Topic starter
06/11/2026 6:40 pm
TL;DR: Most organisations treat responsible AI as a priority but still struggle to convert ethics into operational controls, leaving accountability fragmented and governance uneven, according to Daon. Its ISO/IEC 42001 certification frames AI trust as a lifecycle management problem, not a policy statement, and that matters most where AI supports high-stakes identity verification.
Editorial analysis by NHI Mgmt Group, based on content published by Daon: “Why ISO 42001 Matters for the Future of Trusted Technology”.
Key questions
Q: How should teams govern identity data when AI systems consume it directly?
A: Teams should govern identity data the same way they govern business-critical metrics: define authoritative terms, map them to live sources, and ensure every consuming system uses the same meaning.
Q: Why do AI governance programmes fail when security and advisory ownership is split?
A: They fail because no single team owns the full decision chain from risk identification to remediation and evidence retention.
Q: What is the difference between AI policy and AI governance?
A: AI policy states what the organization wants to allow, while AI governance enforces how those rules work in practice through ownership, access control, logging, and review.
Practitioner guidance
- Define an AI management system scope Set the boundaries for where AI is governed, who owns each decision domain, and which use cases fall inside the control model.
- Assign accountability across the AI lifecycle Name accountable owners for design, training, deployment, monitoring, and retirement so responsibility does not collapse into a single team.
- Tie identity use cases to reviewable controls Require biometric and verification workflows to carry documented risk assessments, monitoring points, and change approvals before release.
Bottom line: ISO/IEC 42001 is best understood as a control framework for making AI governance repeatable, auditable, and lifecycle-based.
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06/11/2026 6:42 pm
ISO/IEC 42001 matters because it converts AI governance from principle into operating discipline. Organisations often say they want responsible AI, but that statement has little value unless it is tied to ownership, monitoring, review, and corrective action. The standard gives AI programmes a structure that can be audited and repeated, which is what turns trust into something operationally defensible.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How can organisations monitor AI trustworthiness after deployment?
A: Organisations monitor AI trustworthiness after deployment by tracking model performance, data quality, drift, and policy breaches against defined acceptance thresholds. Practical controls include continuous validation, logged prompts and outputs, human review for high-impact decisions, access controls on training and inference data, and rollback or disablement when behaviour departs from approved bounds.
👉 Read our full editorial: ISO/IEC 42001 turns AI trust into a governed control model