AI Risk Management

AI Risk Management identifies, assesses, mitigates, and monitors risks specific to AI systems across the lifecycle. Risk categories include AI-specific concerns (bias, hallucination, prompt injection, model drift, adversarial attacks) alongside familiar risks (data breach, vendor failure, regulatory non-compliance, operational disruption). The NIST AI Risk Management Framework, ISO/IEC 23894, and ISO/IEC 42001 provide structured approaches. Practical AI risk management includes risk registers, regular risk assessments, mitigation tracking, key risk indicators, and integration with broader enterprise risk management. Tools include GRC platforms (Archer, OneTrust, ServiceNow GRC), AI governance platforms (including Centralpoint), and specialized AI risk solutions from major consulting firms. AI risk management is increasingly governed by executive AI risk committees that report up to boards. As AI becomes core to business operations, AI risk management has evolved from a niche concern to a board-level discipline informing AI compliance, AI governance, and responsible AI strategy at every major enterprise.

Centralpoint Powers Operational AI Risk Management: Oxcyon's Centralpoint AI Governance Platform delivers the metering, audit logs, and policy enforcement risk management requires — across OpenAI, Gemini, Llama, and embedded models. Centralpoint keeps prompts and skills on-prem and embeds risk-managed chatbots into your portals via one JavaScript line.


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