AI Risk Management

AI risk management applies familiar risk practice to AI-specific concerns, such as harmful or biased outputs, privacy, security, reliability, transparency and accountability. It considers the whole lifecycle, including data, models, deployment and ongoing use, and it involves people from technical, legal, privacy, security and business functions. Frameworks and standards offer structure, for example ISO/IEC 42001 for management systems and guidance such as the NIST AI Risk Management Framework. Organisations choose and adapt what fits their context. Example: A bank plans to use an AI model to help screen loan applications. Risk management identifies bias, explainability and data quality risks, assigns owners, sets testing and human review requirements and monitors outcomes after launch. Managing AI risk helps organisations adopt AI with defined accountability instead of avoiding it or using it blindly.