Partnerships

AI Risk

Management

Identify, assess, and mitigate AI-specific risks—including high-risk use cases, bias, opacity, and safety issues—before they become regulatory, operational, or reputational incidents.

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Managing AI Risk Across the Lifecycle

AI introduces risks that extend beyond traditional IT risk, including discrimination, lack of explainability, automation bias, and systemic impact. Regulators increasingly require proactive identification and mitigation of these risks.

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AI Governance Frameworks

Regulatory Expectations

Common expectations across emerging AI rules and standards include:

•• Conducting AI risk assessments prior to deployment

•• Classifying AI systems by risk level (e.g., minimal, limited, high)

•• Implementing documented mitigation measures

•• Monitoring AI performance and impact over time

•• Reassessing risk after significant model changes or new use cases

Key AI Risk Areas

•• Bias and discrimination in decisions affecting individuals

•• Transparency and explainability failures that undermine trust

•• Automation bias and over-reliance on AI outputs

•• Safety and robustness issues leading to harmful outcomes

•• Security and misuse risks, including prompt injection and model abuse

Our Risk Management Approach

•• AI risk mapping and system inventories

•• High-risk AI classification and justification

•• Design and implementation of mitigation controls

•• Continuous monitoring and review processes

•• Alignment with international AI risk frameworks and standards

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