Organizations leveraging AI for automation and generative tasks need robust AI risk management, and that starts with ISO 42001. Implementing the ISO/IEC42001:2023 framework helps ensure your AI tools and systems are secure, compliant, and trustworthy for clients and partners. Wondering if your organization’s AI governance meets best practices? Request a consultation to assess your compliance today.
How to Manage AI Risks with ISO 42001
ISO 42001 (ISO/IEC 42001:2023) is a voluntary management system standard developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It provides a framework for AI management systems (AIMS), enforcing top-down controls to ensure AI practices are secure, ethical, and efficient.
A key aspect of ISO 42001 compliance is AI risk management. Understanding AI risk management under ISO 42001 involves:
- Framework context: How the ISO 42001 structure governs AI initiatives.
- Direct risk controls: How ISO 42001 mitigates risks specific to AI systems.
- Broader considerations: Integrating AI risk management into organizational practices.
- Certification process: Steps to achieve ISO 42001 compliance and certification.
Partnering with a security program advisor ensures your organization can plan, implement, and assess AI risk controls effectively, satisfying regulatory requirements while maximizing operational efficiency.
Understanding ISO 42001
ISO 42001 (ISO/IEC 42001:2023) is a standard designed to optimize organizational AI Management Systems (AIMS). While it is not legally required in the U.S. or internationally, it is shaping emerging AI legislation worldwide. Adopting ISO 42001 is considered a best practice for organizations operating internationally or using AI tools and systems.
Although ISO 42001 certification is optional, many organizations pursue it to demonstrate a commitment to secure, ethical, and efficient AI governance.
Unlike prescriptive regulations, ISO 42001 provides best practices rather than mandatory controls. Organizations can implement these practices in ways that best fit their operations, allowing flexibility while maintaining robust AI governance.
The framework is structured into 10 clauses:
- Clauses 1–3: Scope, normative references, and terms and definitions
- Clauses 4–10: Guidelines to achieve effective AI governance
Additionally, annexes provide detailed guidance on controls and how to meet ISO 42001 requirements.
For AI risk management, the standard includes select controls that address AI-specific risks, offering guidance on designing IT systems to minimize potential issues and enhance operational security.
AI Risk Considerations in ISO 42001
While ISO 42001 provides broad guidance for AI management systems, explicit coverage of AI risk management represents a focused portion of the framework. Key controls directly addressing AI system risks include:
Clause 6: Planning
- 6.1: Actions to address risks and opportunities: Organizations are required to identify, evaluate, and respond to risks and opportunities related to their AI management systems. This includes consideration of sub-clauses referenced in commentaries (6.1.1–6.1.3) for assessment, treatment, and impact evaluation, though these are not normative requirements. The goal is to integrate AI risk management into overall planning activities.
Clause 8: Operation
- 8.2: AI risk assessment: Establish a systematic, documented process to evaluate risks that could affect AIMS objectives, ensuring assessments align with the organization’s context and scale.
- 8.3: AI risk treatment: Prioritize identified risks and implement treatment plans, applying controls proportional to their assessed severity and likelihood.
- 8.4: AI system impact assessment: Evaluate the potential consequences of identified risks, enabling decision-makers to apply effective governance or mitigation measures.
Clause 10: Improvement
- This clause addresses risks related to noncompliance and nonconformity. While these risks are not explicitly labeled as “AI risks,” they are integral to maintaining an effective AI governance system.
By following these controls, organizations can ensure that AI risk management under ISO 42001 is proactive, structured, and aligned with operational and compliance objectives.
AI Risk Management Methods in ISO 42001
While ISO 42001 does not prescribe specific AI risk management methods, it defines desired outcomes that organizations can achieve through approaches tailored to their unique AI environments. To meet ISO 42001 objectives, organizations are encouraged to leverage established best practices from complementary standards and guidance frameworks.
Commonly used AI risk management methods include:
- STRIDE: Monitor risk indicators such as Spoofing (S), Tampering (T), Repudiation (R), Information Disclosure (ID), and Elevation of Privilege (E) across AI tools and systems.
- DREAD: Assess AI risk impact based on Damage potential (D), Reproducibility (R), Exploitability (E), Affected users (A), and Discoverability (D) to prioritize mitigations.
- OWASP: Apply the Open Worldwide Application Security Project’s analytical framework for ML systems, identifying vulnerabilities, threats, and risk factors.
These methods are most effective when used together and complemented by customized assessment criteria specific to your AI tools, systems, and organizational context. Partnering with an advisor can help define how to assess AI risks, including measurement techniques and logistical considerations such as assessment cadence.
Effective AI risk management under ISO 42001 involves:
- Understanding the AI risk environment.
- Implementing ISO 42001 or other controls to mitigate risks.
- Assessing risk indicators and impacts across systems.
Sources of AI System Risks in ISO 42001
Effective AI risk management under ISO 42001 focuses on comprehensive risk assessment rather than specific mitigation methods. Organizations must identify and address all potential sources of AI risk to ensure thorough coverage.
