Even the Biggest AI Security Vendors Check Answers After They Are Already Generated.

Gartner has a name for the market Truebe operates in and the industry's biggest security vendors are already building for it.

Gartner's Market Guide for AI Trust, Risk and Security Management defines the formal framework as AI TRiSM. It breaks AI oversight into four layers: AI Governance, AI Runtime Inspection and Enforcement, Information Governance, and Infrastructure and Stack. Gartner specifically calls out the top two layers, governance and runtime enforcement, as new to AI and actively consolidating into their own distinct market segment. ^[1]

That runtime enforcement layer, stopping a bad AI output before it causes damage, is the problem Truebe solves, from the other direction.

The biggest players are already moving in.

This isn't a niche concern. Palo Alto Networks and Cisco are both actively building unified AI TRiSM platforms, according to Gartner's analysis. Palo Alto Networks markets a product called Prisma AIRS specifically for this purpose. Smaller vendors in the runtime enforcement category include Lasso Security, Prompt Security, Zenity, and AIShield. ^[2]

Palo Alto Networks' own research shows why the urgency is real. According to the company's State of Generative AI 2025 report, GenAI traffic surged more than 890% in 2024. In 2025, the average monthly number of GenAI-related data security incidents rose 2.5 times and now accounts for 14% of all data security incidents the company tracks. The average enterprise runs 66 GenAI applications and 10% of those are classified as high risk. ^[3]

Palo Alto Networks is the biggest name in the space. It publishes a detailed walkthrough of its own AI TRiSM control flow. An employee asks an AI assistant to summarize a sensitive document. The system checks the assistant's risk profile and the document's data classification. If those checks pass, the request moves to runtime inspection. ^[3]

But the company's own description states the model generates its output first. Only after that happens do separate policy engines evaluate the result. One engine checks whether sensitive information could leak through the summary. Another, in the company's own words, evaluates the output for hallucinations or reasoning errors. A third checks for policy violations. The three scores are blended into a single risk score, and only then does the system decide whether to approve the answer, quarantine it for human review, or block it outright. ^[3]

Why the gate matters more than the inspection.

Every vendor named in Gartner's runtime enforcement category, including the largest pure-play cybersecurity vendor in the world, builds the same way: generate an answer, then inspect it, then decide whether to let it through. That is a real improvement over no inspection at all but it's not the same as never generating an unapproved answer in the first place.

Truebe's Gated Truth Architecture removes the generate-first step entirely for anything a user actually sees. An AI still drafts candidate answers from your documents, but that draft is not evaluated after the fact by an automated policy engine. It is reviewed and approved by a human before it can ever be shown to anyone. Once approved, the system only retrieves and repeats that exact answer. There is no live generation left at the moment someone asks a question and therefore nothing for a policy engine to catch after the fact because nothing ungated ever reaches the user to begin with.

The practical takeaway.

If a vendor tells you their platform catches hallucinations, ask them where in the process it actually does it. If the honest answer is after the model generates a response, you're looking at the same architecture Palo Alto Networks, Cisco, and every other named AI TRiSM vendor currently ship: a system that still lets AI generate its own response, not one where each answer was approved by a person before anyone ever saw it.