How to Conduct an AI Risk Assessment: A Practical Guide | Arté

AI systems introduce a category of risks that traditional information security assessments were never designed to address. Biased outputs, hallucinations, adversarial attacks, privacy violations from training data, lack of explainability — these are not edge cases. They are inherent characteristics of how AI systems work, and they require their own risk assessment approach. Whether you're deploying a commercial AI tool, developing models in-house, or using AI features embedded in existing software, understanding and managing these risks is becoming both a regulatory expectation and a business necessity. Why AI Needs Its Own Risk Assessment A standard information security risk assessment focuses on confidentiality, integrity, and availability of information. AI risk assessment shares some of these concerns but adds dimensions that don't fit neatly into the traditional CIA triad: Fairness and bias — Does the AI system produce discriminatory outcomes for certain groups? Reliability — Does it hallucinate, drift in accuracy over time, or fail silently? Transparency — Can you explain how and why the system produced a specific output? Accountability — Who is responsible when an automated decision causes harm? Privacy — Does the system process personal data in its training or operation, and can that data be extracted? These are not theoretical concerns. The EU AI Act explicitly requires risk management for high-risk AI systems, and ISO/IEC 42001 provides a management system framework for governing AI responsibly. Step 1: Define the Scope Start by identifying which AI systems are in scope. This includes: AI models you develop or train internally Commercial AI tools and APIs you use (including large language models, copilots, and automation tools) AI features embedded in third-party software (CRM predictions, automated screening, chatbots) Experimental or pilot AI projects For each system, document its purpose, the data it processes, who uses it, and what decisions it influences. An AI system that recommends products has a different risk profile than one that screens job applicants or approves credit applications. Step 2: Identify AI-Specific Threats AI systems face threats that go beyond traditional cybersecurity: Data threats — Biased or unrepresentative training data, data poisoning by adversaries, poor data quality that degrades model performance, and privacy violations from personal data in training sets. Model threats — Adversarial attacks that manipulate model outputs, prompt injection in large language models, model theft through API extraction, and performance degradation (model drift) over time as real-world data distributions change. Operational threats — AI hallucinations producing confident but false outputs, automated decision errors with real-world consequences, lack of human oversight in critical processes, and negligent operators who over-rely on AI outputs without validation. Ethical and legal threats — Discriminatory outcomes that violate fairness principles, non-compliance with AI regulations (EU AI Act, national laws), lack of explainability that prevents meaningful human review, and privacy violations from AI processing. Step 3: Identify AI-Specific Vulnerabilities On the vulnerability side, consider weaknesses specific to AI systems: Data vulnerabilities — Insufficient or unrepresentative training data, lack of data versioning and lineage tracking, missing data quality controls, and personal data mixed into training datasets without proper governance. Model vulnerabilities — Inadequate model testing and validation, lack of ongoing model monitoring, missing model documentation, no version control for models, and insufficient explainability mechanisms. Governance vulnerabilities — Missing AI ethics framework, no AI governance policy, lack of AI-specific risk assessment processes, inadequate accountability mechanisms, and missing AI impact assessments. Operational vulnerabilities — No AI-specific incident response procedures, missing human-in-the-loop controls for critical decisions, inadequate performance monitoring and alerting, and no AI system backup and recovery procedures. Step 4: Score and Prioritize As with any risk assessment, evaluate each AI risk on likelihood and impact. The same 5x5 matrix used for information security risks works for AI risks — but the factors that drive likelihood and impact are different. For likelihood, consider: How exposed is the AI system? How sophisticated are the potential adversaries? How frequently does the risk type occur in similar deployments? Is the AI system customer-facing or internal? For impact, consider: What decisions does the AI system influence? Could a failure cause financial harm, reputational damage, or physical safety issues? Are there regulatory penalties for non-compliance? Could discriminatory outputs affect individuals' rights? Step 5: Define Controls and Treatment AI risks require AI-appropriate controls: Data governance — Data quality checks, bias testing, privacy impact assessments, data lineage tracking Model governance — Regular testing and validation, monitoring for drift, version control, documentation Human oversight — Human-in-the-loop for high-risk decisions, override capabilities, escalation procedures Transparency — Explainability mechanisms appropriate to the risk level, clear disclosure when users interact with AI Incident response — AI-specific incident procedures, reporting mechanisms for AI failures, post-incident review processes Step 6: Review and Iterate AI systems change more frequently than traditional IT systems. Models are retrained, fine-tuned, or updated. Training data evolves. Usage patterns shift. The regulatory environment is actively developing. Build a review cycle that accounts for this pace of change. Reassess risks when models are updated, when training data changes significantly, when new use cases are deployed, and when regulatory requirements evolve. The Framework Foundation ISO/IEC 42001 provides the internationally recognized management system framework for AI governance, including risk assessment requirements. The EU AI Act mandates risk management for high-risk systems. Both emphasize a structured, documented, and repeatable approach to identifying and managing AI risks. You don't need to address every AI risk on day one. Start with your highest-risk AI systems — those that affect people's rights, make consequential decisions, or process sensitive data — and build your assessment capability from there. Start an AI risk assessment