Annex C of ISO 42001 outlines the primary sources of AI system risks:
- C.3.1: Environment complexity – The volume, diversity, and sensitivity of the IT ecosystem where AI tools and systems are developed, deployed, and managed.
- C.3.2: Lack of transparency : The degree to which stakeholders can view AIMS operations, including inputs, processing, outputs, and storage.
- C.3.3: Level of automation: How extensively AI tools automate tasks and processes.
- C.3.4: Machine learning risks: Risks inherent in ML training data, training approaches, and controls applied to guide AI behavior.
- C.3.5: System hardware issues: Vulnerabilities associated with the AI ecosystem’s hardware components.
- C.3.6: System life cycle issues: Risks tied to specific stages of AI tools’ and AIMS’ lifecycle, from inception to retirement.
- C.3.7: Technology readiness: Vulnerabilities related to users’ familiarity with AIMS systems and processes.
In addition to these standard sources, organizations should document and secure any unique or niche risks specific to their AI ecosystem that may not be captured in Annex
Other AI Risk Management Considerations
Organizations looking to implement ISO 42001 effectively can benefit from insights in related standards and frameworks:
- ISO 31000 series: Provides general risk management controls and best practices applicable across industries, helping organizations align AI governance with broader enterprise risk strategies.
- ISO 42005:2025: A new framework that specifies AI system impact assessments, supporting ISO 42001 requirement 8.4 and offering guidance on evaluating AI-related risks.
- NIST AI Risk Management Framework (RMF): Particularly relevant in the U.S., the NIST AI RMF emphasizes centralized governance while also focusing on mapping, measuring, and managing AI risks, ensuring visibility, accurate tracking, and effective control implementation.
Leveraging these frameworks alongside ISO 42001 helps organizations strengthen AI risk management, improve compliance, and establish a structured approach to governance across AI systems.
AI Risk Management Throughout the AI Lifecycle
A critical aspect of ISO 42001 compliance is managing AI risks across the entire lifecycle of AI tools and systems. While ISO 42001 focuses on AI governance and risk management, ISO/IEC 22989:2022 provides detailed guidance on lifecycle stages to ensure risks are addressed from inception to retirement.
The AI lifecycle includes the following stages:
- Inception: Preparatory work to define goals, objectives, and logistics for AI deployment.
- Design: Developing system architecture, workflows, and training models.
- Verification: Validating that AI tools perform as expected and meet objectives or applicable requirements.
- Deployment: Installing AI tools and systems in their intended environments.
- Operation: Operating AIMS while monitoring performance and adjusting processes as needed.
- Validation: Continuously confirming that goals and requirements are being met.
- Re-evaluation: Reviewing whether pre-established goals remain sufficient, adjusting benchmarks as needed for compliance and performance.
- Retirement: Securely winding down and decommissioning AI tools, ensuring proper data deletion and system disposal to prevent residual risks.
Accounting for risks at each stage requires diligence and structured processes. Retirement, in particular, can be challenging, as many organizations default to haphazard software deletion. With AI, it is critical to implement secure end-of-life procedures to prevent abandoned AI tools from creating unforeseen vulnerabilities.
ISO 42001 Certification and AI Compliance
Achieving ISO 42001 certification requires careful planning, attention to detail, and patience. Organizations must implement all required controls and continuously assess their effectiveness. Partnering with an ISO 42001 consulting and certification provider simplifies the process. Advisors typically start with a gap assessment to determine readiness, then create a custom roadmap for development, deployment, and ongoing maintenance.
Full certification also requires a third-party audit through an accredited auditor, so even highly skilled internal IT teams cannot achieve certification alone.
Organizations should also consider how AI governance intersects with other regulations. For businesses operating across multiple industries or regions, AI controls may need to satisfy several rulesets simultaneously, reducing the risk of non-compliance. Examples include:
- European Union GDPR: Protects personal data and extends to AI systems handling personal information.
- EU AI Act: Introduces additional requirements for AI systems, complementing GDPR.
- Payment Card Industry Data Security Standards (PCI DSS): Ensures AI systems protect cardholder data (CHD).
Working with an advisory organization can streamline compliance across ISO 42001 and other AI-related regulations, minimizing duplication, reducing risk, and improving operational efficiency.
Optimize Your AI Risk Management with ISO 42001
AI can drive remarkable efficiency by automating repetitive tasks and generating high-quality outputs, but it also introduces security and compliance risks. Organizations leveraging AI must implement strong governance aligned with ISO 42001 and other relevant frameworks.
RSI Security has helped organizations across industries achieve compliance with ISO and NIST frameworks, ensuring AI systems are secure, efficient, and auditable. By establishing disciplined AI governance upfront, organizations gain greater operational freedom and reduced risk down the line.
To learn more about our ISO/IEC 42001:2023 compliance services and how RSI Security can help your organization manage AI risks, contact us today.
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