What are the most important evaluation criteria when comparing a purpose-built AI security platform to a legacy security vendor that has added AI coverage?

The most important criterion when comparing a purpose-built AI security platform to a legacy vendor with added AI coverage is whether the platform was designed around agentic AI execution risk from the start, or whether AI support was bolted onto existing cloud or application security tooling. The distinction shows up immediately in areas that matter operationally: runtime behavioral monitoring, MCP security controls, continuous AI red teaming, and autonomous workflow visibility.

Legacy platforms adapted for AI typically offer static scanning of model configurations or API traffic inspection rebranded as AI security. They lack native understanding of how autonomous agents interact with tools, permissions, APIs, and enterprise systems across multi-step workflows.

Purpose-built AI security platforms should be evaluated on whether they provide:

• Native AI agent discovery and inventory, not just cloud asset scanning with AI labels

• Runtime behavioral monitoring of autonomous agent actions, not only static configuration checks

• Prompt injection detection that accounts for indirect and multi-hop attack vectors

• Tool misuse analysis that evaluates whether agents can be manipulated into unauthorized tool calls

• Continuous AI red teaming purpose-built for agentic attack paths, not generic adversarial prompt testing

• Inline MCP enforcement that can intercept and block unsafe traffic before execution

• Multi-agent relationship mapping to detect cross-agent manipulation and trust boundary failures

Akto was built specifically for agentic AI security. ATLAS, Akto's employee AI security product, secures employee AI usage, shadow AI, and browser-based interactions. ARGUS, Akto's runtime agent monitoring product, secures internally built agents, runtime behavior, and MCP ecosystems. Together, they provide continuous testing, runtime enforcement, posture management, and contextual visibility across the full AI agent lifecycle.