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14/08/2026 6:36 pm
TL;DR: Traditional DLP assumes file transfers through known channels, but AI tools such as ChatGPT, Cursor, and autonomous agents move data through prompts, workflows, and integrations without classic upload or download events, according to Orion. The control gap is now a threat-modeling problem: teams need to map AI-specific data flows before they can govern them effectively.
NHIMG editorial — based on content published by Orion: LLMjacking: How Attackers Hijack AI Using Compromised NHIs
Questions worth separating out
Q: How should security teams govern AI data flows that bypass traditional DLP channels?
A: Start by mapping the full path from prompt to output, including connectors, service accounts, and any delegated access behind the scene.
Q: Why do AI tools create data-loss risk even when users never download files?
A: Because the data can move through prompts, model outputs, and agent actions instead of through an obvious transfer event.
Q: What do security teams get wrong about AI access risk?
A: Many teams focus on the model while ignoring the identity path that reaches it.
Practitioner guidance
- Map AI-specific data flows Trace how prompts, connectors, agent actions, and outputs move sensitive data across SaaS, repositories, and email.
- Audit delegated identities and secrets Review service accounts, API keys, OAuth grants, and local credentials used by AI tools and agents.
- Extend DLP to prompt and output surfaces Classify prompts, generated summaries, and connector-mediated outputs as sensitive handling points.
What's in the full article
Orion's full article covers the operational detail this post intentionally leaves for the source:
- The article's fuller explanation of how prompt-driven flows differ from classic file-transfer DLP cases.
- The specific examples of AI tools and integrations that create blind spots in existing policy and monitoring models.
- The source's discussion of what a modern AI threat model should map across flows, surfaces, and enforcement points.
- The article's own framing of why teams are reacting case by case instead of governing AI data movement systematically.
👉 Read Orion's analysis of why AI data flows are breaking traditional DLP →
AI data flows are changing DLP assumptions. What should teams do?
Explore further
14/08/2026 6:43 pm
AI data loss is now a governance problem, not just a control problem. The article’s core point is that organisations cannot protect what they have not mapped. Traditional DLP was built for visible movement across known channels, but AI introduces prompt-mediated and agent-mediated flows that do not fit those assumptions. For practitioners, the consequence is clear: controls must be aligned to actual data paths, not legacy boundary labels.
A question worth separating out:
Q: What is the difference between traditional DLP and AI-specific data governance?
A: Traditional DLP focuses on known transfer channels, while AI-specific governance has to cover prompts, agents, and integration layers that can move information indirectly. In practice, AI governance is broader because it must account for both the content being handled and the identities or secrets enabling the workflow.
👉 Read our full editorial: AI data loss outpaces traditional DLP models in prompt-driven flows
