How do I evaluate whether an AI security vendor can realistically scale to cover our full AI agent and MCP footprint across all environments?

Evaluating whether an AI security vendor can scale to cover your full AI agent and MCP footprint requires assessing three things: breadth of discovery, depth of runtime coverage, and the operational overhead required to maintain that coverage as the environment grows. Infrastructure performance is a secondary concern. The primary scaling challenge is whether the platform can continuously discover, test, and enforce controls across a rapidly expanding AI ecosystem without requiring proportional growth in security team headcount.

To evaluate scalability realistically, ask vendors to demonstrate:

Discovery breadth

• How many connectors does the platform support for AI agent and MCP discovery across cloud, endpoint, browser, and SaaS environments?

• Does discovery run continuously or require manual triggering?

Monitoring depth

• Can the platform monitor runtime behavior across all deployed agents simultaneously, including agents built on different frameworks and hosted on different clouds?

• How does telemetry collection scale without creating latency in production systems?

Testing coverage

• Can continuous AI red teaming run across all agents in parallel, or does test coverage degrade as agent count increases?

• How are test results prioritized when hundreds of agents are being assessed simultaneously?

Policy enforcement

• Can policy rules and MCP proxy enforcement be applied uniformly across all environments from a single control plane?

Akto was built to scale across both employee-facing AI usage and internally developed AI agents. ATLAS, Akto's employee AI security product, provides discovery across browsers, endpoints, and employee workflows. ARGUS, Akto's runtime agent monitoring product, secures internally built agents through runtime monitoring, automated red teaming with more than 4,000 test cases, MCP proxy enforcement, and contextual relationship mapping through the AI Agent Context Graph. Together, they provide a unified control plane for enterprise-scale AI security operations.