Data Governance
Trusted Data based on the following elements
Ownership & Accountability
Uniform Data Point Definitions
Data Architecture & Lineage
Data Inventory & Classification
Data Quality Management
Controls, Validation & Assurance
Monitoring & Remediation
AI Governance extends Data Governance with AI-specific requirements
AI Governance
Trusted AI based on the following core components
AI Ownership & Accountability
Human Oversight
AI Inventory & Use-Case Management
AI Risk Assessment & Classification
Third-Party AI Governance
AI Lifecycle Controls
Transparency & Explainability
Accountability
Quality Management
Monitoring
Controls
Data Governance and AI Governance are closely connected disciplines that together establish the foundation for trustworthy, transparent and scalable use of AI. Effective Data Governance ensures that institutions and organisations can rely on accurate, complete and timely information for decision-making, risk management and reporting. As regulatory expectations continue to evolve, strong governance of data is increasingly viewed as a business and risk management capability rather than a purely technical exercise.
Building on this foundation, AI Governance introduces additional structures, responsibilities and controls required to manage AI-specific risks. Robust governance enables institutions and organisations to deploy AI confidently while balancing innovation, risk management and regulatory expectations.
While Data Governance and AI Governance address different objectives, they rely on shared governance capabilities. Together, these capabilities create the trust and transparency required to support both regulatory compliance and sustainable adoption of AI technologies.
ADVANTA supports financial institutions and organisations in navigating Data Governance and AI Governance with pragmatic, implementation-focused and regulator-aligned solutions. Our focus is on enabling effective governance, sustainable adoption of data and AI capabilities, and operational resilience while ensuring regulatory readiness and alignment with supervisory expectations.
We support institutions and organisations in understanding and operationalising emerging AI-related regulations, including requirements stemming from the EU AI Act and related regulatory initiatives. Our focus is on translating regulatory expectations into practical governance, controls and implementation roadmaps.
We support institutions and organisations in assessing the maturity and effectiveness of their current AI Governance capabilities. Our assessments identify governance gaps, clarify ownership structures and evaluate alignment with regulatory expectations, enabling targeted improvements and sustainable governance development.
We help institutions and organisations establish transparency over existing AI use cases and AI systems by creating structured inventories and classification approaches. This enables risk-based prioritisation, governance oversight and preparation for regulatory reporting obligations.
We support institutions and organisations in assessing and strengthening governance over externally sourced AI solutions and AI service providers. Our approach considers governance, controls, transparency and risk management requirements across the AI supply chain to support safe and responsible adoption.
We help institutions and organisations establish fit-for-purpose AI Governance frameworks covering roles and responsibilities, governance processes, policies and human oversight mechanisms. Our approach ensures that governance structures are scalable, operationally embedded and aligned with broader risk management and control frameworks.
Are you interested in a particular AI topic, or facing a specific AI-related challenge? Book an informative briefing with our experts!
The AI Omnibus has adjusted the EU AI Act implementation timeline, but the direction of travel remains clear: more transparency, stronger governance, and increasing attention around AI-related risks. During this briefing, we explain when the revised EU AI Act timeline takes effect after the AI Omnibus, how current regulatory developments shape expectations around AI governance, transparency and risk management, and what institutions and organisations need to comply with today.
25 min
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Transparency-Focused Profile
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The AI Governance Navigator is an automated guidance tool based on self-reported information. The assessment provides indicative results only and is not legally binding. It does not replace an organisation's own assessment of regulatory applicability, legal analysis, compliance review, or supervisory evaluation. Institutions and organisations remain responsible for determining applicable requirements and implementing appropriate governance measures.
We establish transparency over the organisation's AI landscape by identifying AI systems and use cases, defining organisational roles and uncovering material third-party dependencies. We assess risk exposure, governance maturity and regulatory applicability, distinguishing immediate obligations from upcoming requirements and longer-term strategic governance needs.
We design a proportionate AI Governance framework with clear accountability, decision rights, escalation routes and human oversight across the AI lifecycle. Thus, we integrate AI-specific requirements into existing frameworks, including risk management, compliance, cybersecurity and procurement. Furthermore, we expand these to incorporate required policies, documentation standards and clear accountability for controls.
We implement prioritised governance and control measures based on the organisation's regulatory status, risk profile and AI use cases. This includes AI inventory and classification, lifecycle controls, third-party oversight, human oversight, documentation and incident processes, as well as applicable transparency, disclosure and traceability measures for AI interactions and AI-generated content.
We establish monitoring, assurance and periodic governance reviews to maintain control as AI systems, risks and regulatory expectations evolve. Metrics, incident reporting, control testing, capability building and scenario-based risk reviews support timely escalation, evidence of compliance and responsible scaling of AI across the organisation.
We deliver future-state frameworks, training as well as comprehensive and audit-ready documentation that ensure transparency and traceability.
We assess the maturity of existing Data Governance capabilities, evaluate data quality management, ownership structures and governance processes, and identify gaps against supervisory expectations and good market practices.
We design governance frameworks, ownership models and policies that establish clear accountability across the data lifecycle and support effective risk management and reporting.
We support the implementation of governance controls, data quality measures, lineage capabilities and remediation activities to strengthen data integrity, transparency and reporting effectiveness.
We establish monitoring mechanisms, governance reporting and continuous improvement practices that enable institutions and organisations to maintain sustainable governance capabilities and respond to changing regulatory expectations.
We deliver future-state frameworks, training as well as comprehensive and audit-ready documentation that ensure transparency and traceability.
Managing Director,
Auditor
Lena Franke is a managing partner at ADVANTA. As an auditor and consultant, she supports financial institutions and organisations with risk management and governance topics as well as the implementation of regulatory and ESG-related requirements.
Manager Advisory
Olya Kolesnikova is a manager at ADVANTA. As an expert in risk management, governance, compliance, and ESG, she supports financial institutions and organisations with regulatory requirements as well as the transformation and automation of processes.