Jul 18 2025

Mitigate and adapt with AICM (AI Controls Matrix)

Category: AI,ISO 42001 — disc7 @ 9:03 am

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 NameExample Controls Count
1Governance & Leadership15
2Risk Management14
3Compliance & Legal13
4AI Ethics & Responsible AI18
5Data Governance16
6Model Lifecycle Management17
7Privacy & Data Protection15
8Security Architecture13
9Secure Development Practices15
10Threat Detection & Response12
11Monitoring & Logging12
12Access Control14
13Supply Chain Security13
14Business Continuity & Resilience12
15Human Factors & Awareness14
16Incident Management14
17Performance & Explainability13
18Third-Party Risk Management13
+———————————————————————————+
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:

  1. Audit and Assurance
  2. Application and Interface Security
  3. Business Continuity Management and Operational Resilience
  4. Change Control and Configuration Management
  5. Cryptography, Encryption and Key Management
  6. Datacenter Security
  7. Data Security and Privacy Lifecycle Management
  8. Governance, Risk and Compliance
  9. Human Resources
  10. Identity and Access Management (IAM)
  11. Interoperability and Portability
  12. Infrastructure Security
  13. Logging and Monitoring
  14. Model Security
  15. Security Incident Management, E‑Discovery & Cloud Forensics
  16. Supply Chain Management, Transparency and Accountability
  17. Threat & Vulnerability Management
  18. Universal Endpoint Management

Gap Analysis Template based on AICM (Artificial Intelligence Control Matrix)

#DomainControl ObjectiveCurrent State (1-5)Target State (1-5)GapResponsibleEvidence/NotesRemediation ActionDue Date
1Governance & LeadershipAI governance structure is formally defined.253John D.No documented AI policyDraft governance charter2025-08-01
2Risk ManagementAI risk taxonomy is established and used.341Priya M.Partial mappingAlign with ISO 238942025-07-25
3Privacy & Data ProtectionAI models trained on PII have privacy controls.154Sarah W.Privacy review not performedConduct DPIA2025-08-10
4AI Ethics & Responsible AIAI systems are evaluated for bias and fairness.253Ethics BoardInformal process onlyImplement AI fairness tools2025-08-15
…………………………

🔢 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 DomainISO 42001 ClauseControl ObjectiveCurrent State (1–5)Target State (1–5)GapResponsibleEvidence/NotesRemediation ActionDue Date
1Governance & Leadership5.1 LeadershipLeadership demonstrates AI responsibility and commitment253CTONo AI charter signed by execsFormalize AI governance charter2025-08-01
2Risk Management6.1 Actions to address risksAI risk register and risk criteria are defined and maintained341Risk LeadRisk register lacks AI-specific itemsIntegrate AI risks into enterprise ERM2025-08-05
3AI Ethics & Responsible AI6.3 Ethical impact assessmentAI system ethical impact is documented and reviewed periodically154Ethics TeamNo structured ethical reviewCreate ethics impact assessment process2025-08-15
4Data Governance8.3 Data & data qualityData used in AI is validated, labeled, and assessed for bias253Data OwnerInconsistent labeling practicesImplement AI data QA framework2025-08-20
5Model Lifecycle Management8.2 AI lifecycleAI lifecycle stages are defined and documented (from design to EOL)253ML LeadNo documented lifecycleAdopt ISO 42001 lifecycle guidance2025-08-30
6Privacy & Data Protection8.3.2 Privacy & PIIPII used in AI training is minimized, protected, and compliant253DPONo formal PII minimization strategyConduct AI-focused DPIAs2025-08-10
7Monitoring & Logging9.1 MonitoringAI systems are continuously monitored for drift, bias, and failure352DevOpsLogging enabled, no alerts setAutomate AI model monitoring2025-09-01
8Performance & Explainability8.4 ExplainabilityModels provide human-understandable decisions where needed143AI TeamBlack-box model in productionAdopt SHAP/LIME/XAI tools2025-09-10
……………………………

🧭 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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Tags: #AI Guardrails, #CSA, AI Controls Matrix, AICM, Controls Matrix, EU AI Act, iso 27001, ISO 42001, NIST AI Risk Management Framework

Jul 16 2024

Understanding Compliance With the NIST AI Risk Management Framework

Category: NIST Privacy,Risk Assessment — disc7 @ 10:06 am
Understanding Compliance With the NIST AI Risk Management Framework

Incorporating artificial intelligence (AI) seems like a logical step for businesses looking to maximize efficiency and productivity. But the adverse effects of AI use, such as data security risk and misinformation, could bring more harm than good.

According to the World Economic Forum’s Global Risks Report 2024, AI-generated misinformation and disinformation are among the top global risks businesses face today.

To address the security risks posed by the increasing use of AI technologies in business processes, the National Institute of Standards and Technology (NIST) released the Artificial Intelligence Risk Management Framework (AI RMF 1.0) in January 2023. 

