AI risk management is the ongoing process of identifying, assessing, mitigating, and monitoring risks that arise across the AI lifecycle.
AI risk management applies established risk management principles to challenges specific to artificial intelligence, such as bias, model drift, security threats, privacy concerns, and misuse. It sits within a broader AI governance framework, which establishes the policies, responsibilities, and oversight for responsible AI, while AI risk management focuses on identifying and treating specific risks. An AI risk management policy can formalize how those risks are assessed, managed, monitored, and assigned to owners.
An effective AI risk management framework should cover the entire AI lifecycle rather than relying on a one-time assessment before deployment. This includes traditional machine learning and generative AI, as well as increasingly autonomous or agentic AI systems where actions may occur with less direct human oversight.
AI systems can introduce security, privacy, compliance, and operational risks. These risks can evolve as models interact with new data, users, and use cases. Effective risk mitigation starts with understanding the main areas of exposure:
Managing AI risk requires a structured process before and after deployment. A clear risk management strategy helps teams identify risks early, assign ownership, implement controls, and respond as risks change. The process can be organized into four key stages:
Before deployment, use an AI risk assessment framework to evaluate the system’s intended use, data, security concerns, affected stakeholders, and applicable requirements. Identify risks such as bias, privacy exposure, misuse, or inaccurate outputs and assess their likelihood and potential impact. This helps determine which controls are needed before the system goes live.
Define appropriate controls and assign clear responsibility for each identified risk. Risk management platforms can help teams centralize this information and track it over time.
Track model drift, performance issues, misuse, security threats, and other emerging risks. When risk levels change, controls and mitigation measures should be reviewed and updated accordingly.
Maintain records of key risks, controls, mitigation actions, decisions, and supporting evidence for each AI system. This creates an audit trail and gives stakeholders greater visibility into the organization’s AI risk posture.
Organizations may need to consider several frameworks, standards, and regulations when managing AI risk. Which requirements apply depends on factors such as where the organization operates, how AI is used, and the type of data or systems involved. Here are the main frameworks and regulations shaping AI risk management:
The NIST AI Risk Management Framework (AI RMF) is a voluntary framework designed to help organizations address AI-related risks throughout the lifecycle. It is structured around four core functions: Govern, Map, Measure, and Manage. Together, these functions provide a structured approach to understanding, assessing, and managing AI risks throughout the lifecycle.
ISO/IEC 42001 is an international standard for establishing, implementing, maintaining, and continually improving an AI management system. It provides a structured approach for managing AI risks, responsibilities, policies, and processes across an organization. Unlike the NIST AI RMF, organizations can pursue certification against ISO 42001.
The EU AI Act establishes mandatory requirements for AI systems based on their risk classification and intended use. Requirements can include risk management, documentation, testing, transparency, and human oversight, depending on the system. Understanding the applicable EU AI Act risk categories is therefore an important part of AI risk management and compliance.
GDPR applies when AI systems collect, use, or otherwise process personal data belonging to individuals in the EU or EEA. Organizations need to consider requirements around lawful processing, data minimization, transparency, data subject rights, and safeguards for automated decision-making. These requirements make privacy and data protection an important part of AI risk management throughout the AI lifecycle.
Scytale’s AI GRC platform helps organizations manage AI risks, controls, and compliance requirements in one centralized environment. Teams can maintain an AI risk register, map risks and controls to frameworks such as ISO 42001 and the EU AI Act, and streamline evidence collection and ongoing monitoring as requirements evolve.
This gives compliance, security, risk, and technical teams a shared view of AI systems and their compliance status. By replacing scattered spreadsheets and manual tracking with a structured process, Scytale makes it easier to maintain oversight, demonstrate compliance, and stay audit-ready as AI use grows.
