AI risk governance

AI risk governance is the organizational practice of systematically identifying, assessing, prioritizing, and managing the risks introduced by AI systems — including model failures, data quality issues, misuse, bias, and autonomous action risks — within the broader enterprise risk management structure.

What AI risk governance covers

AI risk governance addresses the full range of risks AI systems introduce. Technical risks include model errors, hallucination, data drift, and adversarial inputs. Operational risks include AI systems that fail in production in ways not caught during testing, dependencies on third-party model providers, and loss of institutional knowledge when AI automates decisions previously made by humans. Compliance risks include regulatory requirements around AI use in consequential decisions — lending, hiring, healthcare, law enforcement — and the legal liability exposure from incorrect AI outputs. Reputational risks include the harm to public trust that follows a visible AI failure. Risk governance maps these categories to the organization's specific AI deployments and implements controls proportionate to the assessed risk level.

How AI risk governance relates to enterprise risk management

AI risk governance is a specialization of enterprise risk management (ERM), not a separate discipline. It brings AI-specific risk categories and assessment methods into the organization's existing risk framework. This integration matters for practical reasons: AI risks often interact with existing risk categories — an AI failure in a customer-facing system is also an operational risk and a reputational risk — and governance mechanisms like risk registers, escalation paths, and board reporting work best when AI risks use the same language and processes as other enterprise risks. Organizations that build AI risk governance as a separate silo often find it disconnected from the decisions that actually determine how AI systems are deployed.

Risk assessment for AI deployments

Risk assessment for a specific AI deployment evaluates: the potential impact of failures (on customers, employees, third parties, or the organization itself), the probability of those failures given the system's design and operating environment, the reversibility of adverse outcomes, and the effectiveness of controls in place to prevent or mitigate them. High-impact, low-reversibility applications — automated lending decisions, medical diagnostic support, autonomous actions with financial consequences — warrant the most rigorous assessment. Lower-stakes uses may warrant lighter-touch evaluation. The assessment should be repeated when the AI system changes substantially, when its operating environment changes, or on a defined review cadence.

AI risk governance — FAQ

What is a risk register for AI, and what should it contain?

An AI risk register is a structured record of identified risks associated with the organization's AI systems. Each entry typically includes: the AI system or use case the risk relates to, a description of the risk, the potential impact and estimated likelihood, the risk rating or priority, the controls in place, the residual risk after controls, and the owner responsible for managing the risk. The register should be a living document updated as systems change and new risks emerge, not a one-time compliance exercise.

How does AI risk governance differ from AI safety?

AI safety in the technical sense focuses on ensuring AI systems behave according to human intent and do not cause catastrophic harm, particularly in highly capable systems. AI risk governance is the organizational practice of identifying and managing the practical risks current AI systems introduce in business contexts. They share the goal of preventing AI-caused harm but operate at different scopes — safety research focuses on long-term and large-scale risks; risk governance focuses on current deployments in organizational settings.