Data Security & Governance in the AI Era

By Arsalan Khan, Chief Security Officer, Graphiant

As the Chief Security Officer at Graphiant, I’ve spent considerable time observing how rapidly enterprises are embracing AI, and how that rush is introducing new, complex risks around data security and governance. In this blog, I’ll dive deeper into what those risks are, why the traditional controls may not suffice anymore, and why the network is the right place to address AI data security and governance challenges so you can confidently leverage AI without compromising your data.‍

The New Landscape: AI + Data = New Risk

When your organization shifts toward AI use-cases, you are inevitably dealing with large volumes of data, much of which moves across systems, clouds, partners. That data movement, especially when it’s enabling AI workloads, introduces new exposure.

• Misconfigurations or human error can lead to inadvertent data loss or leakage.
• Malicious actors can exploit the increased data flows and the “legitimacy” of AI models to camouflage exfiltration.
• The need to push data into cloud training environments blurs the line between what’s acceptable and what’s risky.

In short: the speed and breadth of AI adoption fundamentally change the attack surface and the governance challenge.

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Key Requirements That Shift with AI

Let’s break down the main security and governance requirements that change when you bring AI into the mix, and what you should demand of your systems.

End-to-end Encryption & Data in Motion

When this part of the process is overlooked, you create a “single point of exposure.” If you decrypt all traffic for inspection in one place, a bad actor only needs to compromise that single point.

We believe strongly that data in motion must remain encrypted end-to-end, including across our infrastructure. Even within Graphiant’s network fabric, we architect so that we cannot see your raw payloads.
This principle aligns with “data-centric security” approaches that emphasize protecting the data itself, not just the perimeter.‍

Centralized Visibility & Governance Enforcement

With AI, many applications and data flows are highly distributed often crossing clouds, geographies, partner networks. Relying on each application layer to enforce governance is not scalable or reliable. Instead, you need a network-layer vantage point that gives you:

• A “single pane of glass” view of data sources, consumers, flows.
• The ability to audit and drill into how data is moving (which path, which app, which partner).
• Centrally-enforced policy control (for example, restrict any data leaving a given geography or partner boundary).

Partner Management, Auditing & Revocation

Sharing data with trusted partners is often necessary for AI use-cases(training, collaboration, etc.). But with sharing comes responsibility: you must ensure that data is shared only with authorized partners, and that you have the historical traceability (audit logs) and revocation capability.

Too often, governance breaks down because once data leaves your direct control, you lose visibility. AI makes this worse because large data transfers may look “legitimate” even if they’re anomalous.

Compliance, Geographies, and Data Sovereignty

You may have secured your network well but that does not automatically satisfy governance or regulatory requirements. The rules you face will vary by region (for example, California vs. UK vs. Australia).

Thus, you must build for:

• Geographical enforcement (e.g., data cannot leave North America).
• Auditability and report-generation so you can show compliance to regulators or partners.

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Why Application Layer Alone Can’t Solve This

I’ll say this clearly: relying entirely on the application layer to enforce governance is inadequate. Applications tend to be highly distributed, each with its own context, permissions, and “blind spots.” You lose centralized policy enforcement, you lose consistent visibility, and you lose control.

By contrast, the network layer is uniquely positioned to provide:

• A unified vantage point across all traffic and applications.
• Policy enforcement that works consistently across the enterprise.
• Encryption, data flow control, geo-boundaries, partner boundaries, all at scale.

That’s why at Graphiant, we’ve chosen to build our governance and security capabilities deeply into the network fabric rather than as a bolt-on at the application layer.

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Graphiant’s Approach: Built-in Data Governance for AI

Let me walk you through how Graphiant’s solution addresses these challenges in practice.

Native Integration

We’ve built compliance, governance and security capabilities natively into the fabric, meaning you don’t have to adopt yet another product layered on top of your network. This reduces complexity, improves adoption, and ensures consistent policy enforcement.‍

Key Capabilities

There are three major capabilities that make our governance solution ready for the demands of AI workloads:‍

1. ‍Partner Data Sharing & Auditing‍
   
   • You define which partners can receive data, in which geographies.
   • Full auditing of what each partner consumed, when, and via which path.
   • Ability to revoke access whenever required.
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2. Auditability of Data in Motion‍
   
   • You see, for every data flow: source, destination, path, application context.
   • Via our visibility capability you can trace and monitor traffic behavior.
   • This enables you to compute threat scores and detect anomalous consumption.
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3. Policy Enforcement at Network Layer‍
   
   • Once visibility is established, you can apply policy: e.g., “Data from App X must not leave Region Y”, or “This application cannot communicate with that partner.”
   • Enforcement happens automatically in the fabric, while maintaining encryption end-to-end.‍

The Benefit

With this approach you get:‍

• ‍Security: encrypted, controlled data flows, partner management, threat detection.‍
• Governance & Compliance: centralized visibility, consistent policy enforcement, audit logs, reports for regulators/boards.‍
• Readiness for AI: ability to handle large data transfers, distinguish legitimate from potentially illegitimate flows, and enforce geographic/partner boundaries.

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What You Should Do Now

Whether you’re just starting your AI journey or already deep in it, here are three immediate actions I recommend:‍

1. ‍Map your data flows: Understand where your data lives, how it moves (especially for training AI), who accesses it, and which partners are involved.‍
2. Enforce encryption and least-privilege access: Ensure all data in motion is encrypted end-to-end. Limit access to only those applications/partners that need it. Also, consider how your network layer can enforce this rather than relying on apps alone.‍
3. Implement centralized governance and monitoring: Don’t let each app own its own policy. Instead, build a governance model that gives you a holistic view across the enterprise; data sources, sinks, partners, geographies, flows, threats. Generate regular reports, monitor anomalies, and be able to respond quickly.

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Final Thought

In today’s AI-driven world, data is both a tremendous asset and a potential liability. If you treat governance and security as afterthoughts, you’re exposing yourself to risks, such as breaches, non-compliance, partner misuse, data exfiltration.

At Graphiant, we believe the most dependable model is to build security + governance by design, at the network layer, with strong encryption, centralized visibility and partner control baked in. If you adopt that mindset, you’ll not only reduce risk but also unlock AI’s full potential confidently, compliantly, and securely.

Thank you for reading. Please feel free to reach out if you have questions about how this works in practice or how you can apply it in your environment.

A huge thanks to the delegates at Networking Field Day #NFD39 who attended our presentation where we talk about AI, security, and compliance. If you’re interested in learning more, watch our presentation with 3 demos on Business to Business Data Exchange, Data Assurance, and Gina AI on YouTube here.

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