The AICM (AI Controls Matrix) is a cybersecurity and risk management framework developed by the Cloud Security Alliance (CSA) to help organizations manage AI-specific risks across the AI lifecycle.
AICM stands for AI Controls Matrix, and it is:
- A risk and control framework tailored for Artificial Intelligence (AI) systems.
- Built to address trustworthiness, safety, and compliance in the design, development, and deployment of AI.
- Structured across 18 security domains with 243 control objectives.
- Aligned with existing standards like:
- ISO/IEC 42001 (AI Management Systems)
- ISO/IEC 27001
- NIST AI Risk Management Framework
- BSI AIC4
- EU AI Act
+———————————————————————————+
| ARTIFICIAL INTELLIGENCE CONTROL MATRIX (AICM) |
| 243 Control Objectives | 18 Security Domains |
+———————————————————————————+
| Domain No. | Domain Name | Example Controls Count |
|---|---|---|
| 1 | Governance & Leadership | 15 |
| 2 | Risk Management | 14 |
| 3 | Compliance & Legal | 13 |
| 4 | AI Ethics & Responsible AI | 18 |
| 5 | Data Governance | 16 |
| 6 | Model Lifecycle Management | 17 |
| 7 | Privacy & Data Protection | 15 |
| 8 | Security Architecture | 13 |
| 9 | Secure Development Practices | 15 |
| 10 | Threat Detection & Response | 12 |
| 11 | Monitoring & Logging | 12 |
| 12 | Access Control | 14 |
| 13 | Supply Chain Security | 13 |
| 14 | Business Continuity & Resilience | 12 |
| 15 | Human Factors & Awareness | 14 |
| 16 | Incident Management | 14 |
| 17 | Performance & Explainability | 13 |
| 18 | Third-Party Risk Management | 13 |
| +———————————————————————————+ | ||
| TOTAL CONTROL OBJECTIVES: 243 | ||
| +———————————————————————————+ |
Legend:
📘 = Policy Control
🔧 = Technical Control
🧠 = Human/Process Control
🛡️ = Risk/Compliance Control
🧩 Key Features
- Covers traditional cybersecurity and AI-specific threats (e.g., model poisoning, data leakage, prompt injection).
- Applies across the entire AI lifecycle—from data ingestion and training to deployment and monitoring.
- Includes a companion tool: the AI-CAIQ (Consensus Assessment Initiative Questionnaire for AI), enabling organizations to self-assess or vendor-assess against AICM controls.
🎯 Why It Matters
As AI becomes pervasive in business, compliance, and critical infrastructure, traditional frameworks (like ISO 27001 alone) are no longer enough. AICM helps organizations:
- Implement responsible AI governance
- Identify and mitigate AI-specific security risks
- Align with upcoming global regulations (like the EU AI Act)
- Demonstrate AI trustworthiness to customers, auditors, and regulators
Here are the 18 security domains covered by the AICM framework:
- Audit and Assurance
- Application and Interface Security
- Business Continuity Management and Operational Resilience
- Change Control and Configuration Management
- Cryptography, Encryption and Key Management
- Datacenter Security
- Data Security and Privacy Lifecycle Management
- Governance, Risk and Compliance
- Human Resources
- Identity and Access Management (IAM)
- Interoperability and Portability
- Infrastructure Security
- Logging and Monitoring
- Model Security
- Security Incident Management, E‑Discovery & Cloud Forensics
- Supply Chain Management, Transparency and Accountability
- Threat & Vulnerability Management
- Universal Endpoint Management
Gap Analysis Template based on AICM (Artificial Intelligence Control Matrix)
| # | Domain | Control Objective | Current State (1-5) | Target State (1-5) | Gap | Responsible | Evidence/Notes | Remediation Action | Due Date |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Governance & Leadership | AI governance structure is formally defined. | 2 | 5 | 3 | John D. | No documented AI policy | Draft governance charter | 2025-08-01 |
| 2 | Risk Management | AI risk taxonomy is established and used. | 3 | 4 | 1 | Priya M. | Partial mapping | Align with ISO 23894 | 2025-07-25 |
| 3 | Privacy & Data Protection | AI models trained on PII have privacy controls. | 1 | 5 | 4 | Sarah W. | Privacy review not performed | Conduct DPIA | 2025-08-10 |
| 4 | AI Ethics & Responsible AI | AI systems are evaluated for bias and fairness. | 2 | 5 | 3 | Ethics Board | Informal process only | Implement AI fairness tools | 2025-08-15 |
| … | … | … | … | … | … | … | … | … | … |
🔢 Scoring Scale (Current & Target State)
- 1 – Not Implemented
- 2 – Partially Implemented
- 3 – Implemented but Not Reviewed
- 4 – Implemented and Reviewed
- 5 – Optimized and Continuously Improved
The AICM contains 243 control objectives distributed across 18 security domains, analyzed by five critical pillars, including Control Type, Control Applicability and Ownership, Architectural Relevance, LLM Lifecycle Relevance, and Threat Category.
