AI Risk Management
The process of identifying, assessing, and mitigating risks associated with developing or deploying AI systems — from technical failures to legal exposure and ethical harms.
What Is AI Risk Management?
AI risk management is the systematic process of identifying, assessing, and mitigating risks that arise from using or deploying artificial intelligence. It extends traditional IT risk management to cover AI-specific failure modes — hallucinations, bias, prompt injection, data poisoning, and model drift — as well as the legal, ethical, and reputational risks unique to AI systems.
The US National Institute of Standards and Technology (NIST) published the AI Risk Management Framework (AI RMF) in 2023 to provide a structured approach to governing AI risk.
Why AI Risk Is Different
AI systems introduce risks that traditional software doesn't:
- Non-determinism: The same input can produce different outputs across runs
- Opacity: Many AI models are "black boxes" — the reasoning behind decisions isn't easily auditable
- Emergent behaviour: AI systems can behave in unexpected ways not predicted by training
- Data dependency: Model quality is entirely dependent on training data quality and representativeness
- Drift: Model performance can degrade silently as the world changes after training
The NIST AI RMF Core Functions
The NIST AI Risk Management Framework organises AI risk management into four functions:
Govern: Establish organisational processes, culture, and accountability for AI risk management.
Map: Identify and categorise AI risks in context — what is the AI used for, who does it affect, what could go wrong?
Measure: Assess identified risks using appropriate metrics, tests, and evaluations.
Manage: Prioritise and treat risks — implement controls, document residual risks, and monitor over time.
Key AI Risk Categories
| Risk Category | Examples | |--------------|---------| | Technical | Hallucination, adversarial attacks, model drift | | Privacy | Training data leakage, membership inference | | Security | Prompt injection, data poisoning, model exfiltration | | Compliance | GDPR violations, EU AI Act non-compliance | | Ethical | Bias, discrimination, lack of explainability | | Operational | Over-reliance, automation error propagation | | Reputational | AI-generated disinformation, public failures |
Getting Started
For SMBs deploying AI tools:
- Inventory all AI systems in use (including vendor-supplied AI features)
- Classify each by risk level — what decisions does it inform or make?
- Define human oversight requirements for high-risk applications
- Establish a policy for what data can be shared with AI tools
- Monitor AI outputs and maintain a process to report issues