Enterprise Features, Explained

Zero Trust starts with a simple rule: never trust a session just because someone opened a browser tab. With GenAI, that rule matters more than ever. Employees can reach dozens of AI tools in seconds — and each one is a potential path for code, customer data, or strategy docs to leave the organization.

SSO-gating puts your identity provider at the center of that decision. AI Data Shield only trusts sessions that have been verified through your company's SSO. If the session isn't IdP-verified, the tool doesn't load. That aligns GenAI access with how modern security teams already think about trust: verify first, then allow.

From the employee's perspective, the experience is straightforward. They open an AI tool. The extension checks whether SSO is required and whether the current session meets that requirement. Verified sessions pass through. Unverified ones are blocked — without asking IT to maintain a manual allow/deny list of every AI tool that appears on the internet. Policy drives the outcome; list-chasing does not.

Most legacy DLP approaches were built to detect sensitive content after it moved — in email, file shares, or network traffic — and then alert someone to clean up. That model breaks down with GenAI chat. By the time a weekly report shows an unapproved tool was used, the paste already happened.

AI Data Shield is designed to interrupt at attempt time. When someone tries to open an unapproved or unverified AI platform, they see the block in the moment — not as a surprise finding days later. That changes behavior and closes the gap that detect-after-the-fact tools leave open by design.

For security teams, real-time blocking means fewer post-incident reviews about tools you never sanctioned. For employees, it means clear feedback at the point of use: this path isn't allowed; use an SSO-verified option instead.

Engineering, finance, and legal do not share the same GenAI risk profile. One team may need approved coding assistants under SSO; another may need tighter limits on where customer or regulated data can go. A single company-wide switch is rarely enough.

AI Data Shield lets you set policies that match how your organization actually works. Start from templates so you are not rebuilding rules from a blank page for every department. Then customize where you need finer control — by team, role, or use case — without abandoning the shared foundation.

The result is governance that scales with your org chart: consistent enough for security to manage, flexible enough that each function gets rules that fit their work.

Security and compliance teams need more than a policy document. They need demonstrable enforcement: what was allowed, what was blocked, and under what conditions. AI Data Shield logs allowed, blocked, and flagged events with context so you can show how GenAI access is actually governed.

That visibility supports incident response and day-to-day oversight. It also aligns with what frameworks such as SOC 2 and ISO 27001 expect during audits: evidence that controls exist and are operating — not a claim that AI Data Shield itself is a certification. When auditors ask where data can go, including into AI tools, you have a record of enforcement rather than an explanation that the topic was overlooked.

Reporting turns browser-level decisions into something leadership and compliance can review: clear outcomes, tied to policy, ready when someone asks for proof of control.