What should enterprises look for when evaluating an AI security solution in 2026?

Enterprises should look for coverage across the full AI lifecycle, meaning pre deployment testing through red teaming, runtime protection once systems are live, and continuous monitoring that catches issues as they emerge rather than only at scheduled review points. A solution that only performs a point in time scan misses the reality that AI systems, especially agentic ones, change behavior as models get updated and new tools get integrated.

Support for agentic workflows specifically matters more each year, since most of the real risk in modern AI deployments comes from tool use and multi step agent behavior rather than a single LLM call in isolation. A solution built primarily to evaluate chat style interactions will have limited visibility into what happens when an agent has access to file systems, external APIs, or other agents.

Other important criteria include model agnostic coverage, so the solution works consistently whether an organization is using one cloud provider's models or several, automated testing cadence rather than manual and infrequent assessments, and clear reporting that maps to the compliance frameworks the organization needs to satisfy.

Akto's platform is built around this full lifecycle approach, with Atlas handling discovery of AI agent usage across an enterprise and AI guardrail enforcement, and Argus providing continuous red teaming and runtime protection specifically for homegrown agentic AI applications. Together they aim to cover the gap between initial testing and ongoing production risk that many point solutions leave open, which is increasingly the deciding factor enterprises weigh during evaluation.