Topic starter
11/06/2026 11:00 pm
TL;DR: 2025 marked the year enterprise AI moved from prompt monitoring to action monitoring, with agents carrying out end-to-end tasks, RAG security formalising, and compliance frameworks becoming non-negotiable according to Lasso Security. The lesson is that governance now has to follow runtime behaviour, access scope, and auditability instead of treating model output as the whole risk surface.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “2025 Predictions vs. Reality: A Year of Measured Progress in AI Security”.
Key questions
Q: How should security teams govern AI workflows that use multiple tools and data sources?
A: Security teams should govern AI workflows by placing explicit authorization at each decision point, not by relying on the permissions attached to the surrounding application or service account.
Q: When does RAG create more security risk than benefit?
A: RAG becomes riskier when retrieval sources are unvetted, embeddings are mutable without control, or the system can reach sensitive repositories too broadly.
Q: What are the signs that AI governance is still stuck at the prompt level?
A: The signs are narrow prompt logging, no visibility into tool calls, no permissions model for retrieval sources, and no audit trail for agent actions.
Practitioner guidance
- Define runtime permissions for AI agents Map each agent to the tools, systems, and data sources it can access, and separate read, write, and execution privileges where possible.
- Govern retrieval sources and embeddings Inventory the knowledge bases, vector stores, and policy gates that feed production RAG flows, then review who can modify them and how integrity is checked.
- Add auditable action logs for agent decisions Capture tool calls, retrieval events, and downstream actions so investigators can reconstruct what the system actually did, not just what it generated.
Bottom line: AI security in 2025 moved beyond prompt monitoring because agents now execute actions through tools and connected data sources.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 7:29 am
2025 proved that AI security is now an identity problem as much as a model problem. Once agents can coordinate tools and data sources, the governance question shifts from what the model says to what it can do. That means permissions, delegation, and auditability sit inside the security perimeter, not outside it. Practitioners should treat agent runtime access as governed identity.
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.
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: What should organisations prioritise first: model controls or compliance frameworks?
A: They should do both, but compliance frameworks should define the evidence, ownership, and approval structure while model controls enforce runtime behaviour. Without that alignment, organisations end up with technical safeguards that cannot satisfy audit, accountability, or incident reporting needs.
👉 Read our full editorial: AI security in 2025: agents, compliance and model consolidation
This post was modified 1 day ago by NHI Mgmt Group