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24/06/2026 6:59 pm
TL;DR: Endpoints remain a primary route for data exfiltration, and Netwrix says its roadmap focuses on blocking sensitive data movement across devices, applications, offline systems, and AI tools while preserving usability. For IAM and security teams, that reinforces a broader governance shift toward controlling where data can go, not just who can log in.
NHIMG editorial — here’s why we think this discussion matters
Questions worth separating out
Q: How should security teams control sensitive data leaving endpoints?
A: Security teams should enforce data movement policy at the endpoint itself, not rely only on network controls or user training.
Q: Why do remote and offline endpoints complicate data loss prevention?
A: Remote and offline endpoints complicate DLP because the organisation cannot depend on a constant connection to a central control point.
Practitioner guidance
- Map sensitive data movement paths Inventory where confidential data leaves the endpoint through browsers, USB devices, desktop apps, sync clients, and AI tools.
- Test policy enforcement on offline devices Verify that endpoint controls continue to work when laptops are disconnected, roaming, or outside normal corporate connectivity.
- Separate sanctioned and unsanctioned AI use Define which AI tools may receive sensitive material and which must be blocked.
What to expect at the briefing
Netwrix's full on-demand webinar covers the operational detail this post intentionally leaves for the source:
- Demonstration of endpoint controls for USB, browser, and application-based exfiltration paths.
- Roadmap discussion on how AI-aware data protection is expected to fit into endpoint DLP policy enforcement.
- Coverage of protection patterns for Windows, macOS, Linux, and offline endpoints in mixed environments.
- Speaker-led walkthrough of how teams can balance data protection with user productivity.
👉 Watch Netwrix's on-demand webinar on endpoint DLP and AI-aware data protection →
Endpoint DLP and AI tools: what IAM teams need to know?
Explore further
25/06/2026 4:03 am
Endpoint DLP is becoming an identity-adjacent control plane. When data can move through browsers, sync clients, removable media, and AI tools, the question is no longer only who authenticated. It is also what that authenticated identity is allowed to move, where, and under what contextual conditions. That makes endpoint governance part of the broader identity security stack, especially where NHI-driven workflows or AI-assisted work create new transfer paths. Practitioners should treat data movement policy as a core control, not a side feature.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, which shows that policy intent and operational behaviour often diverge.
A question worth separating out:
Q: What should IAM and security teams measure in endpoint DLP programmes?
A: They should measure how often sensitive data is blocked, which channels users try most often, and whether policy coverage is consistent across devices and operating systems. If one endpoint type produces more exceptions or workarounds, that is a governance gap, not a user-training issue.
👉 Read our full editorial: Endpoint DLP and AI-aware data protection are converging
