AI Governance
AI governance is the policies, processes, and controls to manage AI risk, performance, and compliance across the AI lifecycle, enabling trustworthy, auditable systems.
What is AI Governance?
AI governance is a management framework that sets roles, controls, and documentation for how AI is built, evaluated, deployed, and monitored. It typically includes model inventories, risk assessments, human oversight, and incident response. Teams use it to align AI with business objectives, ethics, and regulations. Programs connect to adjacent practices like AI Risk Management and AI Conformity Assessment and often complement privacy tooling such as a Data Protection Impact Assessment.
Gartner defines AI governance as the process of assigning and assuring accountability, decision rights, risk management, policies, and investment decisions for applying artificial intelligence.
Why AI Governance Matters
AI governance helps executives and product teams ship AI responsibly by clarifying ownership, enforcing controls, and capturing evidence. It improves efficiency with standardized workflows, reduces operational surprises, and builds stakeholder trust with transparent decision-making and measurable guardrails. Frameworks such as the EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001, and OECD AI Principles emphasize risk-based controls, transparency, data quality, human oversight, and post-market monitoring. Strong governance reduces enforcement exposure, supports better UX with explainable decisions, and protects brand equity by preventing unfair, insecure, or unreliable model behavior.
How AI Governance is Used in Practice
- Organizations typically stand up a model of registry and inventory linking each system to owners, risks, datasets, and evaluations.
- Programs often include pre-deployment risk assessments with documented testing for bias, robustness, and security, plus human review and sign-off.
- Monitoring and logging are commonly used to track drift, performance, incidents, and user feedback; trigger retraining or rollback.
- Controls are often localized (e.g., consent, transparency notices, record-keeping) to meet EU, UK, and US requirements.
- Third‑party and vendor models are commonly assessed, capturing attestations and evidence for audits and procurement.
Related Laws & Standards
- EU Artificial Intelligence Act (EU AI Act)
- NIST AI Risk Management Framework (NIST AI RMF)
- OECD AI Principles
- ISO/IEC 42001:2023 (AI Management System Standard)
- EU General Data Protection Regulation (GDPR)
How OneTrust Helps with AI Governance
OneTrust provides AI Governance software to catalog AI and assess risk, monitor posture across platforms, and programmatically enforce controls. Aligning enterprise governance with technical reality, teams can scale faster, reduce risk, and maintain trust.
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FAQs about AI Governance
AI governance defines the overall policies, roles, and controls for AI programs; AI risk management focuses on identifying, assessing, and treating specific risks across the lifecycle. They work together.
Ownership typically spans product/engineering, data science, legal/privacy, and security. A central AI governance council or program lead coordinates standards; the DPO may be involved where privacy risks arise.
It structures conformity tasks: maintaining model inventories, running risk and bias assessments, ensuring human oversight, recording testing results, and enabling post-market monitoring and incident reporting.
