Model Risk

Model Risk is the potential for adverse outcomes from errors, limitations, or misuse of AI and machine-learning models. The discipline originated in financial services with the Federal Reserve's SR 11-7 guidance on Model Risk Management, which has shaped governance practices across banks since 2011. The OCC's Model Risk Management Handbook reinforces these expectations. Common model risks include inaccurate predictions, model bias, instability under data shift, overfitting to historical patterns, and misuse outside intended scope. SR 11-7 requires banks to maintain model inventories, conduct independent validation, monitor performance, and document everything. The framework has been adapted to AI/ML and is now widely adopted beyond finance — including in healthcare, insurance, energy, and government. AI governance, AI compliance, and AI risk management programs in regulated industries treat model risk management as foundational responsible AI infrastructure, supporting enterprise AI deployments by tying technical controls to governance accountability at every level.

Centralpoint Brings SR 11-7-Grade Discipline to All Models: Oxcyon's Centralpoint AI Governance Platform applies model risk management across OpenAI, Gemini, Llama, and embedded models. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds risk-controlled chatbots into your portals via a single line of JavaScript.


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