ISO/IEC 23894:2023 - AI Risk Management

Summary

ISO/IEC 23894:2023 bridges the gap between traditional enterprise risk management and the unique challenges of AI systems. This standard takes the proven ISO 31000 risk management framework and extends it specifically for AI contexts, addressing risks that simply don't exist in conventional IT systems - from algorithmic bias and model drift to societal impact and AI explainability. Unlike generic risk frameworks, this standard provides concrete guidance for identifying, assessing, and mitigating risks throughout the entire AI lifecycle, from initial concept through deployment and ongoing operations.

Who this resource is for

Primary audience:

• Risk managers and chief risk officers implementing AI governance programs
• AI system developers and ML engineers who need structured risk assessment approaches
• Compliance teams ensuring AI systems meet regulatory requirements
• Product managers overseeing AI-enabled products and services Also valuable for:
• Internal auditors evaluating AI risk controls
• Legal teams assessing AI liability and accountability measures
• Executive leadership seeking board-level AI risk oversight frameworks
• Consultants advising organizations on AI governance implementation

What makes this different from traditional risk management

ISO/IEC 23894 recognizes that AI systems create fundamentally new risk categories that don't map neatly onto traditional IT risk frameworks:

AI-specific risk domains covered: Algorithmic bias and fairness

• Beyond data quality issues to systematic discrimination Transparency and explainability
• Risks from "black box" decision-making processes Model performance degradation
• How AI systems can silently fail over time Societal and ethical impact
• Broader consequences of AI deployment at scale Human-AI interaction risks
• Over-reliance, skill atrophy, and trust calibration issues

The standard also addresses temporal aspects unique to AI - risks that emerge during training, deployment, and ongoing operation phases, with specific guidance for continuous monitoring and model governance.

Core implementation components

Risk identification frameworks:

• Pre-built risk taxonomies specific to different AI application domains
• Stakeholder impact assessment templates covering affected communities
• Technical risk checklists for common ML architectures and deployment patterns Assessment methodologies:
• Quantitative approaches for measurable risks (accuracy, bias metrics)
• Qualitative frameworks for societal and ethical considerations
• Combined assessment techniques for complex, interconnected AI risks

Risk treatment strategies:

• Technical controls (model validation, bias testing, performance monitoring)
• Process controls (human oversight, approval workflows, audit trails)
• Governance controls (accountability assignments, escalation procedures)

Getting started with implementation

Phase 1: Risk context establishment (2-4 weeks)

Phase 2: AI risk taxonomy development (4-6 weeks)

Phase 3: Integration with existing processes (6-8 weeks)

Phase 4: Continuous monitoring setup (4-6 weeks)

Relationship to other AI governance standards

• Complements ISO/IEC 42001 (AI Management Systems) by providing detailed risk assessment methodologies that support the management system requirements.
• Aligns with NIST AI RMF governance and risk management functions while offering more prescriptive implementation guidance and assessment techniques.
• Supports regulatory compliance for emerging AI regulations (EU AI Act, etc.) by providing systematic risk assessment evidence and documentation.
• Integrates with ISO 27001 and other information security standards by extending risk assessment techniques to AI-specific security and privacy concerns.