Why AI Governance Now?
AI systems increasingly make decisions that affect people — in HR, credit lending, healthcare, enforcement and marketing. Without governance, organisations risk discriminatory outcomes, reputational damage, legal liability and non-compliance with rapidly evolving regulation.The EU AI Act, NIST AI RMF and ISO 42001 each provide a framework. We help you choose and implement the right combination — tailored to your AI portfolio and risk appetite.
Framework Implementation
We align your AI operations with leading governance standards:- EU AI Act: The EU AI Act classifies AI systems by risk level. High-risk AI (HR, credit scoring, biometrics, critical infrastructure) requires mandatory conformity assessments, registration in the EU database and continuous monitoring. Prohibited AI practices (manipulative systems, social scoring) must be identified and ceased.
- NIST AI Risk Management Framework (AI RMF): The NIST AI RMF provides four core functions — Govern, Map, Measure and Manage — for systematically managing AI risks across the full lifecycle of AI systems.
- ISO 42001: The international standard for AI Management Systems, which embeds governance, accountability and continuous improvement in your organisation. More about ISO 42001 →
Bias & Fairness Audits
Algorithmic bias is a growing legal and reputational risk. Models trained on historical data reproduce and amplify historical inequalities — with potentially discriminatory outcomes for protected groups. We conduct bias audits on your models and data:-
Identification of proxy variables representing protected characteristics
Measurement of demographic parity, equalised odds and other fairness metrics
Concrete recommendations for fairness interventions in data, modelling or post-processing
Policy Development
Internal AI governance begins with clear policy. We develop:- Acceptable Use Policy for AI: What can and cannot be done with AI tools and systems within your organisation?
- Data Privacy Policy for AI: What data may be input to external LLMs? How is training data selected and managed?
- Model Transparency Policy: How are AI decisions explained to users and affected individuals?
- Human Oversight Protocol: When is human review mandatory for AI-driven decisions?
Risk Inventory
Before governance can be put in place, you need to know what you have in house. We catalogue and classify all AI assets in your organisation:-
Inventory of all AI systems, tools and models (internally developed and SaaS)
Classification based on EU AI Act risk levels
Impact assessment per system: what decisions does it make, who are affected by outcomes?
Prioritisation of governance efforts based on risk and impact