Framework Overview

AI Governance & Risk Framework

A structured approach to deploying AI responsibly — covering risk classification, bias auditing, explainability, continuous monitoring, and compliance with the EU AI Act and ISO 42001.

EU AI Act ISO 42001 NIST AI RMF SHAP Fairlearn Evidently AI Python
Architecture

Governance Layer Stack

Four interdependent layers form a complete AI governance system — from data infrastructure up to compliance reporting.

Data & Infrastructure Storage · Pipelines · APIs Models & Algorithms Training · Inference · MLOps Governance Controls Audits · Bias · Explainability Compliance & Reporting EU AI Act · ISO 42001 · NIST ◆ ◆ ◆ ◆
EU AI Act

Risk Classification Tiers

The EU AI Act assigns every AI system to a risk category that determines its compliance obligations.

Unacceptable Risk

Prohibited Systems

  • Social scoring by governments
  • Real-time biometric surveillance in public
  • Subliminal manipulation techniques
  • Emotion recognition in workplaces/schools
Banned outright under Article 5. No conformity path — must not be deployed.
High Risk

Regulated Systems

  • CV screening & recruitment AI
  • Credit scoring & loan decisions
  • Medical diagnosis assistance
  • Critical infrastructure management
Requires conformity assessment, human oversight, logging, and registration in the EU database before market entry.
Limited / Minimal Risk

Low-Obligation Systems

  • Chatbots & virtual assistants
  • Spam filters & content moderation
  • Recommendation engines
  • AI-generated content labeling
Transparency obligations apply (e.g., disclose AI interaction). Minimal documentation required.
Framework

Core Governance Pillars

Six capabilities that together constitute a production-grade AI governance program.

Risk Assessment

Systematic inventory of AI use cases with risk tiering, impact analysis, and red-teaming exercises. Outputs a risk register that drives prioritization of controls.

Model Documentation

Structured model cards and datasheets capturing training data provenance, intended use, known limitations, and performance across demographic groups. Feeds audit trails.

Bias & Fairness Auditing

Automated pipelines using Fairlearn to measure disparity metrics across protected attributes (gender, age, ethnicity). Reports flag statistically significant gaps before deployment.

Explainability

SHAP values and LIME for local and global feature attribution. Counterfactual explanations expose what inputs would flip a decision — critical for high-risk use cases.

Continuous Monitoring

Evidently AI dashboards track data drift, prediction drift, and performance degradation in production. Automated alerts trigger retraining or rollback workflows.

Compliance Reporting

Structured conformity assessments aligned with EU AI Act Annex IV and NIST AI RMF profiles. Quarterly governance reports summarize audit findings, open risks, and remediation status.

Roadmap

Implementation Phases

A practical four-phase sequence to stand up an AI governance program from scratch.

1
Discover
Inventory all AI use cases across the organization. Classify each by EU AI Act risk tier. Identify data sources and existing model documentation gaps.
2
Design
Define governance policies, accountability roles (AI owner, risk officer, DPO), and review board cadence. Establish documentation templates and risk acceptance criteria.
3
Implement
Deploy tooling — SHAP for explainability, Fairlearn for bias checks, Evidently AI for monitoring. Integrate model cards and bias reports into CI/CD pipelines.
4
Monitor
Run continuous drift and bias detection in production. Conduct quarterly governance reviews, update risk registers, and publish annual conformity assessment reports.
Standards & Resources

Key Frameworks

The primary external standards and regulations this framework is aligned with.

EU AI Act

World's first comprehensive AI regulation. Defines risk tiers, conformity assessment processes, and prohibited AI practices. Enforceable from August 2026.

View Regulation →

NIST AI RMF

Voluntary framework from the US National Institute of Standards and Technology. Four functions: Govern, Map, Measure, Manage — applicable across sectors.

View Framework →

ISO 42001

International standard for AI Management Systems. Certifiable framework covering governance, risk management, transparency, and continual improvement of AI systems.

View Standard →

IEEE Ethically Aligned Design

IEEE's guide for embedding ethical considerations into autonomous and intelligent systems design — covering wellbeing, data agency, and accountability principles.

View Resource →