AI GOVERNANCE SERVICES
Data Governance
Service Overview
Effective data governance is essential for operational resilience, regulatory compliance, and AI accountability. Without a structured framework, organizations face risks related to data integrity, security, compliance failures, and inefficiencies in AI-driven decision-making.
Our approach establishes a governance structure that is secure, scalable, and aligned with business objectives. We focus on key pillars: Data Stewardship & Ownership, ensuring clear roles and accountability; Data Quality & Integrity, maintaining accuracy, consistency, and completeness; Data Security & Privacy Compliance, implementing protection controls and access governance; AI & Risk Governance, ensuring ethical AI oversight and risk mitigation; and Data Lifecycle & Retention Management, defining structured policies for data storage, processing, and deletion in compliance with regulatory requirements.
Our framework integrates leading global standards, including ISO 38505, DAMA-DMBOK, COBIT, ISO 8000, ISO 25012, ISO 27001, GDPR, and the EU AI Act, enhancing compliance, security, operational efficiency, and long-term resilience.
Our Approach
Phase 1: Assessing the Current Data Governance Framework
A comprehensive evaluation of existing data governance policies, stewardship roles, data lifecycle management, and compliance readiness is conducted. The assessment identifies gaps in data integrity, access control, and accountability, strengthening governance structures and improving data quality.
Phase 2: Regulatory Compliance Alignment
Governance policies and processes are aligned with regulatory frameworks to support ethical and legally sound data management. By implementing compliance-driven policies, organizations establish structured governance, transparent data handling, and accountability mechanisms, reducing risks associated with improper data use.
Phase 3: Developing a Tailored Data Governance Framework
A structured governance model is designed, incorporating customized policies, procedures, and controls. This phase defines clear roles for data ownership, stewardship, and custodianship, ensuring data quality, consistency, and effective lifecycle management across all business functions.
Phase 4: Training and Knowledge Transfer
To support long-term governance sustainability, training programs cover AI data governance, ethical data management, compliance enforcement, and data stewardship best practices. These programs equip teams with the knowledge required to manage data integrity, transparency, and regulatory responsibilities, embedding a culture of data-driven accountability.
Our methodology integrates global standards, including ISO 38505, DAMA-DMBOK, COBIT, ISO 8000, ISO 25012, ISO 27001, GDPR, and the EU AI Act, enhancing compliance, security, and operational resilience.
Benefits to Your Organization
Accountability
Defines clear roles and responsibilities, ensuring consistent application of data governance.
Efficient Vendor Management
Ensures third-party compliance with governance standards, reducing data quality risks and enhancing compliance and security.
Operational Efficiency
Ensuring proper data lifecycle management, improving accessibility, consistency, and integration across functions.
Risk Mitigation
Identifies and reduces governance risks in internal and external data handling.