NiyamR AI Red Teaming attacks a company’s AI on purpose, with the same tricks real attackers use, then hands over the evidence and proves the gaps are closed.
An AI system can be talked into breaking its own rules, leaking private data, or taking actions it should not. Ordinary security tests do not check for any of this.
Companies are putting AI in front of customers faster than anyone can test it. The first real test is often a public incident.
Enterprise customers, auditors and regulators increasingly want evidence that an AI system was attacked and held up.
Sustained attempts to make the AI break its rules, reveal its instructions or produce harmful answers.
Attempts to hijack the tools and actions an AI agent has been given.
A check of the servers and software the AI runs on for known weaknesses.
Pick one AI system and agree the rules of the test.
196+ techniques, covering every major category of AI risk.
Each finding comes with a severity score and the evidence behind it.
Findings become tasks. NiyamR attacks again to prove the gap is closed.
Checks the servers and software a company’s AI runs on. It recognises over a hundred AI components and confirms which known weaknesses are really exposed.
Confirmed exposures, not version guesses
Tests whether an AI can be talked into breaking its own rules. The result is a score showing how often each kind of attack succeeds.
A success score per attack method
The complete assessment in one engagement, with one evidence pack. It is everything a customer or auditor asks to see.
Everything a buyer or auditor asks to see
A finding does not end in a PDF. It becomes a tracked task, and AI Security can block the gap on live traffic the same day.
Models, agents and the infrastructure beneath them, tested together in one engagement.
The Red Team Readiness Pack is written for the people who ask the hard questions: auditors, clients and boards.
Every engagement closes with a re-test, so the customer can show the problem is gone.
AI regulation and enterprise procurement increasingly expect adversarial testing before an AI system goes live.
Each new AI product, feature or agent is a system that needs testing, and re-testing when it changes.
Each engagement creates demand for governance to track the fixes and security to enforce them.
Start with the registry. Every rule runs in monitor mode on your own hardware until you have seen your own traffic and decided what to turn up.