Adhering to this framework not only puts your organization in strong position to avoid the dangers of AI-based exploits, it also adds an impressive type of compliance to your portfolio, instilling confidence in external stakeholders. Moreover, while NIST AI RMF is more of a guideline than a regulation, today there are several AI laws in the process of being enacted, so adhering to NIST’s framework helps CISOs to future-proof their AI compliance postures.

Let’s examine the four key pillars of the framework – govern, map, measure and manage – and see how you can incorporate them to better protect your organization from AI-related risks.

1.Establish AI Governance Structures

In the context of NIST AI RMF, governance is the process of establishing processes, procedures, and standards that guide responsible AI development, deployment, and use. Its main goal is to connect the technical aspect of AI system design and development with organizational goals, values, and principles.

Strong governance starts from the top, and NIST recommends establishing accountability structures with the appropriate teams responsible for AI risk management, under the framework’s “Govern” function. These teams will be responsible for putting in place structures, systems and processes, with the end goal of establishing a strong culture of responsible AI use throughout the organization.

Using automated tools is a great way to streamline the often tedious process of policy creation and governance. “We view it as our responsibility to help organizations maximize the benefits of AI while effectively mitigating the risks and ensuring compliance with best practices and good governance,” said Arik Solomon, CEO of Cypago, a SaaS platform that automates governance, risk management, and compliance (GRC) processes in line with the latest frameworks.

“These latest features ensure that Cypago supports the newest AI and cyber governance frameworks, enabling GRC and cybersecurity teams to automate GRC with the most up-to-date requirements.”

Rather than existing as a stand-alone component, governance should be incorporated into every other NIST AI RMF function, particularly those associated with assessment and compliance. This will foster a strong organizational risk culture and improve internal processes and standards.

2.Map And Categorize AI Systems

The framework’s “Map” function supports governance efforts while also providing a foundation for measuring and managing risk. It’s here that the risks associated with an AI system are put into context, which will ultimately determine the appropriateness or need for the given AI solution.

As Opice Blum data privacy expert Henrique Fabretti Moraes explained, “Mapping the tools in use – or those intended for use – is crucial for understanding and fine-tuning acceptable use policies and potential mitigation measures to decrease the risks involved in their utilization.” 

But how do you actually put this mapping process into practice?

NIST recommends the following approach:

  • Clearly establish why you need or want to implement the AI system. What are the expectations? What are the prospective settings where the system will be deployed? You should also determine the organizational risk tolerance for operating the system.
  • Map all of the risks and benefits associated with using the system. Here is where you should also determine your risk tolerance, not only with monetary costs but also those stemming from AI errors or malfunctions.
  • Analyze the likelihood and magnitude of the impact the AI system will have on the organization, including employees, customers, and society as a whole.

3.Measure AI Performance and Risk

The “Measure” function utilizes qualitative and quantitative techniques to analyze and monitor the AI-related risks identified in the “Map” function.

AI systems should be tested before deployment and frequently thereafter. But measuring risk with AI systems can be tricky. The technology is fairly new, so there are no standardized metrics yet. This might change in the near future, as developing these metrics is a high priority for many consulting firms. For example, Ernst & Young (EY) is developing an AI Confidence Index. 

“Our confidence index is founded on five criteria – privacy and security, bias and fairness, reliability, transparency and explainability, and the last is accountability,” noted Kapish Vanvaria, EY Americas Risk Market Leader. The other axis includes regulations and ethics. 

“Then you can have a heat map of the different processes you’re looking at and the functions in which they’re deployed,” he says. “And you can go through each one and apply a weighted scoring method to it.”

In the NIST framework’s priorities, there are three main components of an AI system that must be measured: trustworthiness, social impact, and how humans interact with the system. The measuring process will likely consist of extensive software testing, performance assessments and benchmarks, along with reporting and documentation of results.

4.Adopt Risk Management Strategies

The “Manage” function puts everything together by allocating the necessary resources to regularly attend to uncovered risks during the previous stages. The means to do so are typically determined with governance efforts, and can be in the form of human intervention, automated tools for real-time detection and response, or other strategies.

To manage AI risks effectively, it’s crucial to maintain ongoing visibility across all organizational tools, applications, and models. AI should not be handled as a separate entity but integrated seamlessly into a comprehensive risk management framework.

Ayesha Gulley, an AI policy expert from Holistic AI, urges businesses to adopt risk management strategies early, taking into account five factors: robustness, bias, privacy, exploitability and efficacy. Holistic’s software platform includes modules for AI auditing and risk posture reporting.

“While AI risk management can be started at any point in the project development,” she said, “implementing a risk management framework sooner than later can help enterprises increase trust and scale with confidence.”

Evolve With AI

The NIST AI Framework is not designed to restrict the efficient use of AI technology. On the contrary, it aims to encourage adoption and innovation by providing clear guidelines and best practices for developing and using AI securely and responsibly.

Implementing the framework will not only help you reach compliance standards but also make your organization much more capable of maximizing the benefits of AI technologies without compromising on risk.

AI-RMF A Practical Guide for NIST AI Risk Management Framework

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Tags: NIST AI Risk Management Framework