Topic starter
23/06/2026 9:15 pm
TL;DR: Lasso found that 13% of employee-submitted GenAI prompts contained sensitive organizational data, with 30% of code-sharing prompts exposing credentials or proprietary code and 38% of network-exposure prompts revealing internal infrastructure details, according to Lasso Security. The risk is not the chatbot itself but the governance gap between everyday employee use and enforceable data-sharing controls.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Lasso Research Reveals 13% of Generative AI Prompts Contain Sensitive Organizational Data”.
By the numbers:
- 13% of employee-submitted prompts to GenAI chatbots contained sensitive organizational data.
- 30% of prompts in the code and token sharing category included exposed credentials, secrets, or proprietary code.
- 38% of network information submissions posed direct risk by enabling network reconnaissance.
Key questions
A: Security teams should treat GenAI as an inference layer, not just a retrieval layer.
Q: Why do AI agents create a different data leak risk than employee chat prompts?
A: AI agents can retrieve data directly from SaaS apps, internal tools, and databases on a user’s behalf, so the employee does not need to copy data into a prompt manually.
Q: What signals show that AI access is outpacing governance?
A: The clearest signals are broad inherited permissions, large volumes of stale guest access, and inconsistent file classification across teams.
Practitioner guidance
- Define prompt disclosure policy Spell out which classes of data cannot be entered into GenAI tools, including credentials, source code, internal URLs, IP addresses, MAC addresses, personal data, and payment data.
- Deploy continuous GenAI discovery Build a live inventory of approved and shadow GenAI tools so policy can be enforced across the actual user surface, not only the sanctioned chatbot.
- Add pre-submission content scanning Inspect prompts and attached files before they reach GenAI systems so risky disclosures can be blocked before they leave the user session.
Bottom line: Employee prompt behaviour, not the model itself, is the main exposure path in this study.
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25/06/2026 2:28 am
Prompt governance is now a disclosure-control problem, not a chatbot problem. The article shows that employees are already using GenAI tools in ways that move sensitive business data outside governed enterprise systems. That makes disclosure policy, not model novelty, the real control boundary. The practitioner lesson is that prompt handling must be treated like any other data egress path.
A few things that frame the scale:
- Generative AI use specifically increased from 33% in 2023 to 79% in 2025, according to McKinsey’s Global Surveys on the State of AI.
A question worth separating out:
Q: How does prompt injection change the way teams should evaluate GenAI risk?
A: Prompt injection shows that a chatbot can be steered by attacker-supplied instructions hidden in the prompt or surrounding content. That means teams should evaluate not only what users type, but also what external content the system ingests and trusts. If the model can be manipulated into ignoring rules, the interface is not a safe boundary.
👉 Read our full editorial: 13% of GenAI prompts expose sensitive data to enterprise risk