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Without governance, organisations face unclear data ownership, inconsistent data quality, compliance risks under the AI Act and GDPR, rising costs, security vulnerabilities, and a loss of trust. Weak governance stops AI from scaling and creates long-term liabilities.
Data & AI Governance is a comprehensive framework that establishes control, ownership, compliance, and security across your data and AI environment.
We help you build a structure that supports AI growth while managing risk, ensuring quality, maintaining regulatory compliance, and protecting against AI-specific threats.
We implement governance controls across every layer of your data and AI operations:
Who owns which data? Who approves AI use cases? Who’s accountable for quality, compliance, and security?
Rules for data access, usage, retention, and deletion. Standards for AI development, deployment, and monitoring.
Continuous checks to ensure data accuracy, completeness, and consistency.
Meet AI Act, GDPR, NIS2, and industry-specific regulatory requirements with documented evidence.
AI-native monitoring (prompt lineage, tool calls, policy verdicts), drift detection, and incident response workflows.
Visibility into data flows, AI decision-making, cost allocation, and system behaviour.
Track compliance, detect issues, identify AI drift and misalignment, and maintain audit trails for inspections.
Quarterly Testing, Evaluation, Verification & Validation (TEVV), post-market surveillance, and residual risk reporting.
The result is governance that enables AI growth rather than blocks it, control without bureaucracy, and security without friction.
Governance prevents these problems by establishing clear ownership, maintaining quality, ensuring compliance, managing AI-specific risks, and building trust.
This gives you an end-to-end governance model where roles, decision rights, and accountability are clearly defined so everyone understands who is responsible for what. You receive comprehensive policies and processes that set clear rules for the use of data and AI, aligned with the AI Act, NIS2, GDPR, and other relevant industry regulations. Continuous quality controls ensure that data remains accurate, complete, and consistent through automated checks.
An AI Act compliance framework provides all required documentation, approval workflows, and audit trails so that your organisation is always inspection-ready.
Technical security controls include AI native monitoring, such as prompt lineage, tool calls, and policy verdicts, along with drift detection, misalignment detection, and incident response capabilities. A structured approach to risk management supports the identification, assessment, and mitigation of compliance, security, and AI-specific risks. Transparent operations provide full visibility into data flows, AI decision making, system behaviour, and costs. Ethical AI guidelines define principles and thresholds for fairness, transparency, explainability, and accountability.
Continuous assurance processes, including quarterly TEVV cycles, post-market surveillance, residual risk scorecards, and compliance reporting, help maintain long-term oversight. Finally, audit readiness is supported through evidence packs, technical documentation, and clear conformity trails for regulatory inspections.
Is this just about AI Act compliance?
No. While AI Act compliance is critical, governance also addresses quality, security, ethics, cost control, and operational efficiency.
Do we need governance if we’re piloting AI?
Yes. Building governance early prevents expensive redesigns later. It’s much cheaper to do it right from the start.
How is this different from regular data governance?
AI introduces unique risks (prompt injection, model drift, bias) and regulations (AI Act) that traditional data governance doesn’t address.
Can we implement this ourselves?
You could, but most organisations lack AI governance expertise and underestimate the complexity of AI Act compliance.
What if we’re already using AI in production?
We can retrofit governance, though it’s
Understand your current capabilities and readiness.
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Stop relying on assumptions. Build governance that protects your organisation and enables AI growth.