Bold for AI: Protecting Data Across Every AI Interaction on the Endpoint

Work has converged on the endpoint. Every other layer of the security stack sees only a slice: the network sees the wire, cloud tools see sanctioned apps, DSPM sees cloud storage. But data doesn't stay in any of those layers. Across applications, clouds, browsers, and AI tools, the data that matters converges on the device.

AI has turned that convergence into more than just an AI governance problem. It’s a data protection problem. The most sensitive data an organization has now moves through prompts, uploads, and autonomous agents all day. And it moves through a layer most tools were never built to see. 

That's the gap we're closing: Bold now protects data across the full range of AI interactions on the endpoint.

Why traditional DLP is no longer enough

DLP was built for a world where data left through a known set of channels: email, uploads, the network. It watches those channels and matches against patterns. That model held when the exits were predictable, and the data had a recognizable shape.

AI broke both assumptions. The exits are now elusive: prompts, agent tool calls, and MCP payloads, none of which pass through the gateways DLP inspects. And the data at risk (proprietary code, internal models, business context, etc.) has no fixed patterns to match. Against agents, copilots, and assistants, DLP is no longer enough. Not because it was built wrong, but because it was built for a different problem than the one AI created.

The tools meant to govern AI have the opposite limitation. They can flag that someone used an AI tool, but not what data was involved or whether it mattered. Visibility into AI usage isn't the same as understanding the data moving through it.

The problem is distinguishing routine from risky

Blocking AI outright is no longer an option. But allowing it blindly is how data leaves, and it’s already happening. A study from the Cloud Security Alliance showed 65% of enterprises reported an AI-related incident in the past year, and 61% of those incidents involved data exposure. Most AI incidents involved the endpoint.

So the challenge becomes distinguishing between a legitimate data interaction and a dangerous one.

And if we’ve learned anything over two years of building Bold, it’s that the distinction between safe and not almost never lives in the action, the user, or the data alone. The same upload, the same prompt, the same file read can be routine or a serious risk depending on context: what the data actually is, who is moving it, where it's going, and whether that destination is sanctioned.  You can't tell them apart without understanding all of it at once.

Why this has to happen on the device

Every AI interaction passes through the endpoint before it goes anywhere. That makes the device the one place all the context exists together: the file being read, the account in use, the destination, the action, at the moment it happens, before any of it is reduced to a log or an API call somewhere downstream.

Off the device, that context is already gone. Network and cloud tools see a normalized event after the fact, if they see it at all. Desktop AI apps encrypt their own traffic and bypass the proxy entirely. Agents dial their own connections and run their own commands. The only place to see what data an agent actually read, or which account was really behind a prompt, is the endpoint where it happened.

That’s why Bold runs its AI directly on the device. It reads what the data is, not just what pattern it matches, and it weighs the account, the destination, and the intent behind every action, all locally and in real time. That's what makes it possible to allow the AI use that helps and stop the use that puts data at risk, in the same moment, without a round trip to the cloud.

What Bold now covers

The new layer spans the full AI interaction surface, every path sensitive data takes into and through AI on the device.

• Web-based AI: Prompts, clipboard activity, and uploads into copilots and chat tools, tied to the account behind them, so a corporate login and a personal one are never treated the same.
• Desktop AI apps: Clipboard, uploads, and local file access, plus the payloads passed through MCP, the calls a desktop assistant makes to read a file or pull a record that a cloud gateway never sees.
• AI agents and CLI: Full agent sessions: the files they read, the tool calls and MCP connections they open (local and remote), and the commands they run on a user's behalf, often with no person at the keyboard at all.

Across all of it, Bold's on-device AI reads and judges each interaction locally, in real time, keeping good AI use flowing and stopping the risk, without data ever leaving the endpoint.

The power of Bold’s unified, on-device context

Because the verdict depends on context, the same data movement can be handled three different ways. 

Take a developer sending source code to Claude:

• Personal account in a sanctioned tool: The organization approves Claude, but the developer is logged into a personal account with no data processing agreement behind it. The app is fine; the account is the problem. Bold sees the account identity behind the action and can block or redirect it, something a vendor log, which only sees the corporate tenant, never could.
• Shadow AI the organization never approved: The same code goes to an unsanctioned AI assistant, a tool IT has no relationship with, no data retention terms, and no oversight. No gateway or SaaS console covers software that the organization never sanctioned. So only the endpoint can see it.
• Corporate account in a sanctioned tool: The tool is approved for corporate usage and the interaction is likely legitimate. Regardless, Bold still adds the lineage and classification that vendors can't: which internal source the data came from, whether an agent read a credential file mid-task, what the payload of a local MCP call actually contained, and more.

This is what it takes to protect data in the AI era: not blocking AI, and not hoping employees use it safely, but understanding each interaction well enough to tell the difference, in the moment, before anything leaves. It's how security teams get to say yes to AI without giving up control of their data.

The endpoint was always where the data lived. Now it's where AI meets it, and where that meeting is finally protected.

Schedule a demo to see it in action, or stop by our Black Hat booth next week!

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