Topic

Audit Standards

Audit standards are formalized procedures and frameworks that establish consistent methodologies for evaluating AI systems, data practices, and governance controls. They matter for AI governance because they provide objective benchmarks for assessing compliance, identifying risks, and demonstrating accountability to regulators, stakeholders, and the public. In enterprise contexts, audit standards ensure that AI implementations are examined systematically across model development, deployment, and ongoing monitoring, helping organizations substantiate claims about safety, fairness, and regulatory adherence.

1 item

ResearchGlobal2026-07-08

Eight Governance Themes from 2025 Reveal Widening Gaps Between Regulatory Ambition and Enterprise Readiness

A year-in-review analysis by AI governance practitioner Oliver Patel identifies eight major governance developments from 2025, including new US state legislation, ISO/IEC 42006 audit standards, and successive EU AI Code of Practice drafts. The review highlights that compliance teams are now operating across an increasingly fragmented regulatory landscape where frontier AI transparency requirements, international audit standards, and state-level disclosure obligations are advancing at uneven speeds. Third-party AI vendor risk programs and model risk governance functions face the most immediate pressure to adapt.

ISO/IEC 42006 EU AI Code of Practice frontier AI US state AI laws audit standards multi-jurisdiction compliance third-party vendor risk regulatory fragmentation