ATLAS AI Governance Framework

“You cannot manage what you cannot see.”

Core Components & Advisory Scope

• AI Inventory: Recording every AI system in the organisation — in-house models, SaaS, open source, agentic AI, shadow AI and APIs — in an ISO/IEC 42001 aligned register.
• Use Cases & Risk Classification: Classifying use cases such as chatbots, fraud detection and document intelligence as low, medium, high or prohibited under the EU AI Act.
• AI Impact Assessment: Evaluating legal (GDPR/IP), ethical (bias/transparency), security (prompt injection/poisoning) and operational impacts.
• Regulatory Mapping & Data Assessment: Mapping obligations across the EU AI Act, ISO 42001, NIST AI RMF, GDPR and DORA, plus data quality and lineage analysis.
• Maturity Assessment: Determining the organisation's AI governance maturity across ten distinct dimensions.

Core Components & Advisory Scope

• Explainability & Transparency (XAI): Making decision mechanisms intelligible and establishing Model Card and System Card standards.
• Fairness & Bias Management: Algorithmic discrimination assessments, data balancing principles and analysis of effects on sensitive groups.
• Data Reliability & Quality: Source verification criteria, data lineage, and copyright / usage rights audit principles.
• Human Oversight: Designing human-in-the-loop (HITL) and human-on-the-loop (HOTL) control mechanisms in decision processes.
• Ethical Compliance & Traceability: Defining corporate AI ethics principles and audit log standards.

Core Components & Advisory Scope

• AI Strategy & Roadmap: An AI vision aligned with corporate objectives, together with an investment plan and prioritisation model.
• Governance Operating Model: Defining the AI Governance Board, AI CoE, risk, compliance and business unit roles through a RACI matrix.
• AI Portfolio Management: Managing projects by business value, risk and cost, and directing resources to the right initiatives.
• Culture, Awareness & Change Management: Corporate training, policies and workforce adaptation plans for safe AI use.
• Performance & Value Measurement: Tracking ROI, operational efficiency and model performance KPIs.

Core Components & Advisory Scope

• AI Audit & Compliance Management: Internal and external audit mechanisms with continuous compliance tracking against the EU AI Act, ISO 42001 and NIST AI RMF.
• Model Validation & Testing Standards: Establishing technical, risk (bias / hallucination) and security evaluation criteria applied before and during production.
• AI Evidence Management: Technical documentation and traceability record standards meeting EU AI Act high-risk requirements.
• Continuous Improvement & Incident Analysis: A 'measure → evaluate → correct' cycle against data and model drift, plus incident root-cause procedures.

Core Components & Advisory Scope

• AI Security Governance & Policy Design: Preparing a corporate AI Security Policy and implementation principles aligned with ISO 27001 and NIST CSF.
• Data Security Governance & Usage Rules: Defining sensitive data usage, encryption and masking requirements, and Data Loss Prevention (DLP) procedures.
• AI Threat Management Standards: Defining corporate usage constraints and control criteria against prompt injection, data poisoning and hallucination risks.
• Access Governance & Agent Principles: Establishing role-based access (RBAC) principles, plus human approval (HITL) and permission matrix procedures for authorised AI agents.
• Incident Response Procedures: Designing the AI Incident Response Procedure to be executed by internal teams in the event of AI-driven data leakage or security breaches.