Topic starter
14/08/2026 6:36 pm
TL;DR: Most DLP tools fail because they monitor single signals rather than intent, leaving gaps across human error, insider risk, and external attackers, according to Orion, while citing cases such as TalentHook, Toronto-Dominion Bank, and Change Healthcare. The underlying governance problem is that one control model cannot safely handle very different data-loss behaviours at enterprise scale.
Editorial analysis by NHI Mgmt Group, based on content published by Orion: “Finding Intent: The Three DLP Hazards Every Security Team Must Know”.
Key questions
Q: How should security teams implement DLP for human error, insider risk, and AI-driven data movement?
A: Use different control responses for different loss modes.
Q: Why do legitimate access patterns still create data-loss risk?
A: Because legitimate access can be repurposed.
Q: What are the signs that DLP rules are too broad or too weak?
A: A common sign is alert fatigue, where teams receive so many low-value notifications that they stop treating alerts as meaningful.
Practitioner guidance
- Define intent signals for DLP policy Use actor identity, data sensitivity, destination, timing, and deviation from normal behaviour as the minimum context for policy decisions.
- Separate controls by loss scenario Build different response paths for accidental sharing, insider misuse, and attacker-driven exfiltration instead of relying on one blocking rule.
- Constrain AI agent data access Limit what assistants and automations can read, copy, and forward so a single prompt or workflow cannot fan out sensitive data at scale.
Bottom line: Intent-aware DLP is emerging because data loss now spans accidental sharing, insider misuse, and AI-driven scale, and a single control model does not handle those patterns well.
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14/08/2026 6:43 pm
Intent-aware DLP is the right framing because data loss is a governance problem, not just a content-filtering problem. A policy that only inspects payloads will always miss the difference between accidental sharing, insider misuse, and attacker exfiltration. The decisive question is whether the control can interpret behaviour in context, not whether it can match a pattern. Practitioners should treat intent recognition as a core policy design issue.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to the State of Secrets in AppSec.
A question worth separating out:
Q: Should teams treat AI agents like high-speed data movers?
A: Yes. If an AI agent can access sensitive repositories, forward outputs, or act across many records, it should be governed like a fast-moving identity with a larger blast radius than a human user. That means tighter scope, stronger monitoring, and stricter approval boundaries for high-risk data.
👉 Read our full editorial: Intent-aware DLP is becoming essential for AI-era data loss