What are the core pillars of an AI TRISM framework?

Gartner's AI TRiSM model spans four general areas: explainability and model monitoring, ModelOps, application security specific to AI systems, and privacy and data protection. Explainability and monitoring cover whether an organization can understand and track why a model produced a given output. ModelOps deals with the operational lifecycle of deploying, updating, and retiring models in a controlled way. Application security addresses the unique attack surface AI systems introduce, and privacy protection covers how sensitive data is handled as it flows through these systems.

In practice, the application security pillar tends to be where enterprises have the largest gap relative to the other three. Most organizations already have some form of model monitoring and data governance in place from earlier compliance work, but few have tooling specifically built to continuously test AI agents for adversarial behavior or to monitor them at runtime the way they would monitor a conventional web application for intrusion attempts.

That gap is particularly pronounced for agentic systems, where the risk is not just what a model says but what an agent does with the tools and permissions it has been given. A model can pass every explainability and monitoring check and still be manipulated into taking a harmful action through a tool call that none of those checks were designed to evaluate.

Akto's Argus product is built specifically to address this security pillar gap, providing automated and continuous red teaming alongside runtime protection for homegrown LLM and agentic AI applications, which gives the TRiSM framework a working technical layer rather than leaving that pillar as policy language alone.