This guide speaks to practitioners building high-stakes systems. We incorporate principles from the NIST AI RMF, ISO\/IEC 23894, and the EU AI Act.<\/p>\n
Defining AI Risk Categories and Standards<\/h2>\n
Before running tests, you must establish a shared vocabulary across your organization. AI risk differs fundamentally from traditional software risk. Product risk involves system downtime or data breaches. Model risk involves unpredictable outputs, silent failures, and compounding biases.<\/p>\n
Generative models present different challenges than predictive models. Predictive models might deny a loan unfairly. Generative models might fabricate a legal citation entirely.<\/p>\n
You must track several distinct AI risk categories<\/strong>:<\/p>\n Your assessment must align with recognized global standards. The NIST AI RMF<\/a> categorizes activities into four functions: Map, Measure, Manage, and Govern. This structure helps teams identify context, quantify risks, apply controls, and maintain oversight.<\/p>\n The ISO\/IEC 23894 standard<\/a> integrates AI risk into broader enterprise risk management. It provides a lifecycle approach to risk. The EU AI Act consolidated text<\/a> introduces risk tiers.<\/p>\n High-risk systems require strict conformity assessments. You must maintain extensive technical documentation and logging. You must prove your system operates safely under scrutiny.<\/p>\n Standards require translation into daily workflows. A theoretical mapping offers no protection against an active prompt injection attack. You need a step-by-step process with clear acceptance criteria.<\/p>\n Start by defining the exact boundaries of your AI system. Document the exact use case and all involved parties.<\/p>\n Threat modeling for AI requires specialized techniques. You must anticipate how the system might fail or face exploitation. Document potential prompt injection vectors and data poisoning vulnerabilities.<\/p>\n Identify bias vectors in your training data. Map out potential misuse scenarios. Look for areas where users might bypass intended safety filters.<\/p>\n You cannot manage what you do not measure. Run rigorous dataset diagnostics before deployment. Conduct bias and fairness testing<\/strong> across all demographic groups.<\/p>\n Execute resilience tests against adversarial inputs. Measure hallucination rates against strict benchmarks. Complete a thorough data privacy impact assessment<\/strong>.<\/p>\n Design controls that mitigate your identified risks. Implement strict guardrails and output filtering. Use retrieval grounding to tether generative responses to verified facts.<\/p>\n Require human-in-the-loop oversight for high-stakes decisions. Set rate limits and prepare incident response playbooks. Build fallback mechanisms for when the primary model fails.<\/p>\n Auditors look for a clear explainability and audit trail<\/strong>. Maintain strict versioning for models and prompts. Keep detailed decision logs and sign-off matrices.<\/p>\n Publish model cards detailing capabilities and limitations. Store all evaluation reports centrally. Make these reports accessible to compliance teams.<\/p>\n AI systems degrade over time. You need continuous model monitoring<\/strong> to detect drift. Establish feedback loops to capture user corrections.<\/p>\n Set trigger thresholds that force a manual review. Schedule periodic re-assessments based on system criticality. Readers seeking practical implementation can explore our dedicated risk assessment use case<\/a> with workflows and orchestration modes.<\/p>\n Execution requires the right tools and templates. You need concrete artifacts to prove your compliance to auditors and regulators.<\/p>\n Watch this video about ai risk assessment:<\/strong><\/p>\n\n
Mapping to Global Standards<\/h3>\n
Translating Standards into a Repeatable Process<\/h2>\n
Step 1: Scoping and Mapping<\/h3>\n
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Step 2: Risk Identification<\/h3>\n
Step 3: Measurement and Testing<\/h3>\n
Step 4: Control Selection and Design<\/h3>\n
Step 5: Governance and Evidence<\/h3>\n
Step 6: Monitoring and Review<\/h3>\n
Executing Your AI Risk Assessment<\/h2>\n
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