Regulatory Data Governance gives your organisation full control over data — its sources, legal status, quality, and access — enabling AI development, protecting intellectual property, and meeting regulatory requirements in a predictable and scalable way.
Organisations process ever-growing volumes of data, yet typically lack clear accountability, consistent classification processes, access controls, documentation, data quality standards, and an auditable data trail.
Regulations such as GDPR, the Data Governance Act, the Data Act, NIS2, and the AI Act require transparency, oversight, and control across the entire data lifecycle — from acquisition and processing to sharing and deletion. Without a coherent Data Governance framework, it becomes difficult to meet enterprise customer expectations, deploy AI models lawfully, and protect IP embedded in data assets and repositories.
Gain full control over data — including source, legal status, quality, and access scope
Reduce legal and operational risks related to AI, security, and data protection
Protect intellectual property — from datasets to models and confidential assets
Meet the expectations of enterprise customers, investors, and regulators
Build a stable foundation for AI model development, automation, and data products
Improve information management across the organisation — from Data Ops to Legal and Security
Shorten implementation cycles and decision-making through better data quality and availability
Regulatory Data Governance is a comprehensive framework covering data management processes, roles, standards, policies, and practices across the organisation — designed in line with EU regulations and recognised best practices (ISO, NIST, DAMA-DMBOK).
Audit of data sources, processing methods, documentation, and organisational roles.
Assessment of regulatory requirements relevant to your business and associated risks.
Design of data governance roles, accountability structures, and management processes.
Creation of data governance policies, standards, procedures, and controls.
Design of access control, data quality, and security governance mechanisms.
A phased data governance implementation plan aligned with business priorities.
We deliver a practical — not merely formal — approach to data governance and IP protection.
We have supported organisations where data legality and control over data flows were critical for B2B cooperation, investment, and AI deployment.
AIGP (AI Governance), ISO/IEC 27001 Lead Auditor, CDPSE, CIPP/E, ISO/IEC 42001 Lead Implementer — a skill set essential for data governance, AI, and management systems.
We deliver policies, procedures, and guidelines in an operational form, ready for immediate deployment.
We focus on solutions that work in real technology environments and directly support product development and AI model growth.
Data originates from multiple sources, with unclear legal status, quality, completeness, and access controls.
Implementation of Regulatory Data Governance covering data classification, quality standards, Data Steward roles, and documentation processes.
The organisation gained full data control and could safely use datasets in AI models.
Customers and partners require full transparency of data flows, controls, and documentation, while processes differ across departments.
A complete Data Governance Framework with policies, RACI matrices, controls, and data quality standards.
The organisation passed the audit successfully, strengthened partner trust, and reduced operational risk.
Explore answers to key questions regarding our services. Here, you will find quick and concise explanations designed to help you understand our offering.
No — individual regulations address it to varying degrees. However, coherent Data Governance is the foundation of compliance with GDPR, NIS2, the Data Act, and the AI Act, as most obligations rely on control over data sources, quality, documentation, and accountability.
Yes — we cover text, images, visual data, logs, and external datasets.
Yes — all standards are ready to implement.
Yes — the framework is scalable.
Yes — Data Governance is critical for dataset documentation.
Yes — we design data quality standards.
Typically 4–12 weeks, depending on organisational scale.
