Topic starter
10/06/2026 12:10 am
TL;DR: AI runtime security shifts protection to execution time, where prompt injection, tool misuse, policy circumvention, and decision drift emerge in production, according to Lasso Security. Static testing and pre-release guardrails are necessary but insufficient because the most consequential AI risks appear only when live permissions, real data, and real systems are in play.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “AI Runtime Security is the Security Layer AI Can’t Outgrow”.
Key questions
Q: What breaks when AI systems are only protected by pre-release guardrails?
A: Static guardrails fail when the risk appears only after deployment, during live inference, tool use, and action execution.
Q: Why do agentic AI systems change the authorisation problem for security teams?
A: Agentic systems can select tools, access data, and take actions through inherited permissions, so the relevant question becomes what they are authorised to do in the live session.
Q: What are the signs that runtime AI controls are not enough?
A: Common warning signs include unexplained tool usage, policy drift across sessions, repeated dependency on broad inherited access, and incidents that can only be explained after the fact.
Practitioner guidance
- Define execution-time permission boundaries Map exactly which tools, datasets, and actions an AI system may use at runtime, then bind those permissions to the live identity context rather than to the model itself.
- Enforce inline controls for high-risk actions Place blocking or gating controls in front of tool invocation, data access, and code execution so the action can be stopped before impact, not only investigated afterward.
- Instrument runtime telemetry for auditability Capture prompts, retrieved context, tool calls, decisions, and resulting actions so investigators can reconstruct what the AI did under live permissions.
Bottom line: AI runtime security addresses the gap between what an AI system was tested to do and what it actually does when it is connected to live permissions and live data.
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This topic was modified 4 hours ago by NHI Mgmt Group
11/06/2026 1:51 am
AI runtime security is the missing execution layer for agentic identity governance. Static application security assumes the risk surface can be bounded before deployment, but agentic systems change the risk model once they are live. The relevant control point becomes runtime identity, tool use, and policy enforcement at the moment of action. Practitioners should treat runtime as the point where governance either exists or fails.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to GitGuardian and CyberArk.
A question worth separating out:
Q: How do organisations balance AI runtime security with user experience?
A: Organisations should reserve strict inline controls for actions that can change data, trigger workflows, or expose secrets, while using lighter monitoring for routine interactions. The right balance is risk-based enforcement that protects the execution path without slowing every prompt or response.
👉 Read our full editorial: AI runtime security exposes where static guardrails stop working
12/06/2026 3:26 am
Runtime security is now an identity problem, not only an application security problem. Once AI systems can call tools, access data, and act on behalf of users, their live permissions become the security boundary. That shifts the governance focus from model output quality to action authority, privilege scope, and execution oversight. Practitioners should treat runtime control as part of identity governance for AI, not as a bolt-on detection layer.
A question worth separating out:
Q: How should teams balance prevention and observability in AI runtime security?
A: Use inline enforcement for high-risk actions such as tool invocation, sensitive data access, and code execution, then add out-of-band telemetry for drift detection and forensics. The balance depends on how quickly harm can occur and how much latency the business can tolerate. Where actions are irreversible, prevention should come first.
👉 Read our full editorial: AI runtime security exposes where static guardrails stop working
This post was modified 4 hours ago by NHI Mgmt Group