When Gartner analysts urged enterprises to “acquire digital tokenization,” it wasn’t hyperbole. It was a wake-up call for anyone still treating data security as an afterthought in their AI strategy. 

Not because the idea was new, but because someone finally said out loud what everyone in the enterprise AI space has been quietly realizing: data sovereignty can’t be protected by policy alone. 

In an era when agentic AI systems are rewriting how businesses operate, protecting data is no longer enough. You now have to protect the models, the results, and the relationships that AI depends on. And the key to doing that, Gartner argues, lies in tokenization, the most underappreciated weapon in the sovereignty arsenal. 

The Golden Path to Value 

The theme of Gartner’s 2025 Opening Keynote was “Walking the Golden Path to Value.” The idea: organizations are not limited to two options; charging recklessly into AI adoption and hoping nothing breaks or avoiding AI entirely in fear of compliance failure. 

There’s a middle path. 

Gartner calls it the Golden Path to Value, a balanced, deliberate route to AI transformation that aligns innovation with governance, security, and sovereignty. Their newly published Agentic Compass provides a structured way to stay on that path, mapping business outcomes to the right agentic capabilities and vendor models. 

It’s a framework built to cut through the noise of an AI market now crowded with more than 1,000 vendors, each claiming to offer “agentic intelligence.” But beneath all the market speak, one truth remains constant: without trusted data, you have no AI success, let alone sovereign AI. 

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The Rise (and Risk) of AI Sovereignty 

For decades, enterprises have obsessed over data sovereignty, keeping data within national borders, under the jurisdiction of local laws. 

But AI changes the rules of engagement. Once data is trained into a model, the question of sovereignty shifts from where data is stored to how it’s used, governed, and expressed through the model’s behavior and outputs. 

Gartner warns that by 2027, 35% of countries will be locked into region-specific AI platforms using proprietary contextual data. That’s a staggering shift. For global enterprises, it means potential disruption across supply chains, partner ecosystems, and even customer access. For regional players, it risks exclusion from larger networks, AI colonialism in the making. 

That’s why Gartner’s Darrell Plummer delivered perhaps the most memorable line of the entire conference: 

“Acquire a digital tokenization solution to anonymize data so the real data doesn’t leave your shores—even inside a model.” 

This isn’t a compliance checkbox. It’s a survival strategy. 

Tokenization: The Middle Ground Between Control and Capability 

Tokenization replaces sensitive values, like names, account numbers, or payment details, with non-sensitive tokens that preserve the same format and utility for systems and analytics. The original data is securely stored in a controlled vault, and only authorized processes can reverse the mapping when needed. 

This allows data to move freely through analytics, AI pipelines, and partner ecosystems without ever exposing what’s real. Tokenized data keeps your systems interoperable and your insights flowing, all while keeping personally identifiable or regulated information fully protected. 

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That’s what makes tokenization a cornerstone of AI sovereignty. It gives you the power to train, query, and collaborate on data without surrendering control over it. It’s the middle ground between the two extremes mentioned earlier: 

• The “fingers-crossed” camp: racing into agentic AI and leaking sensitive data into opaque models. 

• The “fearful-freeze” camp: avoiding AI entirely to sidestep compliance and sovereignty risk. 

Tokenization allows both innovation and control, a way to advance confidently while staying compliant and sovereign. 

The Agentic Compass Meets Sovereign AI 

Gartner’s Agentic Compass is more than a procurement guide; it’s a strategic map for navigating the complexity of agentic AI ecosystems. 

It encourages organizations to assess seven dimensions of use-case sophistication (complexity, autonomy, volatility, data richness, personalization, risk, and collaboration) and match them against seven corresponding capability dimensions (goal orientation, autonomy, perception, reasoning, tool use, learning, and memory). 

Here’s where tokenization quietly underpins the entire model: 

• Data Complexity: As AI ingests multi-modal, high-volume data, tokenization ensures sensitive components stay sovereign and structured. 

• Criticality of Error: In high-risk use cases—finance, healthcare, infrastructure—tokenization provides a built-in safeguard against data exposure. 

• Human-AI Collaboration: Trust between humans and AI depends on transparency. Tokenization allows explainability without revealing secrets. 

In other words, tokenization is not just a security control; it’s the connective tissue that makes the Agentic Compass actionable. It enables safe data flow between agents, systems, and regions while preserving performance and compliance.  

The Sovereignty Spectrum 

AI sovereignty isn’t binary; it’s a spectrum. On one end lies complete isolation: models trained solely on local data, offering security but sacrificing scale. On the other lies reckless openness: everything shared, everything vulnerable. 

Tokenization allows for something far more powerful, selective permeability. You decide which data stays local, which crosses borders, and under what conditions it can be re-identified. 

It’s sovereignty with scalability. Governance with agility. Protection with purpose. 

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The ALTR Perspective 

At ALTR, we see tokenization not as a feature, but as an architectural pillar for the next decade of data governance. It’s how organizations keep sensitive information sovereign while still enabling collaboration, analytics, and AI-driven innovation. 

We also deliver Format-Preserving Encryption (FPE), which is often what people mean when they think of tokenization. Both approaches share the same intent: to keep data protected, private, and usable wherever it needs to flow. Whether that protection comes through substitution or format-preserving transformation, the outcome is the same; sensitive data that retains its utility without sacrificing sovereignty or speed. 

That’s the foundation of ALTR’s philosophy: security without friction. Our technology allows data to move safely between systems, clouds, and AI environments without breaking applications, disrupting workflows, or slowing innovation. 

Because true sovereignty isn’t about where your data sits, it’s about how confidently it can move. 

Wrapping Up 

Gartner’s keynote made one thing clear: the age of agentic AI demands a new kind of trust infrastructure. Firewalls and encryption keys won’t cut it. The world’s most valuable resource—data—needs protection that moves with it. Tokenization and Format Preserving Encryption are that protection.