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02/06/2026 7:02 pm
TL;DR: Cyera says its Omni DLP Investigation Agent brings data, behavioral, and cross-tool context into Microsoft Security Copilot so analysts can summarize incidents, prioritize policy violations, and judge whether activity is legitimate in one workflow. The real shift is that DLP triage is becoming context-driven rather than dashboard-driven.
NHIMG editorial — what this means for NHI practitioners
By the numbers:
- 5 total incidents were summarized in the last 14 days, including 5 medium-severity events and 4 unique users, all involving email.
- 39 violations on financial data, 6 on internal transport rules, and 2 on internal AI were surfaced in the policy view.
Questions worth separating out
Q: How should security teams use DLP agents without giving up control?
A: Security teams should treat DLP agents as decision support, not as autonomous authorities.
Q: Why does context matter so much in DLP investigations?
A: Context matters because the same data movement can be routine, careless, or malicious depending on the identity, role, and destination involved.
Q: What breaks when DLP alerts are reviewed in isolation?
A: When alerts are reviewed in isolation, teams lose the ability to distinguish policy noise from true risk.
Practitioner guidance
- Map investigative data paths end to end Document which identity, data sensitivity, and destination fields are required for DLP triage before an analyst can close a case.
- Separate severity from volume Review whether policy severity reflects business impact or just rule frequency.
- Set boundaries for investigation agents Restrict which sources an investigation agent can query, what context it can enrich, and which results require human approval before escalation.
That is a direct extension of NHI governance, because the investigation layer itself can become a privileged non-human actor?
👉 Read Cyera's analysis of DLP investigation agents in Microsoft Security Copilot →
Explore further
02/06/2026 7:19 pm
Contextual DLP is becoming an identity problem as much as a data problem. Once alerts are enriched with role, history, and destination data, the investigation layer starts acting like an identity decision engine. That shifts governance pressure onto the quality of identity and behavioral inputs, not just the DLP rule set. Practitioners should treat investigation context as part of the control surface, not an afterthought.
A few things that frame the scale:
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to The State of Non-Human Identity Security.
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
A question worth separating out:
Q: How do teams know whether a DLP investigation workflow is working?
A: A working workflow produces fewer unresolved cases, faster time to disposition, and clearer reasons for why an alert was legitimate or suspicious. Analysts should be able to trace each conclusion back to identity, sensitivity, and destination evidence. If those links are missing, the workflow is still too shallow to trust.
👉 Read our full editorial: DLP alerts need context, not more dashboards, in Security Copilot
