Building a European AI Trust & Data Access Framework for Secure BI and GenAI

A completed European case study on connecting AI governance, data classification, provider assessment, security, privacy, and approval processes for secure BI and GenAI.

Building a European AI Trust & Data Access Framework for Secure BI and GenAI

The project: turning governance requirements into an operating product

As the European client built modern Business Intelligence and AI solutions, the project addressed a central question: How could sensitive enterprise data be used for analytics and AI without losing control over security, privacy, accountability, and regulatory compliance?

The project delivered an internal AI Trust & Data Access Framework. This is a descriptive name for the governance product created during the engagement, not a public product name. The framework combined policies, decision processes, data-classification rules, security concepts, and organisational responsibilities in one operating model.

Its users included business teams proposing AI use cases, data architects mapping information flows, BI teams preparing analytical data, and the security, privacy, governance, and management stakeholders responsible for approving its use. Instead of treating governance as a final document review, the framework guided a request from its initial business purpose through data and platform assessment to a controlled approval decision and subsequent review.

The problem behind the product

BI platforms organise enterprise information for reporting and analysis. AI and GenAI solutions introduce additional questions because data can appear in prompts, retrieval indexes, model training or fine-tuning, application logs, monitoring records, and provider-operated services. The same dataset can therefore have different risks depending on its purpose, users, technical path, and the AI platform processing it.

The client needed a common method for answering practical questions across these environments:

Which enterprise data was involved, and how sensitive was it?
Did the intended AI use fit the original purpose and permissions attached to the data?
Which provider, cloud, privacy, and security risks required controls?
Which governance or legal assessments were necessary?
Who could approve the use of enterprise data in an AI system?
What evidence had to be retained, and when did a change require reassessment?

The project converted these questions into a repeatable governance process. It connected data architecture, BI governance, information security, privacy, and AI regulation rather than allowing each discipline to review the same solution in isolation.

How the AI Trust & Data Access Framework worked

The completed operating model used a seven-stage flow.

1. Register the use case

The process began with a structured description of the intended AI or BI solution: its business purpose, users, data needs, expected outputs, accountable owner, and proposed platform or provider. This made the purpose reviewable before technical implementation decisions obscured it.

2. Map the data path

Data architects and BI teams documented where the relevant data originated, how it was transformed, which analytical or semantic models were involved, and how the AI solution accessed or produced information. The resulting data flow identified the points at which access, minimisation, isolation, logging, or retention controls were required.

3. Classify the data

The framework introduced criteria for assessing sensitivity, personal-data relevance, business criticality, confidentiality obligations, and the consequences of inappropriate disclosure or reuse. Classification was connected to action: a classification decision determined which access controls, review steps, and approval level applied.

4. Assess the AI provider and platform

The project established a consistent assessment of external AI providers, AI platforms, and their cloud-security characteristics. The review covered data location and transfer, retention and deletion, provider access, subcontractors, use of customer data for model improvement, encryption, identity and access management, logging, incident handling, contractual safeguards, and exit considerations.

This prevented provider selection from becoming a feature-only decision. Security and privacy conditions became part of the architecture and approval record.

5. Perform privacy, compliance, and AI-risk screening

The use case was screened against data-protection requirements and the EU AI Act. The process recorded the organisation's role, the nature of the processing, affected people, automated or assisted decisions, transparency needs, potential harm, and the need for deeper legal, privacy, or security review.

The EU AI Act uses a risk-based approach and distinguishes prohibited, high-risk, transparency, and minimal-risk uses (European Commission: AI Act). The binding legal text is Regulation (EU) 2024/1689 (EUR-Lex: Artificial Intelligence Act). The framework translated this regulatory structure into screening and escalation questions; it did not treat one checklist as a universal declaration of compliance.

For personal data, the review incorporated the GDPR principles, data protection by design and by default, security of processing, and the requirement to assess processing likely to create a high risk (EUR-Lex: General Data Protection Regulation). The European Data Protection Board explains that a Data Protection Impact Assessment must precede processing likely to create a high risk to individuals' rights and freedoms (EDPB: Data protection impact assessment).

6. Define controls and make the approval decision

The assessment produced a control package and a documented decision. Controls could address data minimisation, access, environment separation, encryption, logging, retention, human oversight, user information, contractual safeguards, or restricted functionality. The approval path assigned responsibilities and made clear whether a use case could proceed, required changes, or needed escalation.

The process also defined triggers for reassessment. A change of provider, model, purpose, data category, user group, or integration path could alter the risk and therefore reopened the relevant review.

7. Monitor and maintain the decision

Governance continued after release. Ownership, control evidence, incidents, material changes, and periodic reviews remained connected to the approved use case. ENISA's Multilayer Framework for Good Cybersecurity Practices for AI provides a European, step-by-step approach covering cybersecurity foundations, AI-specific cybersecurity, and sector-specific controls across the AI lifecycle (ENISA: Multilayer Framework for Good Cybersecurity Practices for AI). The project applied the same lifecycle principle: governance remained active while the system and its context changed.

