Defence Australia is treating data and artificial intelligence as core elements of national security, with sovereign AI capability increasingly viewed as central to military readiness.
The shift is being supported by the One Defence Data program, which is designed to move Defence away from fragmented information environments and toward a connected data ecosystem capable of supporting faster, better-informed decision-making.
Data has become a growing focus across recent government strategic reviews and investment programs, with information now seen as a source of operational advantage.
Speaking at the Gartner Data & Analytics Summit in Sydney, Defence Australia chief technology officer Nasa Walton said the definition of military strength was changing.
“For centuries, military might has been measured in the volume of ships and planes,” Walton said. “What has been missing is how data has become a military strength and a military might. Especially over the last few years, with the onsets of sensors and information that we have in our capability, data has become a strategic advantage for Defence.”
Federated data, not one central repository
Defence’s challenge is not simply to centralise all information into a single repository. Instead, the organisation is building a federated architecture that allows data to be shared, trusted and accessed while remaining within operational systems where appropriate.
Modern defence assets, including aircraft, land vehicles and vessels, generate thousands of individual data points during operation. Replicating all of that information into one location is often impractical.
The One Defence Data environment is therefore intended to connect information sources and deliver relevant data to decision-makers when required.
The program supports command and control functions by ensuring information is secure, trusted, sovereign and available at speed. It is also increasingly being used to combine structured and unstructured data and allow information to be reused across multiple applications and analytics platforms.
Lessons for industry
The problems Defence is addressing are not unique to the military.
Organisations across financial services, emergency services, logistics and other sectors are facing similar challenges around connecting data, removing silos and ensuring technology investment delivers measurable outcomes.
Cloudera (NYSE:CLDR) ANZ chief technology officer Vini Cardoso said the role of technology leaders had broadened beyond platform selection and capacity planning.
“The role of the CTO these days is not just about selecting technology, not just about provisioning capacity, but making sure that every single investment that is made is measurable towards a proper outcome,” Cardoso said.
As AI adoption accelerates, both Walton and Cardoso said governance frameworks were becoming critical to successful deployment.
For Defence, that means using its own data assets to train and refine AI systems rather than relying solely on externally trained models. Walton said workforce capability was also central, with effective AI programs dependent on staff who understand data, algorithms and operational requirements.
Sovereign AI and trusted data
Data sovereignty remains a key priority for Defence.
While public AI tools have helped drive experimentation across many organisations, Defence requires much higher levels of assurance around data security, model behaviour and information integrity.
The organisation must protect sensitive information while also understanding how misinformation could enter AI systems and influence decision-making.
AI-assisted software development is creating additional considerations around intellectual property and copyright. Organisations need to understand how commercial models are trained and what material may have been incorporated into them.
Risk of falling behind
Cardoso said organisations risk falling behind if they fail to adopt AI, but moving too quickly without appropriate governance could create its own problems.
Regulated industries are increasingly looking for ways to deploy AI while retaining control over data, intellectual property and compliance settings.
“When you bring the models under your control, that can be in your data centre or in your cloud of choice, but it’s ring-fenced to your needs in your private network,” Cardoso said.
This approach is designed to reduce risks around data sovereignty, compliance and intellectual property leakage as organisations begin integrating AI into core business processes.
Recent high-profile cases of employees inadvertently exposing sensitive company information through public AI tools have reinforced the need for governance.
“No one wants to have a situation like that,” Cardoso said.
At the same time, Cardoso warned that excessive caution could leave organisations exposed to competitive risk.
“Organisations that don’t embrace AI will inevitably be left behind, and you’re going to become insignificant,” he said.
Governance before scale
Many organisations are still struggling with fragmented and poorly governed data environments, limiting their ability to move AI initiatives beyond pilots.
AI should not be treated as a shortcut for longstanding data management problems. However, AI can help improve visibility and governance when applied correctly.
Cloudera (NYSE:CLDR)’s data lineage capabilities, for example, use AI to help organisations understand where data originates, how it is transformed and where it flows across the business.
That visibility remains a major challenge for many enterprises, particularly where information is spread across departmental applications, shadow IT systems, shared drives and employee devices.
Cost and culture emerge as barriers
Beyond governance, cost management is becoming another obstacle as enterprise AI use expands.
Some organisations have underestimated the financial impact of large-scale AI deployments, particularly around inference costs and token consumption.
“I know organisations that have a yearly budget and they spend their budget in the first 2 months,” Cardoso said.
As AI use broadens across workforces, controlling usage and monitoring expenditure is becoming a growing concern for both technology and finance teams.
Cultural change may prove just as difficult.
While modern platforms can connect hundreds of systems and make data more accessible, organisations often struggle to overcome internal resistance to sharing information.
Successfully scaling AI will require more than technology investment. Businesses will need governance frameworks that encourage responsible data sharing, maintain safeguards and ensure AI adoption is tied to measurable operational outcomes.