AI Lifecycle Risk Management: ISO/IEC 42001:2023 for AI Governance

Digital Team
Sep 6
4 min read

Why AI Lifecycle Risk Management Matters

Artificial Intelligence (AI) is no longer just an emerging technology—it is now central to how governments, businesses, and organisations operate. From healthcare and finance to public services and education, AI systems shape decisions that affect individuals and entire societies.

With this influence comes significant responsibility.

The challenge is clear: how can organisations ensure AI systems remain ethical, secure, and compliant throughout their lifecycle? This is where AI lifecycle risk management and the international standard ISO/IEC 42001:2023 for AI governance come in.

This article explains the purpose of ISO/IEC 42001, the importance of lifecycle governance, and how threat modelling, impact assessments, and continuous monitoring together create trustworthy AI. It also highlights practical tools, frameworks, and recommendations for embedding responsible AI governance.

What Is AI Governance?

AI governance refers to the rules, policies, and controls that ensure AI systems are designed and used responsibly. It covers the entire AI lifecycle—from concept through retirement.

Key governance activities include:

Defining AI’s purpose and aligning with stakeholders.
Managing risks in data, models, and deployment.
Designing for transparency, fairness, and accountability.
Monitoring system performance and planning for decommissioning.

ISO/IEC 42001 provides a structured framework to help organisations put these practices in place, making sure AI use is safe, ethical, and compliant with regulations.

The AI Lifecycle: Understanding Risk at Every Stage

AI risk doesn’t stop once a system is launched. Instead, risks evolve with the system, making lifecycle management critical. ISO/IEC 22989:2022 outlines the stages of an AI system’s lifecycle, which include:

Inception – defining needs, goals, and feasibility.
Design and development – creating models, setting architecture, and managing data flows.
Verification and validation – testing system accuracy and performance.
Deployment – releasing into the operational environment.
Operation and monitoring – observing performance, logging data, and detecting risks.
Re-evaluation – assessing if objectives are still being met.
Retirement – decommissioning and managing residual risks.

Each stage carries unique risks—such as spoofing in inception, tampering during development, or data disclosure during deployment. ISO/IEC 42001 guides organisations on how to manage these risks step by step.

Risk Management in ISO/IEC 42001:2023

At the heart of ISO/IEC 42001:2023 is risk management. The standard requires organisations to:

Identify and assess risks (Clause 6.1).
Apply operational controls (Clause 8.2).
Continuously monitor and improve processes (Clauses 9 and 10).

For high-risk AI systems, organisations must also conduct AI Impact Assessments (AIIAs). These are similar to data protection impact assessments (DPIAs) but go further by examining ethical, societal, and legal risks.

Two major frameworks often used alongside ISO/IEC 42001 include:

ISO 31000 – a general risk management standard for embedding AI risk into enterprise-wide governance.
NIST AI Risk Management Framework (AI RMF) – focused specifically on AI, covering fairness, robustness, explainability, and accountability.

By combining ISO/IEC 42001 with these frameworks, organisations can achieve a balanced and structured approach to AI risk governance.

Threat Modelling for AI Risk Identification

While risk assessments provide a broad view, threat modelling drills into technical vulnerabilities. This makes it essential for AI lifecycle governance.

Popular frameworks include:

STRIDE – assesses risks like spoofing, tampering, and denial of service.
DREAD – measures severity by impact, exploitability, and likelihood.
OWASP for Machine Learning – identifies adversarial and privacy threats.
MITRE ATLAS – focuses on adversarial AI risks.
LINDDUN – addresses privacy-specific concerns.

For example, STRIDE helps uncover risks across the lifecycle: fake identities in inception, tampering in development, and denial-of-service in deployment. Mapping these risks to ISO/IEC 42001 controls ensures every vulnerability is addressed.

Conducting AI Impact Assessments (AIIAs)

AIIAs are critical for high-risk AI applications such as healthcare or finance. They answer vital questions:

Is the AI system ethical and proportionate?
Could it cause bias, discrimination, or exclusion?
What safeguards are needed for people affected?

An AIIA typically includes:

Purpose and scope of the system.
Stakeholder and impact mapping.
Ethical, legal, and social risk analysis.
Recommendations and mitigation steps.

This structured process ensures that risks to individuals, groups, and society are clearly identified and responsibly managed.

Mapping AI Risks to ISO/IEC 42001 Controls

ISO/IEC 42001 goes further by linking risks directly to controls in Annex A. Examples include:

Inception (spoofing risks): Controls A.6.1 and A.5.1.
Design and development (tampering): Controls A.8.2 and A.9.1.
Verification (repudiation): Controls A.8.5 and A.7.1.
Deployment (privilege escalation): Controls A.10.2 and A.6.1.
Operation (denial of service): Controls A.8.3 and A.10.3.

This mapping ensures risks identified in the lifecycle are matched with specific safeguards, giving organisations practical ways to operationalise governance.

Maintaining AI Governance Over Time

AI governance isn’t a one-off project—it must be continuous. ISO/IEC 42001 requires:

Regular reviews and updates to policies.
Annual AIIAs and threat modelling exercises.
Internal and external audits.
Leadership oversight with transparent reporting.

This ongoing approach ensures AI systems remain resilient, fair, and aligned with evolving regulations and societal expectations.

Conclusion: Building Trustworthy AI with ISO/IEC 42001

AI lifecycle risk management is not just about compliance—it’s about building trustworthy and resilient AI systems that support innovation without harming individuals or society.

By adopting ISO/IEC 42001:2023, organisations can:

Align AI governance with international best practices.
Manage risks across the entire lifecycle.
Integrate ethical, legal, and technical safeguards.
Build public trust through transparency and accountability.

Recommendation: Organisations should embed ISO/IEC 42001 into their governance processes, conduct regular threat modelling and AIIAs, and ensure leadership oversight at every stage.

For more insights on AI governance, digital transformation, and risk management, subscribe to future articles from George James Consulting at www.Georgejamesconsulting.com