ISO 42001 – Clause 8.2 – AI Risk Assessment

---

Identifying and Managing Risks Unique to AI

Clause 8.2 of ISO/IEC 42001 is the nerve center of AI governance. Unlike traditional IT systems, AI systems introduce new categories of risks — such as bias, lack of explainability, model drift, and unintended outcomes. This clause mandates that organizations proactively assess and manage AI-related risks as part of their operational controls.

What Does Clause 8.2 Require?

Organizations must:

• Identify potential risks arising from the use of AI systems.
• Evaluate those risks using defined criteria — such as likelihood, impact, and affected stakeholders.
• Address risks through appropriate controls, mitigations, or design changes.
• Document and retain evidence of risk assessment and treatment decisions.
• Keep risk assessments up to date — especially when AI models are retrained, redeployed, or exposed to new data.

Importantly, the risk assessment must be context-specific: aligned with the intended use, affected stakeholders, and the lifecycle stage of the AI.

---

Why Is This Clause So Important?

Traditional risk assessments often focus on cybersecurity, financial, or operational risks. But with AI, we’re dealing with:

• Algorithmic bias and fairness

• Explainability gaps in model decisions

• Data poisoning or model manipulation

• Overfitting or underperformance in real-world settings

• Ethical and reputational harm to users or society

Without a tailored AI risk assessment, these threats could remain invisible until they cause damage.

---

🛠️ Implementation Strategy

To comply with Clause 8.2, your organization should:

Step	Actions
Define risk criteria	Include bias impact, stakeholder harm, and explainability alongside traditional factors.
Map AI use cases	Identify where AI is used, how it makes decisions, and what data it consumes.
Use AI-specific tools	Leverage tools like model explainers, fairness audits, and bias detection platforms.
Engage stakeholders	Include ethical, legal, and domain experts in the assessment process.
Document risk treatment	Maintain a clear trail of how risks were identified, analyzed, and mitigated.

Pro Tip: Automate continuous risk monitoring using AI assurance tools integrated into your ML pipeline. For example, Kimova’s TurboAudit offers explainability analysis and control mapping to help track AI risks in real-time.

---

Example Risks You Might Assess

Risk Type	Example
Bias Risk	An AI loan approval system favors certain demographics.
Explainability Risk	A model cannot justify why a medical recommendation was made.
Data Drift	An AI’s accuracy drops after a data source changes.
Misuse	Generative AI is used for malicious content creation.

---

Clause 8.2 ensures that AI doesn’t just work — it works safely, fairly, and ethically. Risk assessments shouldn’t be one-time activities. They need to be iterative, transparent, and aligned with the pace of AI innovation.

In tomorrow’s article by Kimova.AI, we’ll explore Clause 8.3 – AI Risk Treatment, where we dive into how to apply controls and mitigation strategies to address the risks identified in your assessment.

---