It maps to leading standards, including NIST AI RMF 1.0 (via AI NIST 600-1), and BSI AIC4 (included today), as well as ISO 42001 & ISO 27001 (next month).
This will be the framework for STAR for AI organizational certification program. Any AI model provider, cloud service provider or SaaS provider will want to go through this program. CSA is leaving it open as to enterprises, they believe it is going to make sense for them to consider the certification as well. The release includes the Consensus Assessment Initiative Questionnaire for AI (AI-CAIQ), so CSA encourage you to start thinking about showing your alignment with AICM soon.
CSA will also adapt our Valid-AI-ted AI-based automated scoring tool to analyze AI-CAIQ submissions
Download info and 7 minute intro video: https://lnkd.in/gZmWkQ8V
#AIGuardrails #CSA #AIControlsMatrix #AICM
🎯 Use Case: ISO/IEC 42001-Based AI Governance Gap Analysis (Customized AICM)
| # | AICM Domain | ISO 42001 Clause | Control Objective | Current State (1–5) | Target State (1–5) | Gap | Responsible | Evidence/Notes | Remediation Action | Due Date |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Governance & Leadership | 5.1 Leadership | Leadership demonstrates AI responsibility and commitment | 2 | 5 | 3 | CTO | No AI charter signed by execs | Formalize AI governance charter | 2025-08-01 |
| 2 | Risk Management | 6.1 Actions to address risks | AI risk register and risk criteria are defined and maintained | 3 | 4 | 1 | Risk Lead | Risk register lacks AI-specific items | Integrate AI risks into enterprise ERM | 2025-08-05 |
| 3 | AI Ethics & Responsible AI | 6.3 Ethical impact assessment | AI system ethical impact is documented and reviewed periodically | 1 | 5 | 4 | Ethics Team | No structured ethical review | Create ethics impact assessment process | 2025-08-15 |
| 4 | Data Governance | 8.3 Data & data quality | Data used in AI is validated, labeled, and assessed for bias | 2 | 5 | 3 | Data Owner | Inconsistent labeling practices | Implement AI data QA framework | 2025-08-20 |
| 5 | Model Lifecycle Management | 8.2 AI lifecycle | AI lifecycle stages are defined and documented (from design to EOL) | 2 | 5 | 3 | ML Lead | No documented lifecycle | Adopt ISO 42001 lifecycle guidance | 2025-08-30 |
| 6 | Privacy & Data Protection | 8.3.2 Privacy & PII | PII used in AI training is minimized, protected, and compliant | 2 | 5 | 3 | DPO | No formal PII minimization strategy | Conduct AI-focused DPIAs | 2025-08-10 |
| 7 | Monitoring & Logging | 9.1 Monitoring | AI systems are continuously monitored for drift, bias, and failure | 3 | 5 | 2 | DevOps | Logging enabled, no alerts set | Automate AI model monitoring | 2025-09-01 |
| 8 | Performance & Explainability | 8.4 Explainability | Models provide human-understandable decisions where needed | 1 | 4 | 3 | AI Team | Black-box model in production | Adopt SHAP/LIME/XAI tools | 2025-09-10 |
| … | … | … | … | … | … | … | … | … | … | … |
🧭 Scoring Scale:
- 1 – Not Implemented
- 2 – Partially Implemented
- 3 – Implemented but not Audited
- 4 – Audited and Maintained
- 5 – Integrated and Continuously Improved
🔗 Key Mapping to ISO/IEC 42001 Sections:
- Clause 4: Context of the organization
- Clause 5: Leadership
- Clause 6: Planning (risk, opportunities, impact)
- Clause 7: Support (resources, awareness, documentation)
- Clause 8: Operation (AI lifecycle, data, privacy)
- Clause 9: Performance evaluation (monitoring, audit)
- Clause 10: Improvement (nonconformity, corrective action)
DISC InfoSec’s earlier posts on the AI topic
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