AI Governance & Compliance

AI systems make decisions that affect people. They can be biased, opaque, or wrong in ways that cause real harm. Without governance, these problems go undetected until they cause damage.

Regulation is increasing. The EU AI Act, sector-specific rules, and emerging standards create compliance obligations. Organisations that ignore governance risk penalties, reputational damage, and operational disruption.

Beyond compliance, governance builds trust. Customers, employees, and partners need confidence that AI is being used responsibly. Good governance provides that assurance.

Comprehensive AI governance addresses several domains.

Accountability establishes who is responsible for AI decisions and outcomes. Clear ownership ensures someone is answering the hard questions.

Transparency ensures AI behaviour can be explained and understood. Users know when they are interacting with AI and can understand why it behaves as it does.

Fairness prevents AI from discriminating or treating people unfairly. Testing identifies bias; controls prevent it from affecting outcomes.

Privacy protects personal data throughout AI processing. Collection, storage, use, and retention follow appropriate principles.

Security safeguards AI systems from attack, manipulation, and misuse. Controls prevent adversarial inputs from causing harm.

Quality ensures AI performs accurately and reliably. Testing, monitoring, and improvement maintain acceptable standards.

We help organisations build and operate AI governance through several services.

Framework development creates policies, standards, and procedures appropriate to your situation. We start with what you need, not a one-size-fits-all template.

Risk assessment identifies where AI creates exposure. We evaluate systems against ethical, legal, and operational risk criteria and prioritise mitigation.

Compliance mapping connects your AI activities to relevant regulations. We identify obligations, assess current compliance, and recommend actions to address gaps.

Technical controls implement governance requirements in systems. Audit logging, access controls, monitoring, and testing turn policy into practice.

Documentation creates the records regulators and auditors expect. Impact assessments, testing evidence, and operational records demonstrate responsible practice.

Training helps your team understand governance requirements and their roles in meeting them. Awareness is the foundation of compliance.

AI regulation is evolving rapidly. Key developments affecting UK organisations include:

EU AI Act: Categorises AI systems by risk level and imposes requirements accordingly. High-risk systems face extensive obligations around documentation, testing, and monitoring.

UK AI framework: Principles-based approach emphasising safety, transparency, fairness, accountability, and contestability. Sector regulators are developing specific guidance.

Data protection: GDPR and UK GDPR create obligations around automated decision-making, profiling, and AI that processes personal data.

Sector regulations: Financial services, healthcare, and other regulated sectors have specific AI requirements from their regulators.

We track these developments and help you understand what applies to your situation.

Governance should enable AI, not prevent it. We help you find the right balance: controls that satisfy legitimate requirements without making AI impractical.

This means:

Proportionate controls. Controls based on actual risk, not theoretical worst cases.

Efficient processes. Governance that integrates with delivery rather than sitting outside it.

Useful documentation. Evidence and records that serve a purpose, not box-ticking.

Adaptability. The ability to evolve as your systems, regulators, and understanding change.

Use these prompts to pressure-test your governance approach before you scale AI use.

“Which AI use cases in our organisation are highest risk, and what controls are appropriate for each?”

“What evidence would we need to show we are using AI responsibly: testing, monitoring, and documentation?”

“Where should we require human review, and how do we define acceptable error rates?”