What is the difference between AI security solutions and traditional application security tools?

Traditional application security tools, including static analysis, dynamic testing, and web application firewalls, were built around deterministic code paths and a well established catalog of known vulnerability classes. These tools work by checking code or traffic against patterns that are known in advance to be problematic, which works well when the underlying system behaves predictably given the same input.

AI security has to account for a fundamentally different kind of system. Model behavior is non deterministic, meaning the same input can produce different outputs across runs, and the attack surface includes natural language manipulation techniques like prompt injection that do not resemble a traditional code vulnerability at all. There is no equivalent to a CVE database for emergent risks that arise from an agent's autonomy or from unexpected interactions between a model and the tools it has access to.

This difference means AI specific platforms need testing methodologies adapted specifically for how LLMs and agents actually fail, rather than reapplying traditional AppSec techniques to a system they were never designed to evaluate. Continuous red teaming, rather than a static scan performed once before launch, becomes necessary because the attack surface shifts every time a model gets updated or a new tool gets integrated into an agent's capabilities.

Akto applies adversarial testing methodologies built specifically for this context, treating AI security testing as an ongoing process rather than a one time gate, which reflects how differently these systems behave compared to the deterministic software that traditional AppSec tooling was designed around.