Skill Guide

Compliance frameworks - NIST AI RMF, ISO 42001, EU AI Act, SOC 2 for AI pipelines

The application of specific governance standards and regulatory requirements to the development, deployment, and monitoring of artificial intelligence systems to mitigate risk and ensure trustworthiness.

This skill is critical for enabling responsible AI innovation while avoiding regulatory penalties, reputational damage, and operational failures. It directly impacts an organization's ability to scale AI commercially in regulated markets.

1 Careers

1 Categories

9.2 Avg Demand

15% Avg AI Risk

How to Learn Compliance frameworks - NIST AI RMF, ISO 42001, EU AI Act, SOC 2 for AI pipelines

Begin with core terminology: risk management, transparency, accountability, and data governance. Read the NIST AI RMF 1.0 Playbook and the EU AI Act summary to understand their scopes and key principles.

Apply frameworks to real pipeline components (data ingestion, model training, deployment). Focus on mapping controls from SOC 2 or ISO 42001 to specific AI lifecycle stages. Common mistake: treating compliance as a one-time audit rather than a continuous engineering practice.

Architect integrated compliance-as-code systems. Develop organization-specific risk taxonomies that reconcile multiple frameworks. Lead cross-functional teams (Legal, Engineering, Product) to implement governance at scale.

Practice Projects

Beginner

Project

NIST AI RMF Risk Assessment for a Simple Model

Scenario

You have a Python script that uses a pre-trained sentiment analysis model to classify customer feedback. The model will be integrated into a internal dashboard.

How to Execute

1. Identify the system's intended use, stakeholders, and potential impacts using NIST RMF 'Map' function. 2. Document known limitations and biases of the pre-trained model. 3. Define simple mitigation controls (e.g., human review of low-confidence predictions) and document them in a basic risk register.

Intermediate

Case Study/Exercise

SOC 2 Type I Readiness for an AI Pipeline

Scenario

A startup is building an AI-powered fraud detection service for financial clients. They need to demonstrate security controls to close their first enterprise deal.

How to Execute

1. Map the AI pipeline stages (data collection, feature engineering, model training, API serving) to SOC 2 Trust Service Criteria (Security, Availability). 2. Identify and document required controls: access logs for training data, model versioning and change management, API endpoint security. 3. Draft a gap analysis report highlighting missing controls (e.g., no formal incident response plan for model bias incidents).

Advanced

Project

EU AI Act High-Risk System Conformity Assessment Design

Scenario

Your company is developing an AI system for medical diagnostic support, classified as high-risk under the EU AI Act. A Notified Body will audit the system.

How to Execute

1. Conduct a full conformity assessment mapping to Annex I requirements (technical documentation, risk management, data governance, transparency, human oversight). 2. Design the required Quality Management System (QMS) processes specific to AI, integrating with existing ISO 13485 for medical devices. 3. Implement the technical 'logbook' for recording training data, hyperparameters, and performance metrics as mandated by Article 12. 4. Prepare the complete technical file for Notified Body submission.

Tools & Frameworks

Governance & Risk Frameworks

NIST AI RMF 1.0ISO/IEC 42001:2023EU AI Act (Regulation 2024/1689)SOC 2 Trust Service Criteria

NIST provides a voluntary, risk-based framework. ISO 42001 specifies requirements for an AI Management System (AIMS). The EU AI Act is a legally binding regulation with tiered requirements based on risk. SOC 2 provides criteria for auditing service organizations. Use these to structure policies and audit requirements.

Technical & Operational Tools

Model CardsData Sheets for DatasetsMLflow (with governance plugins)OpenLineage

Model Cards and Data Sheets provide standardized documentation for transparency. MLflow can be extended for experiment tracking and provenance. OpenLineage provides a framework for data pipeline lineage. These tools operationalize compliance principles within engineering workflows.

Interview Questions

Answer Strategy

Demonstrate structured risk triage and cross-functional facilitation. Answer: 'I would first facilitate a risk classification workshop with both teams using the EU AI Act's tiered approach. We would analyze the specific use case against the Act's definitions of high-risk and limited-risk AI. For our specific feature, I'd propose a phased rollout: an initial internal pilot with enhanced human oversight and logging to gather data, followed by a formal conformity assessment before external launch, aligning with our ISO 42001 AIMS procedures.'

Answer Strategy

Tests practical application and problem-solving. Answer: 'In a project aligning with NIST AI RMF, I discovered our model validation process only measured aggregate accuracy, not performance across demographic subgroups-a gap in the 'Measure' function. I led the implementation of disaggregated evaluation metrics and fairness tests into our CI/CD pipeline, creating automated reports for the governance board, which became a new control requirement.'