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14/08/2026 6:36 pm
TL;DR: Enterprise Claude use creates a distinct data-loss problem because long prompts, uploads, and Claude Code sessions can move confidential documents, source code, and regulated records into third-party AI workflows, according to Orion. The control gap is no longer egress monitoring alone but real-time inspection at the browser or endpoint before the data leaves the user’s surface.
NHIMG editorial — based on content published by Orion: DLP for Claude and real-time data loss prevention
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities , 46% confirmed, 26% suspected.
Questions worth separating out
Q: How should security teams stop employees pasting sensitive data into AI prompts?
A: Security teams should control the browser or endpoint where the paste occurs, not rely only on network or file DLP.
Q: Why do traditional DLP tools miss Claude-style AI workflows?
A: Traditional DLP was built around files, email, and network egress, where pattern matching works reasonably well.
Q: When should organisations prioritise real-time AI DLP over compliance logging?
A: They should prioritise real-time AI DLP whenever users are handling privileged, regulated, or source-code-rich content in conversational AI tools.
Practitioner guidance
- Implement surface-level DLP for AI prompts Deploy controls at the browser and endpoint layer so a paste, upload, or coding-session submission can be allowed, stopped, or coached before it leaves the user surface.
- Classify by content type and context Define policies around privileged agreements, regulated records, source code, and business-sensitive documents, then combine that with sender, destination, and behavioural context.
- Separate compliance telemetry from prevention Use Claude activity feeds for audit and investigation, but do not treat post-event visibility as a substitute for real-time blocking or redaction.
What's in the full article
Orion's full guide covers the operational detail this post intentionally leaves for the source:
- A step-by-step view of how its Claude DLP evaluates prompts, uploads, and Claude Code sessions in real time
- Examples of content types and context signals used to allow, stop, or coach users before submission
- Deployment and integration details for browser, endpoint, and AI workflow coverage
- Customer operating examples showing how one-person programs manage daily review and policy tuning
👉 Read Orion's guide to DLP for Claude and real-time AI data loss prevention →
Claude DLP for enterprise teams: are your controls keeping up?
Explore further
14/08/2026 6:43 pm
Surface-level prevention is the real control boundary for enterprise AI use. Claude-related risk is not solved by after-the-fact logging because the sensitive act happens before the prompt is submitted. Once users are allowed to work in browser-based and terminal-based AI surfaces, security has to intercept the action at the source. The practitioner lesson is clear: if a control cannot act before transmission, it is not controlling loss, only documenting it.
A question worth separating out:
Q: What is the difference between governance visibility and data loss prevention for AI?
A: Governance visibility shows what already happened, while data loss prevention stops or shapes the action before it happens. For AI tools, that difference matters because the dangerous moment is often the paste or upload itself. Teams need both, but only one of them reduces immediate exposure.
👉 Read our full editorial: Claude DLP shifts data loss prevention to the point of paste
