Topic starter
06/09/2026 6:20 pm
TL;DR: AI applications only expose their real attack surface at runtime, and LEVO argues that traditional security tools miss the machine-to-machine interactions, identity drift, and data flows that determine whether agentic systems can move safely from pilot to production. The security model now has to follow execution, not just deployment, because governance fails when AI is treated like static software.
Editorial analysis by NHI Mgmt Group, based on content published by LEVO: “Runtime AI Visibility: The Foundation for Scaling AI Beyond Pilots”.
By the numbers:
- Gartner reported a 750% surge in AI-agent inquiries between Q2 and Q4 2024.
- About 32% of enterprises exploring AI agents stall at pilot stage and are never deployed in production.
Key questions
Q: What breaks when AI security stops at posture instead of runtime behaviour?
A: Security teams lose visibility into the actions that matter most, such as API calls, record access and tool use.
Q: Why do delegated tokens and NHI access create governance risk in AI systems?
A: They create risk because the identity that authorises an action is often not the same identity that executes it, and the effective privilege can change mid-session.
A: Security teams should treat agentic AI as an identity class that needs continuous observation, not just initial authorization.
Practitioner guidance
- Inventory active AI components at runtime Create a live register of agents, MCP servers, LLMs, APIs and third-party integrations, including parent application ownership and usage frequency.
- Trace identity and token usage across agent chains Record who authorised a workflow, which non-human identities executed it, and how delegated token scope changed across each hop.
- Instrument east-west AI traffic for auditability Capture prompts, responses, retrievals and downstream API calls so compliance teams can reconstruct causality after an incident or review.
Bottom line: AI systems do not reveal their full attack surface until they are running, which makes runtime observability a governance requirement rather than an optional enhancement.
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06/09/2026 6:21 pm
Runtime AI visibility is becoming the control plane for AI governance, not an optional telemetry layer. Once agents, MCP servers and APIs begin interacting dynamically, the question is no longer whether the model is accurate enough, but whether the organisation can see and constrain what actually happens. That changes AI governance from a policy exercise into a live execution problem. Practitioner conclusion: if you cannot observe runtime behaviour, you cannot claim control.
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.
- 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: What is the difference between runtime AI visibility and pre-production testing?
A: Pre-production testing checks expected behaviour in controlled conditions, while runtime visibility shows how the system behaves under real prompts, data and delegation patterns. AI systems change with each execution, so testing alone cannot expose emergent tool use, privilege aggregation or shadow data flows. The two are complementary, but only runtime visibility proves control in production.
👉 Read our full editorial: Runtime AI visibility is becoming the control plane for enterprise AI