Supply Chain Transparency. Each domain contains detailed control specifications describing expected security, safety, and privacy outcomes for AI systems across their lifecycle.
A central pillar is the Shared Security Responsibility Model. Every control specifies whether it is owned by a single actor (such as a Model Provider or CSP), shared between specific parties, or shared across the entire AI supply chain. This is particularly valuable for GenAI environments where responsibilities are often fragmented and misunderstood.
Each control is further enriched with multiple contextual mappings:
– architectural relevance (physical, network, compute, storage, application, data),
– lifecycle relevance (from data preparation to model retirement),
– AI-specific threat categories (e.g. model poisoning, prompt injection, model theft, sensitive data disclosure).
Beyond the core matrix, the framework includes implementation guidelines, auditing guidelines, and the AI Consensus Assessment Initiative Questionnaire (AI-CAIQ). Together, these allow organizations not only to define controls, but also to assess, evidence, and communicate their AI security posture to customers, auditors, and regulators.
Crucially, AICM is mapped to ISO/IEC 42001, NIST AI 600-1, BSI AIC4, and the EU AI Act, enabling organizations to use a single control set to support multi-framework compliance and reduce audit fragmentation.
💡 Why it matters?
AICM addresses a real gap between AI principles and operational reality. Many organizations understand whatresponsible AI should look like, but struggle with who is responsible for implementing controls and how those controls translate into day-to-day practices.
By combining a control-based approach with explicit responsibility allocation, AICM makes AI governance auditable, contract-ready, and enforceable. It is especially valuable in complex GenAI supply chains, where legal, security, and technical teams need a shared language to manage risk, demonstrate compliance, and respond to incidents.
For organizations preparing for the EU AI Act, AICM provides a practical bridge between regulatory obligations and technical implementation, without forcing a complete reinvention of existing security programs.
❓ What’s Missing
AICM deliberately avoids prescriptive technical implementations, which is understandable but may frustrate less mature organizations looking for step-by-step guidance. Effective use of the framework assumes existing security, GRC, and AI engineering capabilities.
The focus is clearly on GenAI and LLM-centric architectures. While adaptable, traditional ML systems or non-cloud AI deployments may require additional interpretation. Finally, the framework’s size and depth can feel heavy for smaller organizations without dedicated AI governance resources.
👥 Best For
– AI service providers operating across complex GenAI supply chains
– Organizations preparing for ISO/IEC 42001 or EU AI Act compliance
– Security, risk, and compliance teams seeking auditable AI controls
– Auditors and consultants conducting AI security and governance assessments
📄 Source Details
Cloud Security Alliance, Introductory Guidance to AI Controls Matrix (AICM) v1.0, 2025.
📝 Thanks to
Marina Bregkou and the Cloud Security Alliance AI Controls Framework Working Group for advancing practical, auditable AI governance grounded in real-world security experience.
