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02/08/2026 9:45 am
TL;DR: Enterprises are being sold “AI security platforms” that often repackage legacy tools, while agentic AI introduces risks in tool use, memory, MCP connections, and runtime behaviour that standard AppSec controls miss, according to Akto. The real differentiator is whether a vendor can prove threat-model depth, live interception, and auditable validation across the full agent stack.
NHIMG editorial — based on content published by Akto: How to Evaluate AI Security Vendors for Agentic AI Threats
Questions worth separating out
Q: How should security teams evaluate AI red teaming vendors for agentic systems?
A: Use a coverage matrix that scores attack breadth, depth, runtime validation, and reporting.
Q: Why do traditional AppSec tools fall short for agentic AI?
A: Traditional AppSec tools fall short because they are designed to inspect code, requests, or dependencies, not the meaning of an evolving tool chain.
Q: What do organisations get wrong about MCP security?
A: They often focus on network isolation or prompt filtering and miss the real issue: an authorised workload can still perform an unintended action.
Practitioner guidance
- Test for tool misuse, not just prompt injection Build vendor evaluation scenarios around unsafe tool chaining, manipulated parameters, and multi-step workflows that look legitimate until the execution layer.
- Classify agent connections as governed trust boundaries Treat MCP servers, retrieval sources, and external tools as privileged integrations with explicit ownership, access review, and change control.
- Demand live runtime interception evidence Ask for a demonstration that stops a multi-step attack in production conditions, not a report generated after the event.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- A step-by-step vendor evaluation framework for runtime protection, red teaming depth, and enterprise readiness.
- Specific questions to use in vendor calls when comparing AI agent governance and MCP-aware controls.
- Detailed guidance on how Akto maps its own approach to agentic AI threats and validation.
- Examples of the kinds of red-team probes and runtime scenarios the vendor says teams should demand.
👉 Read Akto's guide to evaluating AI security vendors for agentic AI threats →
Agentic AI security vendors: what should teams actually evaluate?
Explore further
02/08/2026 12:38 pm
Agentic AI security is becoming an identity governance problem, not just an application security category. Once an AI system can select tools, carry memory, and act across sessions, it behaves like a non-human identity with delegated privileges. That means access scope, tool trust, and auditability matter as much as model safety claims. IAM and PAM teams should treat the agent as a governed identity surface, not as a chatbot feature set.
A question worth separating out:
Q: What should organisations require before trusting an AI security vendor in production?
A: They should require live blocking demonstrations, independent validation, and audit trails that link each action back to a specific agent and prompt. They should also verify framework mapping to OWASP Agentic Applications, OWASP MCP, MITRE ATLAS, or the NIST AI Risk Management Framework, so reporting works for both security and compliance teams.
👉 Read our full editorial: How to evaluate AI security vendors for agentic AI threats
