Source: The National Law Review

The National Law Review, in the second part of its series on AI governance, provides a practical guide for organizations on how to effectively map their AI risk landscape. The article stresses that as businesses increasingly adopt AI technologies, they must move beyond ad-hoc management to a structured and comprehensive governance framework. The foundational step in this process is identifying and understanding the full spectrum of risks associated with AI, which extends beyond the commonly discussed issues of data privacy and algorithmic bias to include areas like intellectual property infringement, security vulnerabilities, and "hallucination" or performance-related risks.

The authors outline a systematic approach to risk mapping, which involves creating an inventory of all AI systems in use, assessing the potential impact and likelihood of various risks for each system, and aligning them with the organization's overall risk tolerance. This process should involve stakeholders from multiple departments, including legal, compliance, IT, and business units, to ensure a holistic view. The ultimate goal of this mapping exercise is not merely to identify risks, but to inform the development of appropriate controls, policies, and oversight mechanisms. A well-defined risk map is presented as an essential tool for ensuring responsible AI deployment, demonstrating compliance with emerging regulations, and making informed decisions about technology adoption.