AI Risk Management

AI risk management systematically identifies, assesses, and mitigates risks unique to artificial intelligence systems throughout their lifecycle.

AI risk management addresses both traditional IT risks and AI-specific risks like bias, drift, and adversarial attacks.

AI-specific risks: - Model Bias: Unfair outputs based on training data - Model Drift: Performance degradation over time - Adversarial Attacks: Inputs designed to fool models - Data Poisoning: Malicious training data - Privacy Leakage: Models memorizing sensitive data - Opacity: Inability to explain decisions

NIST AI RMF framework: 1. Govern: Establish accountability 2. Map: Understand context and risks 3. Measure: Assess risks quantitatively 4. Manage: Implement mitigations

Lifecycle considerations: - Data sourcing and quality - Model development and testing - Deployment and monitoring - Retirement and disposal

Why It Matters

AI systems that lack proper risk management can produce biased decisions, leak sensitive data, or fail silently in production—creating legal, reputational, and financial exposure. The NIST AI RMF provides a structured approach to identifying and mitigating these risks. As AI regulation accelerates globally, organizations that implement AI risk management now will be prepared for compliance requirements rather than scrambling to retrofit governance later.

Key Points

NIST AI RMF is the leading framework

Must address bias throughout lifecycle

Model monitoring detects drift and attacks

Documentation is critical for auditability

Third-party AI introduces supply chain risk

Applicable Compliance Frameworks

ISO 27001

Related Terms

AI Governance

AI governance is the framework of policies, processes, and controls that ensure AI systems are developed and used responsibly, ethically, and in compliance with regulations.

Risk Assessment

A risk assessment is a systematic process of identifying, analyzing, and evaluating potential threats to an organization's information assets.

LLM Security

LLM security addresses the unique risks of deploying Large Language Models, including prompt injection, data leakage, and adversarial attacks on AI systems.