How to Assess and Manage AI-Related Risks
Kovrr, Wednesday, July 15th, 2026
This guide walks through the full lifecycle of AI risk assessment and management, from initial discovery through financial quantification and board reporting, with practical guidance on how to build a program that scales.
Organizations assess and manage AI-related risks by establishing a cross-functional governance framework, mapping risks based on impact and financial likelihood, and instituting continuous monitoring that connects AI asset discovery to risk quantification, compliance, and enforcement. The most effective programs treat AI risk management not as a one-time assessment but as a continuous, data-driven discipline that evolves alongside the AI systems it governs.
The standard frameworks for AI risk management, including the NIST AI Risk Management Framework, the EU AI Act, and ISO 42001, all converge on a similar set of principles: identify AI systems in use, assess their risks, implement controls, monitor performance, and report to stakeholders. Where organizations diverge is in how deeply they operationalize those principles. The difference between a mature AI risk program and a compliance checkbox exercise comes down to whether the assessment process produces actionable, quantified insights or just documentation.