AI Risk Management is the ongoing process of identifying, assessing, and mitigating the potential adverse impacts of artificial intelligence systems on individuals, organizations, and society.

Unlike traditional IT risk management, which focuses primarily on cybersecurity (e.g., preventing unauthorized access), AI risk management must address socio-technical risks. These include probabilistic failures where the system works "as intended" technically but produces harmful outcomes, such as biased hiring decisions, dangerous medical advice, or the generation of disinformation.

Effective AI Risk Management is typically structured around frameworks like the NIST AI RMF or ISO 23894, involving four key functions:

• Govern: Establishing a culture of risk awareness, clear policies, and accountability structures (e.g., an AI Ethics Board).
• Map: Contextualizing the risks by understanding the system's intended purpose, the data it uses, and the stakeholders it affects.
• Measure: Quantifying risks using diverse metrics, not just accuracy, but also fairness, robustness, and explainability.
• Manage: Implementing controls to treat the identified risks (e.g., human-in-the-loop reviews, red-teaming exercises, or deciding not to deploy a high-risk system).

Strategic Impact: Proactive AI risk management is a competitive differentiator. Organizations that effectively manage AI risk can deploy models faster and with greater confidence, avoiding the "analysis paralysis" that comes from fear of unknown liabilities. Conversely, failure to manage these risks can lead to regulatory fines, class-action lawsuits, and catastrophic loss of consumer trust.