Skill Guide

AI risk assessment and management (NIST AI RMF, ISO/IEC 23894, EU AI Act high-risk classification)

A systematic process for identifying, analyzing, evaluating, and mitigating risks associated with AI systems, grounded in authoritative frameworks (NIST AI RMF, ISO/IEC 23894) and regulatory classifications (EU AI Act).

This skill ensures AI deployments are safe, trustworthy, and compliant, directly reducing legal liability, reputational damage, and operational failures. It enables responsible innovation and competitive advantage in regulated markets.

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How to Learn AI risk assessment and management (NIST AI RMF, ISO/IEC 23894, EU AI Act high-risk classification)

Focus on core terminology: risk taxonomy, bias, fairness, robustness, transparency. Study the core functions of NIST AI RMF (Govern, Map, Measure, Manage). Read the EU AI Act's definition of high-risk AI systems. Understand basic risk assessment methodologies like impact assessment and hazard analysis.

Apply frameworks to specific AI use cases (e.g., a CV screening tool, a predictive maintenance model). Conduct a full risk assessment using a structured template. Practice mapping controls from NIST or ISO 23894 to identified risks. Common mistake: focusing only on technical risks while neglecting socio-technical risks like misuse or societal impact.

Architect an organization-wide AI governance program integrating RMF controls into the MLOps lifecycle. Develop risk quantification models that tie AI risk to business outcomes. Lead cross-functional review boards and mentor teams on ethical-by-design principles. Align risk management with corporate ESG and cybersecurity strategies.

Practice Projects

Beginner

Case Study/Exercise

NIST RMF Mapping for a Chatbot

Scenario

You are responsible for a customer service chatbot that uses a large language model. Your manager asks for a preliminary risk assessment.

How to Execute

1. Identify key risks: hallucination (inaccuracy), data privacy leakage, offensive outputs. 2. For each risk, select the relevant NIST AI RMF function: Map (context), Measure (metrics), Manage (mitigation). 3. Propose one concrete control for each risk (e.g., output filtering for offensive content). 4. Document this in a simple risk register.

Intermediate

Case Study/Exercise

EU AI Act High-Risk Classification & Mitigation Plan

Scenario

Your company is developing an AI system for autonomous vehicle obstacle detection. Determine its classification under the EU AI Act and design a compliance plan.

How to Execute

1. Analyze Annex III of the EU AI Act to confirm the system is 'high-risk' (a safety component of a regulated product). 2. Conduct a gap analysis against the Act's requirements (e.g., risk management system, data governance, technical documentation, human oversight). 3. Develop a project plan to implement missing controls, such as establishing a post-market monitoring system. 4. Draft the required technical documentation outline.

Advanced

Project

Enterprise AI Risk Management Program Design

Scenario

As the new Head of AI Governance at a large financial institution, you must establish a company-wide program that covers all AI projects from ideation to deployment.

How to Execute

1. Define an AI risk taxonomy and tiered risk classification scheme aligned to business impact. 2. Design governance processes (e.g., risk review board, approval gates) and integrate them into the SDLC/MLOps pipeline using tools like GRC software. 3. Develop training programs for different roles (developers, product managers, executives). 4. Create key risk indicators (KRIs) and dashboards for executive reporting.

Tools & Frameworks

Regulatory & Standards Frameworks

NIST AI Risk Management Framework (AI RMF 1.0)ISO/IEC 23894:2023 (Information technology - AI - Risk management)EU AI Act (Regulation (EU) 2024/1689)

The primary governance structures for defining processes, requirements, and compliance obligations. NIST and ISO provide the operational methodology; the EU AI Act provides legally binding mandates for high-risk systems.

Technical Tools & Methodologies

IBM AI Fairness 360 (AIF360)Microsoft FairlearnGoogle What-If ToolRisk Assessment Matrix (Likelihood vs. Impact)Model Cards / System Cards

Software libraries for detecting and mitigating bias. Visualization tools for model behavior analysis. Risk matrices are used for prioritization. Model cards provide standardized documentation for transparency.

Interview Questions

Answer Strategy

Demonstrate structured thinking by walking through the four core functions. Sample Answer: 'First, in Govern, we establish the risk management policy and roles. Next, in Map, we define the context-identifying stakeholders (customers, regulators), potential biases in historical transaction data, and the risk of false positives blocking legitimate transactions. Then, in Measure, we quantify fairness (e.g., disparate impact ratio) and accuracy metrics. Finally, in Manage, we implement mitigations like human-in-the-loop for high-value alerts and continuous monitoring for model drift.'

Answer Strategy

Tests conflict resolution, regulatory knowledge, and ethical backbone. Sample Answer: 'I would escalate with data, not opinion. First, I'd conduct a formal gap analysis against EU AI Act requirements, highlighting specific non-compliance risks with financial penalties. Then, I'd present a business case showing that the long-term cost of remediation, fines, and reputational harm far exceeds the short-term delay. I'd propose a clear, accelerated path to compliance to align incentives.'