Private AI: from sovereignty obligation to business advantage

Across Australia and New Zealand, data sovereignty is shifting from a technical consideration to a strategic business priority. As organisations accelerate AI adoption, regulators, customers and boards are deep-diving into issues such as where sensitive data lives, who controls it, and how AI can be deployed at scale without diluting accountability for increasing regulatory, reputational and operational risk.

Recent incidents have reinforced how quickly trust can be eroded when governance breaks down. In March last year, a contractor working on the NSW Government’s Resilient Homes Program uploaded the personal information of up to 3000 individuals into a public generative AI tool, triggering a data breach investigation. The incident highlighted the risks associated with using public AI services without appropriate safeguards. The issue was not the use of AI itself, but the absence of a governed, private environment with clear data-handling policies, access controls and accountability.

Other high-profile data incidents, such as the Manage My Health data breach in New Zealand, have also underscored the fragility of public trust when sensitive information is mishandled.

This reinforces a broader trend. While Australia’s evolving Privacy Act reforms and New Zealand’s strengthened privacy regime are tightening requirements around personal data handling, cross-border transfers and breach accountability, these measures alone are not enough. The regulatory direction is clear: stronger security, higher penalties and greater expectations of demonstrable control — making robust governance more critical than ever.

Against this backdrop, private AI is a new operating reality — one where enterprises must harness AI safely, securely and responsibly, while satisfying boards’ expectations for costs, accountability, regulatory compliance and risk oversight.

Private AI: a strategic asset for ANZ boardrooms

For ANZ enterprises, private AI is fundamentally about control and confidence. It enables organisations to deploy advanced AI within governed environments — whether on-premises, in a sovereign cloud or in hybrid architectures — while retaining ownership of data, models and intellectual property. As AI moves into core operations, boards are no longer asking whether AI is interesting — they are asking whether it is safe, compliant, explainable and delivering measurable business value.

This phase of AI adoption is defined not by who experiments the fastest, but by who can productionise AI responsibly. Organisations that embed governance, sovereignty and trust into their AI strategies will be the ones able to scale with confidence, while others remain stuck in pilot purgatory.

Financial services offer a useful case study. Under CPS 230, institutions must demonstrate resilience across critical operations, including dependencies on third parties and technology platforms, also bringing AI systems into the scope of regulatory oversight. However, similar pressures exist elsewhere: healthcare providers managing sensitive patient data, utilities overseeing critical infrastructure and retailers balancing personalisation with privacy expectations.

In each case, private AI helps close the gap between innovation, ambition and governance reality.

From experimentation to economics: the rise of AI economics

Across ANZ, business leaders are increasingly focused on the economics of AI. After years of pilots and proofs of concept, the conversation has shifted to actual business value: productivity gains, cost reduction, revenue impact, risk mitigation and customer experience.

This is where private AI offers a structural advantage. By bringing AI closer to trusted enterprise data — rather than moving sensitive information into external environments — organisations can improve model accuracy, reduce compliance exposure and accelerate time to value. They also avoid hidden costs associated with uncontrolled AI and cloud infrastructure consumption, data leakage, remediation, regulatory breaches and loss of customer trust.

The result is not just better risk control, but a more scalable and economically sound AI foundation, supported by:

• Predictable AI costs: full control of infrastructure and AI execution guardrails, minimising unexpected costs and maximising ROI.
• Regulatory and governance alignment: stronger auditability, model oversight and control in line with tightening ANZ regulatory expectations.
• Data sovereignty and privacy assurance: reduced exposure from cross-border processing and third-party dependencies.
• Enterprise-grade AI at scale: moving beyond pilots into operational systems that deliver impactful business outcomes.
• Sustainable competitive advantage: protecting proprietary data and intellectual property while enabling innovation.
   

In a market where trust is an increasingly powerful differentiator, governance is no longer simply about compliance but a strategic economic asset.

Towards more responsible and controlled AI

Private AI is not about slowing innovation. On the contrary, it makes innovation sustainable in environments where regulatory scrutiny, customer expectations and operational risk continue to intensify.

For Australian and New Zealand enterprises, the question is no longer whether to adopt AI, but whether they can do so in a trusted and governed way, delivering real business value — all while meeting executives’ expectations for accountability, compliance and risk oversight. Organisations that invest in controlled, governable AI foundations will be better positioned to scale with confidence — turning trust into a lasting competitive advantage.

Image credit: iStock.com/cherdchai chawienghong