What makes AI risk management different from traditional IT risk?

AI risk management differs fundamentally from traditional IT risk management due to the unique characteristics of AI systems. Traditional IT risks involve relatively predictable failure modes: servers crash, networks fail, software has bugs. These risks can be managed through redundancy, testing, and well-established controls. AI systems introduce different risk profiles that require different management approaches.

AI models are probabilistic, not deterministic. They don't execute fixed logic; they make predictions based on learned patterns. This means AI systems can fail in subtle, context-dependent ways that are difficult to anticipate. A model that performs well in testing may degrade in production as data distributions shift, introducing model drift that traditional monitoring doesn't detect.

Bias and fairness risks are unique to AI. Training data can embed historical biases that lead to discriminatory outcomes even when protected attributes are excluded. These risks require specialized assessment methods including fairness metrics, bias testing, and demographic parity analysis that don't exist in traditional IT risk frameworks.

AI systems have opaque decision-making processes, especially deep learning models. This opacity creates explainability and accountability challenges. When an AI system denies a loan or flags a transaction, understanding why is often difficult, complicating compliance, debugging, and stakeholder trust.

Finally, AI risks evolve continuously. Adversaries develop new attacks targeting model vulnerabilities, regulations change, societal expectations shift, and AI capabilities advance. Risk management must be adaptive, not static, with continuous monitoring and periodic reassessment built into the framework.