Topic starter
12/06/2026 9:43 pm
TL;DR: LLM security spans prompt injection, data leakage, model poisoning, and excessive agency across training, deployment, and runtime monitoring, according to Lasso Security. The governance gap is that conventional IAM and access review processes were not built for AI behavior that can disclose, transform, or act on sensitive data at machine speed.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Large Language Model (LLM) Security: Challenges & Best Practices”.
Key questions
Q: What breaks when LLMs can act on prompts and connected tools without tight authorization?
A: The boundary between conversation and execution breaks.
Q: Why do LLMs create risk in identity and access management?
A: LLMs create risk when teams confuse fluent answers with verified security evidence.
Q: How do enterprises reduce prompt injection and output-handling risk together?
A: They should combine provenance-aware context handling, runtime policy enforcement, and output controls that prevent sensitive data from leaving the model path.
Practitioner guidance
- Define model output trust boundaries Classify which LLM outputs may influence access, workflow, or external tool execution, then require validation before those outputs are consumed.
- Inventory AI-connected data flows Map prompts, retrieval sources, plugins, APIs, and storage locations so you can see where sensitive data may enter or leave the system.
- Restrict tool authority by use case Separate recommendation from execution and limit each model or agent to the smallest tool scope needed for its task.
Bottom line: LLM security is not just a model problem. It is a governance problem that spans data, identity, tool authority, and runtime behaviour.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 3 days ago by NHI Mgmt Group
13/06/2026 12:00 am
LLM security is really identity governance for behaviour-bearing systems. Once a model can disclose, transform, or act on data, the problem is no longer just application security. It becomes a question of who or what is authorised to influence the system, what context it may retain, and which outputs are allowed to become action. That is why LLM security belongs in the same governance conversation as IAM, PAM, and NHI.
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.
A question worth separating out:
Q: How should organisations govern AI agents that rely on long-form LLM generation?
A: Treat the model as a component inside a governed execution chain, not as the final decision-maker. Require re-evaluation before tool calls, log refusal depth, and separate output generation from authority to act. That approach reduces the chance that unsafe text becomes unsafe execution.
👉 Read our full editorial: LLM security exposes the governance gap in AI and identity controls
This post was modified 3 days ago by NHI Mgmt Group