2 min. read

In the rapidly evolving landscape of artificial intelligence, organizations often view regulatory compliance as an obstacle to innovation and speed. However, this perspective overlooks a crucial reality: compliance frameworks like the EU AI Act are becoming powerful catalysts for implementing the very security practices necessary for responsible AI development. Far from being merely a box-ticking exercise, compliance is increasingly the driving force behind essential data governance that protects organizations while enabling sustainable innovation.

The European Union’s AI Act, the world’s first comprehensive AI regulatory framework, has established a new paradigm for how organizations must handle data throughout the AI lifecycle. Like the EU’s General Data Protection Regulation (GDPR) in 2018, the EU AI Act could become a global standard, determining how AI impacts many aspects of our lives. This isn’t just another compliance burden—it’s a fundamental reshaping of how organizations must approach data security in AI development.

What makes this regulatory shift particularly significant is its focus on data quality and governance as fundamental requirements for high-risk AI systems. Article 10 of the EU AI Act explicitly requires that “high-risk AI systems which make use of techniques involving the training of AI models with data shall be developed on the basis of training, validation and testing data sets that meet the quality criteria” and establishes specific data governance practices that organizations must implement.

The challenge is particularly acute in business-to-business relationships. Most B2B contracts today explicitly forbid shared data to be used for AI training purposes. This restriction isn’t arbitrary—it stems from the absence of robust traceability and accountability frameworks that could address the significant risks if sensitive data ends up in the wrong hands through AI training processes.

Organizations find themselves in a difficult position: they need diverse, high-quality data to train effective AI models, but they lack the security infrastructure and governance processes necessary to provide adequate assurances to their business partners. This leads to contractual prohibitions that limit innovation and the potential business value of AI.

Here’s where regulatory compliance becomes a powerful driver for better security practices. The EU AI Act’s requirements effectively mandate the implementation of security controls that address the very concerns preventing wider data sharing for AI development. Consider these key requirements:

The EU AI Act stresses “the need for well-defined and well-documented data collection and data preparation processing operations” covering different stages of the data lifecycle. This requirement directly addresses the provenance gap—organizations must implement systems to track where their training data originated, how it was transformed, and who has accessed it.

By implementing robust provenance tracking to meet compliance requirements, organizations simultaneously build the technical foundation necessary to provide assurances to business partners about how their data will be used and protected in AI training.

The regulations demand rigorous data governance practices that include technical security measures. For example, when handling special categories of personal data, Article 10 requires that the data “are subject to technical limitations in terms of re-use of the personal data as well as several privacy-related measures” and “are subject to appropriate measures to ensure all data processed is secured, protected, and authenticated via suitable safeguards”.

These requirements drive organizations to implement more sophisticated data security controls that protect not just personal data but all training data—creating the secure data environment necessary to earn trust from business partners.

Organizations must “draw up technical documentation to demonstrate compliance and provide authorities with the information to assess that compliance”. This documentation requirement creates accountability structures that benefit not just regulators but also business partners.

The ability to demonstrate, through documentation, how data is being protected and used throughout the AI development process becomes a competitive advantage when negotiating data-sharing agreements with partners concerned about misuse.

Organizations that approach regulatory compliance strategically will discover a virtuous cycle:

1. Regulatory requirements drive the implementation of robust data governance, security controls, and traceability mechanisms
2. These technical capabilities enable organizations to provide stronger contractual assurances to business partners regarding how their data will be used and protected
3. Increased trust leads to expanded data-sharing agreements and access to more diverse, higher-quality training data
4. Better training data results in more effective, less biased AI models that create greater business value
5. Proven compliance and security frameworks become competitive differentiators in the marketplace

The most forward-thinking organizations recognize that implementing strong data governance and security isn’t just about meeting compliance requirements—it’s about building the foundation for sustainable AI innovation that respects privacy, security, and contractual obligations.

As AI regulations continue to evolve globally, the organizations that thrive will be those that recognize compliance not as an obstacle but as an enabler of responsible innovation. By implementing the security controls, governance frameworks, and accountability structures required by regulations like the EU AI Act, organizations simultaneously address the very concerns that have limited data sharing for AI development.

In this new paradigm, regulatory compliance becomes the catalyst that transforms how organizations approach AI data security—moving from a reactive, minimal-effort approach to proactive governance that builds trust and unlocks new possibilities. The future belongs to those who recognize that in AI development, good security isn’t just about compliance—and effective compliance inevitably leads to better security.