Topic starter
14/09/2026 10:10 am
TL;DR: Enterprise AI use now spans chat tools, embedded copilots, models, and autonomous agents, and WitnessAI argues that traditional packet-centric and keyword-based controls miss much of the risk. Its seven-practice model points to unified visibility, intent-based classification, graduated policy enforcement, runtime guardrails, agent governance, audit trails, and continuous evaluation as the operational baseline.
Editorial analysis by NHI Mgmt Group, based on content published by WitnessAI: “LLM security best practices”.
Key questions
Q: How should security teams manage permissions for AI agents?
A: Security teams should regularly assess and update the permissions granted to AI agents to ensure they align with their intended scope.
Q: Why do static controls fail for enterprise LLM security?
A: Static controls fail because they are designed to inspect known content, not to govern context, intent, and action.
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
- Map all AI interaction paths Inventory chat tools, embedded copilots, agent APIs, native apps, and MCP connections so sanctioned and shadow AI usage is visible in one place.
- Classify AI use by intent and destination Apply policy that considers role, data sensitivity, and model destination together instead of relying on keywords or application allow-lists alone.
- Adopt graduated runtime enforcement Use allow, warn, block, and route decisions so low-risk AI work continues while sensitive prompts or outputs are handled differently.
Bottom line: LLM security fails when organisations rely on static content inspection for systems that now generate, transform, and act on business data in real time.
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14/09/2026 10:11 am
Runtime governance is now the defining control plane for enterprise AI. The article makes a case that LLM risk is not adequately controlled by static content filters, perimeter inspection, or one-time policy design. Once AI systems can read, transform, and act on business data, policy has to be enforced at the moment of interaction. For identity teams, that means the governance question shifts from who logged in to what the system was allowed to do in that session.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: Should organisations block AI tools or enable them safely?
A: Organisations should enable AI safely rather than rely on blanket blocking. Bans often push employees toward personal accounts and unmonitored tools, which reduces visibility and increases risk. A safer model combines approved AI paths, data classification, monitoring, and clear enforcement for prohibited content.
👉 Read our full editorial: LLM security best practices need runtime governance, not static controls