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ISO/IEC 42001 · ISO/IEC 23894 · Regulation (EU) 2024/1689
AI Governance with evidence: policies, controls, and accountability
Governing AI is not writing a policy: it is implementing roles, controls, and accountability with auditable records. Design and audit of AI management systems under ISO/IEC 42001.
Lead Auditor ISO/IEC 42001AIMS ImplementerISO/IEC 23894 Risk Management
6Governance pillars
PDCAContinuous improvement cycle
5Implementation phases
"Governing AI is not writing a policy. It is building an accountability system with evidence."Fernando Arrieta — Lead Auditor ISO/IEC 42001
Why it matters
Why governing AI is not optional
Organizations are adopting AI at market speed but governing it at committee speed. The result: systems without inventory, risks without a map, automated decisions without owners, and Shadow AI out of control.
- 01
Regulatory risk. Regulation (EU) 2024/1689 is already in force. Organizations with high-risk AI systems must demonstrate compliance with documentation, controls, and human oversight.
- 02
Operational risk. Unauthorized use of AI (tools like GPT or Copilot without policy or access control) is an internal threat equivalent to insider threat. Without traceability, there is no accountability.
- 03
Reputational risk. An undetected bias, an opaque decision, or a data leak from generative AI can erode institutional trust. Governance provides the framework to prevent, detect, and respond.
Pillars
Pillars of auditable AI governance
Six dimensions that an AI governance system must cover to be verifiable, not decorative.
AI policy and strategy
Definition of purpose, scope, principles, and limits of AI use in the organization. Alignment with business objectives and risk appetite.
Roles and responsibilities
Clear RACI: who decides, who implements, who oversees, who is accountable. No ambiguity.
Algorithmic risk management
Identification, assessment, and treatment of AI risks: bias, opacity, dependency, impact on rights. ISO/IEC 23894 as guidance.
Human oversight
Oversight mechanisms proportional to risk: human review, override, alerts, and documented escalation.
Transparency and explainability
Records of automated decisions, understandable explanations for those affected, and traceability of algorithmic reasoning.
Continuous improvement (PDCA)
Improvement cycle applied to AI: performance metrics, management review, corrective actions, and lessons learned.
Method
How AI governance is designed
AI inventory
Identify all AI systems in use: proprietary, contracted, Shadow AI. Classify by risk and criticality.
Gap diagnosis
Assess current state against ISO/IEC 42001 requirements and applicable regulatory obligations.
Governance design
Define policy, roles (RACI), controls, metrics, and human oversight procedures.
Implementation
Deploy controls, train teams, document evidence, and activate continuous monitoring.
Audit and improvement
Verify conformity, measure performance, correct findings, and feed the PDCA cycle.
FAQ
AI Governance: what leadership usually asks
What is AI governance?
It is the system of policies, roles, controls, and processes that an organization establishes to manage the responsible use of artificial intelligence. It is not a document: it is an operating system of accountability with documented traceability.
What is the difference between AI governance and AI regulation?
Regulation (such as EU Regulation 2024/1689) establishes external legal obligations. Governance is the internal system the organization implements to meet those obligations and manage its own risks. The audit verifies both dimensions.
What frameworks exist for AI governance?
The main ones are ISO/IEC 42001 (AI management system), ISO/IEC 23894 (AI risk management), NIST AI RMF, and Regulation (EU) 2024/1689. The choice depends on the regulatory, sectoral, and organizational maturity context.
Where to start if we have nothing?
With a gap diagnosis against ISO/IEC 42001. The current governance state is assessed, AI systems in use (including Shadow AI) are identified, risks are mapped, and a prioritized roadmap is defined.
Does AI governance require banning AI use?
No. Governing AI is not about banning it — it is about using it with verifiable control. Governance defines which uses are authorized, which controls apply, who oversees, and how accountability is rendered. The goal is AI with evidence, not AI without limits.
Acreditaciones y membresías institucionales
Evidence-based AI governance starts with a clear conversation.
If your organization is designing or strengthening its AI governance framework, this is the channel to discuss scope and approach. All inquiries are handled under confidentiality.
The consulting and implementation services described on this site are provided independently. Certification audits and decisions are the exclusive responsibility of accredited certification bodies. In accordance with ISO/IEC 17021-1 §5.2, impartiality restrictions and cooling-off periods apply.