Topic starter
23/03/2026 1:54 pm
TL;DR: LLMs and AI assistants expand data exposure because prompts, entitlements, and outbound channels can all move sensitive information beyond intended boundaries, according to RSA Security. The real issue is not model novelty but governance drift: access reviews, data scoping, and control settings were built for predictable access patterns, not wide-open assistant behaviour.
Editorial analysis by NHI Mgmt Group, based on content published by RSA Security: “Best Practices for LLMs and Generative AI”.
Key questions
Q: What breaks when AI agents are given broad enterprise access without tight governance?
A: Broad access turns AI agents into high-speed execution paths that can move data, spend money, modify records, or delete assets before operators can intervene.
Q: Why do AI assistants increase data-exposure risk even when users follow prompt rules?
A: Because prompt discipline only addresses input, while the larger risk comes from what the system is allowed to retrieve and emit.
Q: How should security teams use AI agents for user access reviews?
A: Use AI agents to gather evidence and draft recommendations, but keep final approval with accountable human reviewers until the control is proven stable.
Practitioner guidance
- Define assistant input boundaries Publish explicit rules for what data employees may place into prompts, including what is prohibited because it is not intended for public consumption.
- Map assistant entitlements to data sources Inventory every repository, mailbox, workspace, and application the assistant can query, then confirm which datasets it should never reach.
- Include AI assistants in access certifications Add assistant accounts, connector permissions, and retrieval scopes to regular access reviews so reviewers assess the full disclosure path.
Bottom line: LLMs and generative AI create governance risk when prompts, entitlements, and outbound channels can move sensitive information beyond intended boundaries.
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This topic was modified 1 day ago by NHI Mgmt Group
11/10/2026 2:57 am
LLM governance is fundamentally an access boundary problem. The article’s real message is that identity governance has moved upstream from user access to assistant-mediated access paths. Once prompts can trigger retrieval across connected data sources, the control question becomes whether the assistant is authorised to combine and return those sources at all. Practitioners should treat assistant scope as part of the entitlement model, not as a separate AI issue.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- More than 80% of enterprises will have used generative AI APIs or deployed GenAI applications by 2026, up from 5% in 2023, according to Gartner.
A question worth separating out:
Q: What is the difference between prompt controls and data-scope controls for LLMs?
A: Prompt controls govern what a user may ask, while data-scope controls govern what the assistant can actually access and return. Both matter, but only data-scope controls stop an authorised prompt from producing an unauthorised disclosure. In practice, the second control is the one that limits blast radius.
👉 Read our full editorial: Identity governance best practices for LLMs and generative AI