Risk Assessment (AI) is the systematic process of identifying, analyzing, and evaluating the potential negative consequences associated with the development, deployment, and use of an artificial intelligence system.

Unlike traditional software risk assessments, which focus largely on deterministic failures (e.g., "will the server crash?"), AI risk assessments must address probabilistic and socio-technical risks. This means evaluating not just if the system works according to its code, but how it interacts with the real world, including the potential for unintended bias, hallucination, misuse by bad actors, and long-term societal impact.

A comprehensive AI Risk Assessment typically follows a structured workflow, often aligned with frameworks like ISO/IEC 23894 or the NIST AI RMF:

• Risk Identification: Cataloging potential hazards. This includes technical risks (data poisoning, model drift), ethical risks (discrimination, lack of explainability), and operational risks (regulatory non-compliance, reputational damage).
• Risk Analysis: Determining the likelihood of a risk occurring and the severity of its impact. For AI, this often involves "Red Teaming" to empirically test how the model behaves under adversarial conditions.
• Risk Evaluation: Comparing the analyzed risks against the organization's risk tolerance criteria. This step determines whether a risk is "acceptable" (e.g., a music recommendation engine making a bad suggestion) or "unacceptable" (e.g., a medical bot misdiagnosing cancer).
• Mitigation Planning: Designing controls to reduce high risks to an acceptable level. This might involve technical fixes (retraining with balanced data), operational changes (human-in-the-loop review), or limiting the scope of deployment.

Regulatory Context: Under the EU AI Act, a documented risk assessment is a mandatory requirement for all High-Risk AI Systems (Article 9). It must be an iterative process, meaning it is not a "one-and-done" document but must be updated throughout the system's entire lifecycle. Similarly, the NIST AI Risk Management Framework positions risk assessment (the "Map" and "Measure" functions) as the core engine of responsible AI governance.

Strategic Impact: A robust risk assessment functions as an "early warning system." By identifying vulnerabilities before deployment, organizations can avoid the costly "technical debt" of fixing a broken model in production and the legal liability associated with deploying unsafe technology.