Topic starter
06/06/2026 11:07 am
TL;DR: RSA 2026 showed agentic AI security is crowded, but most offerings still cover only one slice of the problem, with endpoint-only visibility, adjacent-category rebrands, and missing runtime enforcement leaving major blind spots according to Zenity. The category is moving toward unified platform coverage that spans discovery, posture, detection, response, and governance across all agent deployment patterns.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “After RSA, Here Is What Comprehensive Agentic AI Security Actually Looks Like”.
Key questions
Q: What breaks when agent security is covered only by endpoint tools?
A: Endpoint-only coverage leaves SaaS-embedded agents and custom cloud agents outside the security view, so the organisation sees activity on laptops but not the wider execution surface.
Q: Why do identity controls matter so much in agentic AI attack paths?
A: Identity controls matter because the fastest route from initial access to impact often runs through credentials, session tokens, and role inheritance.
Q: What are the signs that an agentic AI red team is too narrow?
A: A red team is too narrow when it focuses mainly on prompt injection scores, jailbreaks, or single-turn harmful output tests.
Practitioner guidance
- Inventory agent deployment patterns Classify every known agent as endpoint, SaaS-embedded, or custom-built, then document which security controls can actually observe each pattern.
- Separate posture from runtime control Require one control path for build-time configuration review and another for live action interception, because the two problems are not interchangeable.
- Test for cross-environment visibility Ask whether one platform can see agent identity, tool usage, data access, and action chains across all environments before accepting a category claim.
Bottom line: Agentic AI security now spans multiple deployment patterns, so point products cannot provide complete governance on their own.
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06/06/2026 11:44 am
Agentic AI security is becoming a platform category because the control problem is cross-environment by design. Endpoint-only visibility, SaaS-only visibility, and cloud-only visibility each expose a different slice of the same risk surface. The field is moving away from single-point controls because agent deployments do not stay in one place, one identity model, or one execution context. Practitioners should expect category consolidation around platforms that can govern the whole agent estate.
A few things that frame the scale:
- The agentic AI market is already worth $5 billion and is projected to reach $50 billion by 2030.
A question worth separating out:
Q: How should teams evaluate agent security platforms against platform coverage claims?
A: Teams should test whether a platform can discover agents across all deployment patterns, assess posture before execution, and enforce policy at runtime. If it can only monitor one environment or one control layer, it is a partial control, not a complete agent security programme.
👉 Read our full editorial: Agentic AI security needs platform coverage, not point products