In recent years, AI has been advancing faster than regulation can catch up. But the gap is closing. New regulatory frameworks are being introduced and implemented throughout the EU, China, and South Korea, while in the US, the American AI Action Plan focuses on accelerating innovation by cutting red tape.
In their wake, organisations are navigating an increasingly complex compliance landscape as they work to unlock the full potential of AI. Progress now depends on moving beyond simple box-ticking and building trust through data sovereignty – ensuring that data remains under control, transparent, and accountable at every stage of the AI lifecycle.
The EU AI Act takes shape
The EU AI Act marks a historic step as the first comprehensive attempt by a major governing body to regulate artificial intelligence. With most provisions set to take effect in August 2026, the Act outlines clear expectations for how companies design, deploy, and manage AI systems within the European Union.
At its core, the Act is a risk-based framework that classifies AI systems according to their potential impact, with requirements around transparency, verification, and human oversight. The higher the risk, the stricter the rules. Organisations that fall short, particularly in high-risk applications, face significant financial and reputational consequences.
The message is clear: compliance alone isn’t enough. Meeting the Act’s growing privacy expectations and achieving true data sovereignty now means going beyond generic public AI models.
Why data sovereignty matters
Data sovereignty is becoming one of the defining principles of digital trust. It’s the idea that data collected in a particular region must remain subject to the laws of that region. But in practice, it means much more than legal compliance. It’s about control, transparency, and accountability in how data is managed, stored, and shared. Maintaining control over data enables organisations to provide traceable evidence to regulators. Losing that control, by contrast, can result in operational restrictions or even exclusion from global markets.
It’s no surprise, then, that security and compliance are now top of the agenda for business leaders. According to a Gallagher survey, more than two in five business leaders have had to reassess their security measures surrounding AI to ensure compliance and reduce security risks.
Establishing true data sovereignty is one of the most effective ways to address these risks. It ensures that sensitive information is never exposed to third parties, never used to train external AI models, and always remains within compliance boundaries defined by current and future regulations.
How private AI provides control
While governance principles such as human oversight and fairness apply broadly, private AI offers an extra layer of control that helps organisations meet the twin demands of security and compliance.
Partnering with a private AI vendor ensures sensitive information stays under your control. Unlike public AI, private models are trained exclusively on your data, meaning it’s never shared externally or used to enhance third-party systems. This approach enables tailored solutions aligned with regulatory expectations.
Private AI platforms keep data in-house, supporting compliance with the EU AI Act and GDPR through encryption, customer-managed keys, and granular access controls. Embedding AI within your infrastructure provides full visibility over data processing, reducing the risk of breaches or unintended exposure.
Non-compliance risks under the EU AI Act
Public AI models can create uncertainty around how data is used, increasing the risk of compliance gaps and making traceability a challenge. Without defined boundaries, data may be repurposed or exposed. This risk is especially pressing as the EU AI Act imposes stringent requirements on data privacy, risk management, and traceability.
Private AI solutions, on the other hand, meet these demands by keeping data and models confidential and under your control, with defined governance, enterprise-grade guardrails, and seamless integration with existing workflows. Properly implemented, they enable auditability, updatability, and data erasure — all key for compliance and avoiding fines of up to 35 million EUR or 7% of a company’s annual turnover, according to Article 99 of the EU AI Act.
Compliance and efficiency combined
Real-world examples show how private AI can deliver measurable results while strengthening compliance. Take the example of a Fortune 500 pharmaceutical firm, for instance, which faced major document-handling challenges that slowed regulatory processes. By integrating a private AI vendor directly into their workflow, the company streamlined processes and delivered process certificates with 99% accuracy, all while maintaining full compliance.
As well as delivering fast results, laying strong private AI foundations early enables companies to stay ahead of regulatory changes, making future transitions more straightforward and transparent for both the organisation and regulators. And at a time when regulatory concerns are front of mind, early adoption of compliance frameworks also helps strengthen stakeholder engagement and build customer trust.
Human oversight is more crucial than ever
Even as both public and private AI become more sophisticated, human oversight is essential. Keeping people in the loop allows organisations to intervene, correct errors, and maintain accountability. Embedding AI into business processes with clear governance frameworks can strengthen stakeholder confidence while supporting sustainable, compliance growth.
For organisations prioritising data control, private AI platforms take oversight a step further, providing full visibility into model behaviour, data usage, and decision pathways. This transparency ensures that human decision-makers can trust AI outputs while maintaining ultimate responsibility for outcomes.
