Notifications
Clear all
Topic starter
14/08/2026 6:36 pm
TL;DR: AI data leaks happen when employees move sensitive company data into AI tools, often accidentally, and traditional DLP misses many of these events because they travel through approved interfaces and lack obvious pattern matches, according to Orion. The practical challenge is to govern data movement in context, not simply block AI use and drive employees into shadow AI.
NHIMG editorial — based on content published by Orion: AI data leak prevention and the limits of traditional DLP
By the numbers:
- 63% of breached organizations had no AI governance policy in place.
Questions worth separating out
Q: How should security teams handle data leakage risks in AI models?
A: Security teams should treat AI leakage as a lifecycle governance problem, not just a perimeter problem.
Q: Why do traditional DLP tools miss AI data leakage?
A: Traditional DLP tools are designed to inspect files, messages, and network flows, but AI leakage often happens inside legitimate prompts and valid API calls.
Q: What breaks when organisations rely only on blocking unapproved AI tools?
A: Blocking alone fails because it does not address the business need that drives Shadow AI use.
Practitioner guidance
- Define AI data handling rules by sensitivity class List the data types that must never enter public or unmanaged AI tools, including customer data, source code, secrets, financials, and regulated records.
- Map sanctioned and unsanctioned AI usage paths Identify where people actually use AI, including browser assistants, copilots, coding tools, and personal accounts.
- Add real-time controls for sensitive prompt content Use controls that evaluate the user, data, and destination at the moment of transfer so unsafe moves can be stopped before the data leaves.
What's in the full article
Orion's full article covers the operational detail this post intentionally leaves for the source:
- The specific context-aware verdict model used to distinguish safe from unsafe AI data movements
- Examples of how approved copilots, browser assistants, and endpoint activity can be governed differently
- Operational guidance on defining data rules for source code, customer records, and regulated information
- The deployment and workflow details behind Orion's 30-minute rollout claim
👉 Read Orion's analysis of preventing AI data leaks through context-aware controls →
AI data leaks and DLP gaps: what security teams need to know?
Explore further
14/08/2026 6:43 pm
AI data leakage is now an identity-adjacent governance issue, not only a content-security issue. The article is right to treat the user, the data, and the destination as a single control problem. In practice, human identity, approved applications, and data policy all intersect at the prompt. That means organisations need policy enforcement that understands who is acting and where the data is going, not just what the data looks like.
A question worth separating out:
Q: What is the difference between preventing AI data leakage and detecting it after the fact?
A: Prevention stops an unsafe transfer before the data leaves, while detection only tells you that the leak already happened. In AI workflows, that distinction matters because the risky action is often a normal employee task. Controls need to intervene at the point of movement, not after review.
👉 Read our full editorial: AI data leaks expose the limits of traditional DLP controls
