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19/08/2026 3:27 pm
TL;DR: Agentic AI changes the security model because it does not just generate content, it takes actions across tools and environments, and ActiveFence’s discussion of OWASP’s new Top 10 highlights risks including goal hijacking, tool misuse, privilege abuse, memory poisoning, cascading failures, and rogue agents. The governance assumption that access can be granted, reviewed, and contained through static IAM patterns breaks when agents decide and act at machine speed.
NHIMG editorial — based on content published by ActiveFence: The OWASP Top 10 for Agentic AI Explained
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 govern AI agents that can invoke multiple tools in one session?
A: Security teams should govern AI agents as decision-making identities, not just tool users.
Q: Why do AI agents complicate traditional PAM models?
A: Traditional PAM assumes access is relatively stable and can be mediated around known operators or fixed service identities.
Q: What do organisations get wrong about agent memory and trust?
A: They often treat memory as a convenience layer instead of a governed trust store.
Practitioner guidance
- Constrain agent objectives to trusted sources Allow only verified systems to influence agent goals, task updates, and escalation paths.
- Scope every tool to the minimum required action Map each agent tool to a specific action class and remove broad access to files, APIs, and messaging systems that are not required for the task.
- Monitor memory and context for persistence of bad instructions Track when agents store new rules, preferences, or policy references, and alert when those inputs come from low-trust sources or change the agent’s future behaviour.
What's in the full article
ActiveFence's full article covers the operational detail this post intentionally leaves for the source:
- The article walks through each OWASP agentic risk category in more operational depth, including example attack paths and failure modes.
- It breaks out mitigation themes such as red teaming, guardrails, and governance into more concrete implementation language.
- The source includes the vendor's own framing of how it uses the OWASP project in client-facing AI security work.
- It provides additional context around how the Top 10 should be used alongside broader agentic AI security planning.
👉 Read ActiveFence's analysis of OWASP's Top 10 for Agentic Security →
Agentic AI security risks: are your identity controls keeping up?
Explore further
19/08/2026 4:11 pm
Agentic AI turns identity from a permission model into a behaviour model. Traditional IAM can describe who or what should have access, but it does not by itself explain how an agent will use that access at runtime. Once a system can choose tools, sequence actions, and continue without human approval, the security question shifts to behavioural control across the execution path. Practitioners should treat agent identity as a governed runtime actor, not a static account.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to the AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What should teams do when an agent starts taking unsafe actions?
A: Teams should stop the workflow before additional tool calls complete, preserve logs and context for investigation, and revoke the agent’s high-risk access path until the failure mode is understood. Containment matters because unsafe behaviour can cascade into downstream systems quickly. The immediate objective is to limit blast radius and prevent repeated execution.
👉 Read our full editorial: Agentic AI security risks are now identity governance problems
