For enterprise leaders, the problem with AI isn’t ambition or imagination. Most organizations know exactly what they want AI to do: improve decisions, optimize operations, reduce risk, and surface insights faster. The real challenge is structural and unavoidable.

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Enterprise data will never be fully centralized. Compliance requirements, regulatory boundaries, data residency laws, and organizational realities ensure that silos persist. Some data must remain isolated by design. Some data lives in operational systems that cannot be moved. Some data is generated in real time and loses value the moment it’s delayed. This isn’t a failure of architecture; it’s the operating reality of modern enterprises.

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As a result, critical data is inherently distributed. It spans data lakes and warehouses, edge environments like factories, retail networks, and logistics systems, regulated applications, and real-time operational streams that must be acted on immediately, often before centralization is even possible.

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This creates a fundamental tension. Centralizing sensitive or regulated data expands the threat surface, increases compliance risk, and erodes control. But limiting AI to sanitized, partial, or delayed datasets strips away the context needed to produce meaningful outcomes in production.

Both paths lead to the same problem: decisions based on what is visible, rather than what is true. 

As AI becomes embedded in operational and strategic decision-making, this gap compounds across the organization, from the engineer building the model, to the analyst validating the output, to the executive acting on the result. As Dr. David Bauer, Axonis CTO and Technical Founder, puts it: “If you’re only working with part of your data, you’re only seeing part of the truth.”

What Data Level Security Actually Means

Dr. Bauer explains that data level security implies that there are portions of the data that you may not have access to at a very fine granularity. And this level of control is fundamental to making AI usable in enterprise environments.

Axonis Data Level Security enforces policy at this granularity, down to individual data cell, so access and use are governed precisely where sensitivity exists, rather than at the system or dataset boundary.

How Axonis Security Works in Practice

Data level security is enabled by Axonis’ federated AI architecture, which deploys federates alongside the data itself. A federate is the Axonis execution environment that runs next to a data source, whether that data resides in a data lake, an operational system, at the edge or in a closed environment.

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Rather than moving data into a central system, the federate brings AI workflows to the data and enforces policy continuously at runtime.

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This ensures security controls persist beyond initial access. Policies are evaluated as data is accessed, transformed, and used for learning, protecting sensitive attributes throughout feature engineering, model training, and inference. As Bauer notes, “It’s not really just a matter of redacting data from people. It’s when you have to build capabilities off of that data.”

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Because enforcement happens at the federate, different portions of the same dataset can safely be used in different contexts. Some data elements may participate in federated model training, others may be restricted to local analysis, and others may remain fully isolated. There are no copies of your data or a breach of compliance.

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By binding fine-grained policy enforcement to where data actually lives, Axonis enables AI systems to operate across centralized, distributed, and real-time environments while preserving security, governance, and trust.

Fixing Hidden Data Blind Spots

One of the biggest blind spots in enterprise AI initiatives is underestimating where sensitive data exists. It does not live only in customer databases or formally regulated systems.

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Sensitive information frequently appears in:

• Application and infrastructure logs
• Operational telemetry
• System metadata and configuration data

Bauer puts it plainly: “People don’t realize how pervasive sensitive data is. Even when you’re looking at something as simple as system logs, you’ll often find names, addresses, phone numbers, IP addresses, authentication tokens, and other sensitive details.”

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Treating these sources as low risk either prevents organizations from using them effectively or, worse, exposes them to unintended leakage. Axonis Data Level Security allows these data sources to be analyzed safely, enabling operational insights that would otherwise remain inaccessible.

Where Data Level Security Shows Up in Practice

The need for data level security is not limited to highly regulated datasets or obvious compliance use cases. It emerges wherever organizations need to apply AI across distributed, operational, and mixed-sensitivity data, conditions that increasingly define modern enterprises.

Operational and Edge Environments

In environments where data is difficult or impossible to centralize, such as factories, warehouses, retail networks, and field operations, systems must operate with limited connectivity, constrained resources, and strict security requirements. Data is generated and acted on locally, often in real time.

Axonis approach to security also enables collaboration across organizational boundaries, between business units, partners, and ecosystem participants, without pooling raw data.

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In one example described by Bauer, a global manufacturer worked with a network of retailers and partners to improve customer experience and supply chain efficiency. Each participant retained control over its own systems and data. Using federated learning with data level security, AI models operated across the ecosystem, improving outcomes while preserving data sovereignty and trust.

You can read more about the significantly improved business outcome in our case study.

Regulated and Compliance-Driven Environments

In regulated industries, the challenge is rarely a lack of data. It is the ability to use that data without violating financial controls, healthcare privacy requirements, or geographic data-residency mandates. Financial institutions, healthcare providers, and global enterprises operate across datasets that combine highly sensitive information with data that can be more broadly analyzed, often subject to overlapping regulatory regimes.

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Axonis Data Level Security is designed for these environments. By enforcing policy at a fine granularity—down to individual data elements—Axonis allows AI workflows to operate on permitted attributes while ensuring restricted data remains protected and governed according to regulatory and jurisdictional requirements. This includes constraints around financial data access, protected health information, and geographic data sovereignty.

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See our case study on scaling AI operations for healthcare and financial institutions.

From Insight to Production

Across these use cases, the common thread is the ability to move from insight to action without breaching security or governance. By connecting directly to production data at its source, Axonis enables models to be trained and deployed without months of re-engineering.

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If your data is distributed, regulated, and real-time (and it is), data-level security isn’t optional. It’s the foundation for AI you can trust in production.

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Let’s talk about what that looks like in your environment.

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