Is this just DLP with an AI sticker on it?
No. Pattern-matching DLP cannot tell a draft email from a deal memo because prompts are unstructured and contextual. Static rules either flood teams with false positives or get ripped out entirely. We classify the meaning of the work, not the shape of the string. That is what lets us govern inline, where DLP can only monitor.
How is this different from Zscaler, Netskope, or any other SASE tool?
SASE inspects network traffic to known AI domains. Useful, but it misses everything that does not cross the network: Claude Desktop, Cursor, local MCP servers, embedded AI inside Canva or Salesforce, free-tier accounts on personal devices. Most shadow AI exposure happens on personal devices that never touch the corporate network, which is also where SASE has no jurisdiction. We sit on the device and inside the AI surface itself. That is why we can govern where SASE can only observe, and why we cover the agent layer SASE never reaches.
What about Microsoft Purview or other AI-aware DLP?
Purview gives you visibility inside Microsoft, on Microsoft tools, with Microsoft pattern matching. Real AI usage is not Microsoft-only. We see the full stack across vendors, including the long tail and the agentic surfaces, and we govern with intent classification rather than regex.
Can't we just whitelist the AI tools we've approved?
You can, and it's a reasonable starting point. The problem is that AI no longer lives only in the tools you evaluated. Google AI mode is built into Search. Salesforce Einstein runs inside your CRM. Copilot ships with every Microsoft 365 license. Canva, Grammarly, Notion, and most of your SaaS stack now have AI features that activate whether or not you toggled them on. Whitelisting governs the standalone tools you approved. It does not reach the AI embedded in the tools you already use every day.
What actually happens when an employee shares something they shouldn't?
Depends on what you want to happen. You can block in real time, warn the employee with context about why the action is risky, or log silently for security team review. Most customers start with warn-and-log during rollout, then move toward inline blocking for the highest-risk categories once they understand the patterns. The governance layer is yours to configure. We do not impose defaults that shut down legitimate work.
What about AI agents that act autonomously, not just a human typing into a chat window?
This is the problem most security platforms cannot see yet. When an agent reads a file, calls an API, writes to a database, and emails a summary, all without a human in the loop, there is no browser request to inspect and no prompt to classify at the keyboard. We govern at the MCP layer and at the tool surface, which is where agentic workflows execute. Policy follows the action, not the person.
How do you avoid this becoming employee surveillance?
HR, Finance, Ops, and Founders are excluded from reporting by design. Employee names can be masked in the portal. The dataset is sanitized and frozen. EU hosting is available on request. The design principle is that security teams need risk visibility, not a feed of individual employee behavior. We made the hard restraint choices in the product so you do not have to defend them in every internal review.
How fast is deployment?
Minutes. Roll out through Intune, JAMF, Kandji, or Group Policy. The browser extension covers all browsers and MCP gateway run on Windows, macOS, and Linux. No proxy redesign, no certificate gymnastics, no long onboarding. On day one you get a full inventory of AI tools in use across your organization. By the end of the first week, most security teams have a clearer picture of AI data exposure than they have had in years.
What surfaces do you actually cover?
Browsers (Chrome, Edge, Firefox, Safari, Arc, Brave, Vivaldi, Island, Genspark, Comet, Dia). Desktop AI (Claude Desktop, ChatGPT Desktop, Cursor, Windsurf). Agents and MCP (Claude Code, Cowork, custom MCP servers). Embedded AI (Canva, Grammarly, Google AI mode). Plus the long tail of 1,000+ web AI tools the catalogue updates every week.
Does this help with the EU AI Act, GDPR, or other regulations?
Yes, though compliance is a byproduct of good governance, not the other way around. The EU AI Act requires organizations to manage high-risk AI use and maintain logs of consequential AI-assisted decisions. GDPR creates exposure whenever personal data enters AI tools hosted outside the EEA. Our data classification and logging give you the audit trail, the data residency controls, and the ability to demonstrate that AI use in your organization operates within defined boundaries. Documentation mapping our controls to specific regulatory requirements is available on request.
