Map AI Risk Across Four Business Areas

A model can be technically accurate and still damage a business through poor integration, weak adoption, inappropriate decisions, privacy failures, or misleading claims. Risk mapping must cover the whole system. This decision connects to Build an AI Strategy That Starts With Business Value and Turn Company Priorities Into an AI Opportunity Portfolio, which provide the strategic direction and portfolio context. Use this four-part leadership framework Technical — Examine reliability, security, data quality, model behavior, integration, and observability. Operational — Examine process failure, exception load, continuity, ownership, cost, and vendor dependency. Human — Examine affected people, bias, overreliance, accessibility, notice, challenge, and skills. Legal and reputation — Examine privacy, contracts, sector duties, claims, intellectual property, and trust. A practical decision example A hiring-support tool performs well on a test set but lacks an appeal path, hides source evidence, and produces more exceptions than recruiters can review. The risk map prevents a narrow accuracy metric from authorizing launch. This is a hypothetical example; use your own baseline, constraints, and evidence. Evidence, governance, and responsible use Use the NIST AI Risk Management Framework to connect the initiative to governance, context, measurement, and ongoing management. The companion NIST AI RMF Playbook turns those functions into questions leaders can assign and review. The GAO AI Accountability Framework is useful for examining governance, data, performance, and monitoring across the system life cycle. Compare the plan with the OECD AI Principles, particularly transparency, robustness, accountability, and respect for people affected by the system. For generative AI, review the NIST Generative AI Profile and test representative cases using OpenAI’s evaluation guidance. Use the OWASP Top 10 for LLM Applications to discuss application threats before a model can access sensitive information or take actions. Check performance statements against the FTC’s guidance on AI claims, and review information handling with the FTC’s privacy and security resources. For a broader organizational management approach, study the ISO/IEC 42001 overview. Take this to the next leadership meeting Create a risk register with scenario, cause, affected group, consequence, likelihood, detectability, controls, control owner, evidence, residual risk, and escalation threshold. Record the owner, evidence source, decision date, and what would cause the company to stop. The goal is not to make the document look complete. The goal is to make the next decision explicit, measurable, and accountable. Continue with Decide Where Human Judgment Must Remain Primary. Use Run a Quarterly AI Portfolio Review: Keep, Fix, Scale, or Stop to revisit the decision with current evidence.