How to evaluate AI security vendors for agentic AI threats

At a glance

What this is: This is a practitioner guide to evaluating AI security vendors for agentic AI, with a clear finding that legacy security tooling misses the agent’s decision, tool-use, and memory layers.

Why it matters: It matters because IAM, PAM, and NHI programmes increasingly have to govern AI agents as privileged software entities, and buying the wrong control model leaves tool access, data access, and auditability unresolved.

👉 Read Akto's guide to evaluating AI security vendors for agentic AI threats

Context

Agentic AI security is not the same problem as traditional application security. AI agents can call tools, access data, retain memory, and chain actions across sessions, which means the control plane is no longer just traffic inspection or model filtering. For identity and access teams, the question becomes whether an agent’s permissions, tool bindings, and audit trail are governable as a non-human identity or whether they remain outside policy.

The article argues that many vendors are still repackaging older security categories for a new attack surface. That distinction matters for practitioners because a dashboard that flags prompts after the fact does not establish runtime control, and a posture scan does not prove an agent is safe to operate. For context on how machine identities and agent identities fit into broader identity governance, see the Ultimate Guide to NHIs and the OWASP NHI Top 10.

Key questions

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. Focus on whether the platform tests the agent decision loop, connected tools, MCP paths, and multi-step abuse, not just model outputs. The right question is not whether the vendor does red teaming, but how much of the real attack surface it exercises.

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. In agentic systems, the danger often appears only after several valid steps combine into an unsafe outcome. That makes sequence control and identity attribution essential.

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. MCP security fails when teams trust transport and service authentication to answer an authorization question that only the request context can resolve.

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.

Technical breakdown

Why legacy AppSec tools miss agentic AI risk

Traditional WAFs, CSPM tools, and static model scanners each examine one slice of the problem. WAFs look at request traffic, CSPM checks cloud configuration, and model scanners test isolated model behaviour, but agentic systems create risk through reasoning, tool selection, memory, and multi-step workflows. The failure often appears only after a chain of benign-looking actions has been manipulated. That is why a model can look safe in isolation while the surrounding agent is still exploitable.

Practical implication: evaluate AI security as a system control problem, not a point-product filter problem.

What makes MCP security a distinct control layer?

Model Context Protocol changes the attack surface because it standardises how agents discover and invoke external tools. Each MCP server becomes a trust boundary, and insecure connections can expose tools, data sources, or execution paths that were never designed for autonomous use. In practice, MCP risk is not just transport security. It is about whether the agent is allowed to reach the right tool, with the right parameters, for the right reason, and whether that path is monitored at runtime.

Practical implication: inventory MCP connections as privileged integrations and subject them to access review.

Why runtime protection matters more than detection reports

Runtime protection intervenes while the agent is acting, which is different from post-event alerting or offline red teaming. If a platform can only show that an attack would have been detected, it has not reduced blast radius in production. Behavioural anomaly detection is useful because agent compromise often shows up as unusual tool sequencing, unusual data access, or unexpected cross-system movement before a single action becomes obviously malicious. The operational question is whether the vendor can stop that sequence live.

Practical implication: insist on live blocking demonstrations for multi-step attacks, not slideware about detection.

NHI Mgmt Group analysis

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.

Purpose-built controls are now the dividing line between real coverage and repackaged legacy tooling. The article correctly shows that WAFs, CSPM, and model scanners each miss the core failure modes of agentic systems. That creates a market signal: buyers will increasingly need vendors that can model input, execution, and output layers separately. The named concept here is agent execution blind spot, meaning the gap where controls observe prompts or posture but not the agent’s decisions and tool actions.

MCP-aware governance will become a standard requirement for enterprise AI programmes. As agents rely on structured tool connectivity, the protocol layer becomes a privilege boundary, not just an integration detail. Organisations that do not classify MCP servers, tool permissions, and downstream data paths as governed assets will keep discovering risk only after an incident. Practitioners should expect access governance, not model governance alone, to define the next maturity step.

Vendor validation is shifting from claims to proof, and that is the right direction. The article is strongest where it demands third-party validation, audit trails, and framework mapping. In a fast-moving category, those are the controls that let security, legal, and compliance teams use the output operationally. For practitioners, the lesson is simple: if a vendor cannot explain how it tests, blocks, and records agent behaviour, it is not ready for enterprise decision-making.

What this signals

Agentic AI programmes are moving faster than the governance models built to supervise them. The practical consequence is that security teams will need to classify agents as governed identities, map their tool dependencies, and define runtime controls before the deployment footprint becomes too large to audit effectively.

Agent execution blind spot: the real governance gap is not whether a model can be tested, but whether its runtime decisions can be constrained. Teams that anchor their programme in OWASP Agentic AI Top 10 and MITRE ATLAS adversarial AI threat matrix will be better positioned to separate model risk from execution risk.

Auditability will become a compliance requirement as much as a security one. If a vendor cannot explain how an action ties back to a prompt, an agent, and a tool call, the organisation will struggle to investigate incidents or satisfy assurance requests when agents touch sensitive data.

For practitioners

• 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. Require the vendor to show how it detects or blocks a wrong tool call after an agent has already accepted a benign-looking instruction.
• 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. If a connector can influence agent behaviour or expose data, it needs the same scrutiny you would apply to a high-risk API path.
• 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. A useful control should intervene during the agent’s action sequence, before downstream systems receive the wrong request or data.
• Require audit trails that bind actions to prompts and agents Make traceability a selection criterion. Your team should be able to reconstruct which agent acted, what prompt or input influenced it, which tool it used, and what decision point led to the outcome without manual log stitching across systems.

Key takeaways

• Agentic AI creates an identity and access problem as much as a model-safety problem, because agents act through tools, permissions, and memory.
• Legacy AppSec, CSPM, and model-scanning controls do not prove runtime safety, which is why vendor claims need live evidence and audit trails.
• The next maturity step is governed agent identity, with MCP-aware access review and blocking controls that operate during execution.

Key terms

• Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions — including calling APIs, writing code, and orchestrating other agents — with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
• Model Context Protocol: Model Context Protocol is an open protocol that lets AI agents connect to tools and data sources. It expands what an agent can reach, so governance has to cover not only the model and its prompts, but also every system that can receive or return agent-driven data.
• Runtime Protection: Runtime protection is a control model that observes application behavior while software is running and blocks unsafe actions as they occur. In Java estates, it helps distinguish active exploit paths from dormant vulnerable code, which is essential when patching is delayed or impossible.
• Agent execution blind spot: Agent execution blind spot is the gap between observing inputs or outputs and understanding what an agent actually did in between. It appears when controls can inspect prompts or model responses but cannot see reasoning, tool selection, memory influence, or multi-step action paths.

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.

👉 Akto's full post covers the vendor call questions, runtime protection criteria, and framework mapping in more operational detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and identity lifecycle control. It helps practitioners connect identity governance to the security programmes that now have to account for AI agents and other non-human identities.