NEW RESEARCH: Your Sandbox Is Made of Glass
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Alternative to Cordon AI
Cordon AI orchestrates models to find and validate security risks in your infrastructure. Trinitite governs what your AI agents do in production and produces signed, reproducible evidence of every decision. If your real need is runtime AI governance you can prove, start here.
What Cordon AI is
Cordon AI is an AI-native security validation platform. It orchestrates multiple specialized models to discover, validate, and remediate security vulnerabilities in authorized environments, shipping confirmed findings with reproducers and fix recommendations to a human reviewer.
Where they’re strong
Continuous, multi-model security testing of applications and infrastructure — surfacing and validating real vulnerabilities with low false positives. If you are buying security validation, that is their strength.
The difference
Cordon AI validates the security of your systems. Trinitite governs the behavior of your AI agents at runtime and proves it. We sit inline on every model output and tool call, return a five-valued verdict — pass, correct, mask, block, or escalate — and sign a hash-chained, externally anchored receipt that reproduces bit-for-bit. The verdict is not an LLM-as-judge that drifts day to day; it runs on a determinism-fixed kernel, so the same input yields the same bytes on any cluster. For teams whose question is “can I prove what my AI did, and stop it when it is wrong?”, that is the wedge. The two can coexist: validate your infrastructure with one, govern and evidence your AI with the other.
Side by side
Dimension
AI security validation
Trinitite
Primary job
Find & validate security vulnerabilities
Govern AI agent behavior at runtime
When it runs
Against your assets, on a testing cadence
Inline on every production call
The judge
Multi-model consensus pipeline
Deterministic per-tenant Auditor — same input, same bytes
Output
Validated findings + reproducers
Signed, anchored, replayable verdict per call
Acts on live traffic?
Decision-support for security teams
Block / correct / mask before the output ships
Audit / insurance evidence
Finding lifecycle logs
Verifiable in a browser; feeds audit + insurance layers
Questions to ask any vendor
01
Once a model ships, what governs what it does in production — and can you prove each decision afterward?
02
Is that proof reproducible bit-for-bit, or does the same input give a different verdict on a busy day?
03
Does the tool stop a non-compliant AI output before it reaches a user, or only chart that it happened?
04
When an agent is prompt-injected, what judges the action itself — a blocklist it can talk around, or an independent check that ignores the agent’s reasoning?
05
Is the evidence externally anchored, so not even the vendor can backdate it?
FAQ
Run the free 1,000-log pre-audit and put a signed, reproducible Trinitite report next to whatever you’re evaluating today. Verify it in a browser, no NDA.
Trinitite
AI governance that catches mistakes, proves compliance, and shows the board what it saved—in dollars.
Trinitite is built by Fiscus Flows, Inc.
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