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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.
NHIMG editorial — based on content published by Lasso Security: AI Runtime Security is the Security Layer AI Can’t Outgrow
Questions worth separating out
Q: How should security teams govern AI systems that act at runtime?
A: Security teams should govern runtime AI by tying allowed actions to identity, context, and execution state, not just to model output.
Q: Why do agentic AI systems need runtime security instead of static guardrails alone?
A: Agentic systems can plan, call tools, and adapt while they are live, so static guardrails cannot reliably predict or contain every harmful sequence.
Q: What signals show that AI runtime controls are failing?
A: Warning signs include unexplained tool usage, access to data outside the expected workflow, repeated policy overrides, and behavioural drift across sessions.
Practitioner guidance
- Map every live AI permission to an owner Inventory which human, service, or application identities each AI system inherits at runtime, then assign a business and technical owner for each permission set.
- Separate high-risk actions from low-risk interactions Use inline enforcement for tool invocation, data access, and code execution, while keeping behavioural monitoring out of band for lower-risk analysis.
- Log execution context for every material action Capture prompts, retrieved context, identity context, tool calls, and resulting actions so incident review can answer who authorised what and when.
What's in the full article
Lasso Security's full post covers the operational detail this post intentionally leaves for the source:
- Runtime enforcement architecture for live AI applications and agentic workflows
- Decision points for inline versus out-of-band control placement across production paths
- Execution telemetry categories used to inspect prompts, context, tool calls, and actions
- Operational response patterns for blocking, modifying, or escalating risky AI behaviour
👉 Read Lasso Security's analysis of AI runtime security and agentic control →
AI runtime security: are your controls keeping up at execution time?
Explore further
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
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