Data architecture and BI governance as control foundations

The project treated data architecture as part of governance. Policies alone could not protect sensitive data if teams could not identify its origin, transformations, ownership, or consumption paths.

The architecture work therefore connected source information, BI structures, analytical models, and AI interfaces. It identified where classification metadata, access decisions, and security controls had to follow data through the processing chain. Business definitions and ownership responsibilities helped prevent the same data from being interpreted or governed differently by separate teams.

Within the BI environment, the engagement established governance structures for ownership, stewardship, quality, access, and change. BI teams retained the ability to build useful analytical content, while data owners and control functions had a defined role in how sensitive information was prepared and reused for AI.

This produced a practical separation of responsibilities:

business owners remained accountable for purpose and acceptable use;
data owners and stewards maintained meaning, classification, and quality expectations;
data architects and BI teams implemented controlled information flows;
security and privacy specialists assessed technical and legal risks; and
management resolved material risk decisions and policy exceptions.

Security concepts for cloud and AI platforms

The security concept combined enterprise security controls with AI-specific risks. For organisations within its scope, Article 21 of the NIS2 Directive establishes a European baseline covering risk analysis, incident handling, business continuity, supply-chain security, secure development and vulnerability handling, encryption, access control, asset management, and authentication (EUR-Lex: Directive (EU) 2022/2555 — NIS2). ENISA's AI cybersecurity framework complements that legal context with three layers: cybersecurity foundations, AI-specific cybersecurity, and sector-specific cybersecurity for AI (ENISA: Multilayer Framework for Good Cybersecurity Practices for AI).

Applied to the project, these European perspectives connected baseline cloud controls—identity, access, encryption, logging, supplier risk, incident response, and resilience—with AI concerns such as model and data provenance, misuse, untrusted inputs, output handling, monitoring, and changes in model behaviour. NIS2 applicability depends on the organisation and sector; the project used the control areas as relevant security context rather than assuming universal legal scope.

The result was not a single security pattern for every AI service. It was a decision method that adjusted controls to the data, architecture, provider, purpose, and risk of each use case.

AI-provider and GDPR assessment

Provider assessment addressed both technical safeguards and the provider's role in processing enterprise information. The review distinguished between data sent to a service for inference, data retained for operations, and data that might be used to improve a model. It also considered deletion, transparency, subcontracting, international transfers, and the client's ability to retrieve or remove its information.

The EDPB's Opinion 28/2024 addresses GDPR questions concerning personal data in AI models, including anonymity, legal bases, and the consequences of unlawfully processed training data (EDPB Opinion 28/2024). The project's assessment process used such regulatory questions to identify when specialist review and stronger evidence were necessary.

This approach avoided two unsafe shortcuts: assuming that a well-known provider was automatically suitable, or assuming that technical security alone established GDPR compliance.

Workshops and organisational introduction

The analyst worked across management, business units, data architecture, BI, data governance, information security, and privacy. Workshops turned broad policy goals into decisions that teams could apply.

Architecture and data workshops mapped information flows and ownership. Governance sessions defined classification criteria, responsibilities, and approval stages. Provider and security workshops translated cloud and AI risks into assessment questions and controls. Management sessions clarified risk acceptance and escalation. Enablement sessions introduced the policies and showed delivery teams how to prepare evidence for review.

Communication was essential because the framework affected both strategic and operational decisions. Management needed a concise view of material risk and accountability. Delivery teams needed concrete requirements, templates, and decision paths. Business units needed to understand why certain data uses required additional safeguards or could not proceed in their original form.

What the project delivered

The completed engagement produced an integrated governance product and operating model comprising:

an enterprise AI-governance strategy and policy set;
rules for acceptable use of AI systems and enterprise data;
a sensitive-data classification and handling model;
data-security and privacy concepts for BI, cloud, and AI solutions;
a structured AI-provider and platform assessment;
GDPR, AI Act, and compliance screening with escalation criteria;
approval processes for using enterprise data in AI systems;
BI data-governance roles and decision structures;
documented responsibilities across business, architecture, BI, security, privacy, and management;
review and reassessment triggers across the AI lifecycle; and
workshop, communication, and enablement materials for introducing the policies.

Together, these outputs gave the client a controlled route from an AI idea to an approved and maintainable enterprise solution. The delivered product was not simply a policy document. It was the combination of architecture insight, data classification, provider due diligence, security controls, compliance screening, accountable decisions, and organisational adoption.

Conclusion

The AI Trust & Data Access Framework made governance part of how BI and AI solutions were designed and operated. It connected sensitive-data handling with architecture, cloud security, provider assessment, GDPR, the EU AI Act, and management accountability.

Most importantly, it answered the operational question at the centre of enterprise AI: not only whether a technology could use a dataset, but whether the organisation had a documented purpose, suitable controls, accountable approval, and a process for keeping that decision valid over time.