Discussions around artificial intelligence increasingly focus on speed, scale, and strategic advantage. These are important debates. But they risk overlooking a more fundamental issue—one that ultimately determines whether AI strengthens security or undermines it.
AI does not create intelligence on its own. It amplifies what it is given.
And what it is given is data.
As governments deploy AI across defense, intelligence, border security, and public services, the quality, integrity, and governance of underlying data become decisive. Without trusted data, even the most advanced AI systems produce unreliable outcomes. In national security contexts, that is not simply a performance problem—it is a strategic liability.
Untrusted Data Leads to Dangerous Outcomes
The failure rate of AI initiatives remains high, particularly in public sector and defense environments. The causes are rarely algorithmic sophistication. They are structural: fragmented data, weak governance, unclear accountability, and inconsistent security controls.
A report from the Committee of Public Accounts in the UK House of Commons noted, “Out–of–date legacy technology and poor data quality and data–sharing is putting AI adoption in the public sector at risk.”
For intelligence and defense leaders, the implication is clear. Untrusted data leads to untrusted intelligence. And untrusted intelligence leads to flawed decisions—sometimes at speed, sometimes at scale, always with consequences.
In a geopolitical environment defined by ambiguity, disinformation, and contested narratives, decision advantage depends on confidence in inputs. That confidence cannot be assumed. It must be engineered.
Cyber Risk Has Moved Up the Value Chain
Cyber threats are no longer limited to data theft or service disruption. Increasingly, they target the integrity of data itself—poisoning datasets, manipulating inputs, or exploiting opaque AI pipelines.
This represents a shift in the threat model. The objective is not just to deny access, but to distort reality.
In such an environment, cybersecurity and AI safety converge. Protecting systems is not enough if the data they rely on cannot be verified, traced, and governed. Security strategies that fail to address data provenance and integrity will struggle to keep pace with modern threats.
Why Trusted Vendors Matter More Than Ever
Trust in emerging technologies does not emerge organically. It is built through governance, transparency, and accountability—across the entire technology supply chain.
This is where the concept of “trusted vendors” becomes strategically relevant. Trusted vendors are not defined solely by technical capability or market position. They are defined by their commitment to robust risk management, clear governance standards, transparent operations, and long-term accountability.
For governments, this is not about limiting innovation. It is about ensuring that innovation delivers secure and ethical outcomes. As AI systems become embedded in national security workflows, vendor trust becomes inseparable from system trust.
Trust Is a Policy Choice, Not a Technical Feature
Too often, trust is treated as a byproduct of technology adoption. In reality, it is the result of deliberate policy decisions.
Regulatory frameworks, procurement standards, and public-private partnerships all shape the trustworthiness of national digital ecosystems. Efforts around data sovereignty, supply chain security, and cybersecurity regulation reflect a growing recognition that trust must be designed into systems from the outset.
This is not about technological isolation. It is about ensuring that openness is matched with responsibility—and that interdependence does not become vulnerability.
Building Intelligence Systems Worth Relying On
As AI reshapes the security landscape, the question is not whether governments will adopt these technologies. They already are.
The real question is whether those systems will be worthy of reliance under pressure.
That depends less on algorithms and more on data—how it is governed, secured, validated, and recovered. Trusted intelligence begins long before insights are generated. It begins with disciplined choices about data, vendors, and governance.
In a world where decisions are increasingly automated and accelerated, trust is not a soft value. It is a hard security requirement.
Intelligence Systems Worth Relying On
As AI reshapes the technology landscape, the question is not whether governments will adopt the
And it starts with trusted data.
César joined NetApp following a long career at Microsoft, where he served as President of Microsoft Asia Pacific, President of Microsoft Latin America, and Global Corporate Vice President of the brand. Having walked in the shoes of NetApp’s enterprise customers, he brings a customer-centric perspective to all he does as president. César serves as the non-executive director and Chairman of the ESG committee at Gestamp, an international organization dedicated to the automotive industry; as an advisory board member of Georgetown University’s McDonough School of Business; and as an international advisory board member of IESE Business School – University of Navarra.