AI is helping revolutionize risk management

AI isn’t just evolving risk management — it’s helping reengineering it. Artificial intelligence (AI), including generative AI (Gen AI) and agentic AI, is driving a seismic shift in how organizations anticipate, assess, and act on risk. The old playbook of manual processes, backward-looking assessments, and fragmented frameworks is being replaced by intelligent systems that learn, adapt, and act in real time. According to the KPMG Future of Risk Survey, 400 executives rank AI and Gen AI as by far the most popular type of technologies for managing additional risk responsibilities in the next three to five years.

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The five stages of AI maturity in risk management

Findings from our research

Risk identification

AI can generate process flows, detect emerging risks, and recommend mappings to risk taxonomies, processes, and controls. This helps organizations identify risks more accurately and earlier.

Risk assessment

AI can recommend risk ratings, generate and monitor key risk indicators, and calculate residual risk. By making risk assessment more probabilistic, AI can enhance the precision and consistency of risk ratings.

Risk mitigation

AI tools support decision making around risk response strategies and automate or optimize mitigation actions. For instance, they can identify issues and root causes, review and design control inventories, and monitor alerts more efficiently and precisely.

Risk monitoring

AI enables real-time or continuous monitoring of risk indicators, producing aggregated reporting and moving from point-in-time reporting to more dynamic, real-time capabilities.

Risk review and reporting

AI can help improve the efficiency and quality of risk reporting by automating the generation of reports, thematic analysis, and standardized risk and control report outputs.

Testing and validation

AI can automate control testing activities, validate control effectiveness, and detect anomalies across large data sets. By continuously learning from historical patterns and outcomes, AI enhances the accuracy, efficiency, and coverage of testing activities — helping reduce manual effort and enabling faster identification of control weaknesses.