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06/09/2026 3:45 pm
TL;DR: AI security testing now focuses on how AI systems behave in production, including the APIs they call, the data they access, and the actions they execute, because autonomous execution creates risk that static testing misses, according to LEVO. Runtime visibility and continuous validation matter because governance only works when teams can prove AI systems stay inside approved boundaries after deployment.
NHIMG editorial — based on content published by LEVO: AI security testing for runtime behaviour and execution risk
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams test AI-powered systems in production-like environments before rollout?
A: Security teams should test AI systems across prompts, workflows, integrations, and safety boundaries before production exposure.
Q: Why do delegated AI identities create governance risk?
A: Delegated AI identities can be broader than the task requires, and autonomous systems can chain valid actions into outcomes that violate policy or intent.
Q: What are the signs that an AI application is failing its security boundaries?
A: Common warning signs include inconsistent outputs for the same prompt, unexpected changes in response quality, hidden dependence on mutable retrieved data, and successful prompt manipulation through user controlled fields.
Practitioner guidance
- Define runtime test cases for AI execution scope Build tests that validate which APIs, tools, and data sources an AI system can actually reach under production credentials, then rerun them whenever integrations change.
- Treat AI service accounts as governed non-human identities Assign ownership, expiry, review cadence, and revocation criteria to every AI identity, including shared tokens and delegated service accounts used by agents.
- Monitor chained actions and policy drift continuously Track multi-step behaviour across sessions so teams can detect when an AI system begins combining valid actions into unsafe workflows or expanding its access pattern.
What's in the full article
LEVO's full research post covers the operational detail this post intentionally leaves for the source:
- Concrete runtime test scenarios for API invocation scope, data access, and action authorization
- The article's full breakdown of behavioural drift, chained actions, and failure handling in AI systems
- LEVO's explanation of how it distinguishes AI security testing from model risk management and governance
- Operational examples showing how runtime visibility supports continuous validation in deployed environments
👉 Read LEVO's full analysis of AI security testing and runtime behaviour →
AI security testing for runtime behaviour: are your controls keeping up?
Explore further
06/09/2026 3:45 pm
Runtime behaviour is now the security boundary for AI systems. The article correctly treats AI risk as an execution problem because deployed systems act, call, and chain decisions in ways static reviews cannot fully predict. That aligns with modern AI governance thinking, where policy intent only matters if the runtime system can be checked against it. Practitioners should treat observed behaviour as the control surface, not the model alone.
A question worth separating out:
Q: How do organisations separate AI governance from AI security testing?
A: AI governance defines what should be allowed, while AI security testing verifies whether the deployed system actually stays within those boundaries. Governance without runtime validation is only policy on paper, especially once agents can retrieve data, call tools, and trigger workflows on their own.
👉 Read our full editorial: AI security testing must verify runtime behaviour, not just model accuracy
