Risk Assessment

Understanding AI Risk

AI systems introduce unique risks that traditional IT risk frameworks don't fully address. Unlike conventional software, AI systems learn from data, make probabilistic predictions, and can exhibit unexpected behaviors. They may perpetuate or amplify biases present in training data. They can be vulnerable to adversarial attacks specifically designed to manipulate AI outputs. Effective AI governance requires understanding and managing these distinctive risks.

Risk assessment for AI must be systematic and comprehensive, yet practical enough to scale across your organization's AI portfolio. The key is matching the rigor of risk assessment to the actual risk level, focusing detailed analysis on high-stakes applications while maintaining lighter-touch oversight for lower-risk uses.

Risk assessment is not a one-time gate to pass before deployment. AI systems exist in dynamic environments where risks evolve over time. Build continuous risk assessment into your AI lifecycle, not just upfront evaluation.

Key Risk Categories

1. Fairness and Bias Risks

AI systems can perpetuate, amplify, or introduce biases that lead to unfair or discriminatory outcomes. These biases may stem from training data that reflects historical discrimination, from proxy variables that correlate with protected characteristics, or from deployment contexts that affect different populations differently. Bias risks are particularly acute when AI informs decisions about employment, lending, housing, education, healthcare, or criminal justice.

Assessing fairness risks requires both technical analysis and contextual understanding. Involve domain experts, affected communities where possible, and ethics expertise in fairness assessments.

2. Security and Privacy Risks

AI systems face both traditional security risks and novel AI-specific threats. AI-specific risks include model extraction attacks, adversarial attacks that manipulate inputs, data poisoning, and model inversion attacks. Privacy risks are particularly significant — AI models can memorize and inadvertently reveal sensitive information from training data, and large language models may be prompted to generate private information they've learned.

3. Regulatory and Compliance Risks

The regulatory landscape for AI is evolving rapidly. The EU AI Act creates a comprehensive risk-based regulatory framework. GDPR includes provisions affecting automated decision-making. Industry-specific regulations increasingly address AI. Compliance risk assessment must identify which regulations apply to each AI system based on its use case, geographic scope, industry sector, and the types of decisions it makes.

4. Operational and Performance Risks

AI systems can fail in ways that impact business operations. Models may produce incorrect predictions, performance can degrade over time (model drift), dependencies on external AI services create availability risks, and integration failures can occur. Assess operational risks by understanding the business process context and ensuring robust monitoring, human oversight, and contingency plans.

Risk Assessment Process

A structured risk assessment process ensures consistent, thorough evaluation. Start by clearly defining the AI system scope and use case. Identify the risk categories relevant to this specific system. For each relevant category, assess both likelihood and impact using a combination of technical analysis, expert judgment, and stakeholder input.

Document identified risks in a risk register with clear descriptions, likelihood and impact ratings, risk owners, and proposed mitigation strategies. The risk assessment should culminate in a clear recommendation: proceed with deployment, proceed with specified mitigations, or do not proceed until risks are addressed.

From Assessment to Action

Risk assessment is valuable only if it leads to action. For each identified risk, develop specific mitigation strategies. Some risks can be reduced through technical controls, others require process controls. Residual risks must be explicitly accepted by appropriate stakeholders with clear understanding of potential consequences.