ZynoviqDon't only test what AI should do. Test how it can fail.
Representative test cases show how your AI behaves on a normal day. Professionals know where the work actually goes wrong: the exception nobody remembers, the instruction that contradicts policy, the request that should have been refused. Zynoviq red teams your AI with those scenarios, on purpose.
Professional red teaming — not security testing
The term "red team" often means attacking systems. Ours is different. We look for failures of professional judgment: places where your AI gives a confident wrong answer, follows the wrong process, or acts when it should have stopped.
We do not perform penetration testing, prompt-injection hacking, data-leakage testing or cyber testing. For security testing of your infrastructure, use a qualified security firm.
What we try to break
| Category | What the scenario does |
|---|---|
| Professional ambiguity | Facts that support more than one reading, where a practitioner would ask before answering |
| Exceptions | Cases where the general rule does not apply, such as a credit that is normally blocked but allowed by a specific condition |
| Contradictory instructions | A user request that conflicts with policy, or two documents that disagree |
| Incorrect assumptions | A question built on a false premise the AI should challenge, not accept |
| Policy manipulation | Pressure, urgency or plausible reasons offered to bend a written rule |
| Tool misuse | Situations that tempt the AI to use the wrong tool, record or data source |
| Workflow exploitation | Attempts to skip a step, reorder approvals or complete a process out of sequence |
| Authorisation failures | Cases where the correct behaviour is to refuse, hold or escalate to a human |
Agents fail in their own ways
An agent that acts inside a system can fail even when its reasoning sounds right. For agents, our scenarios also look for these failure modes — in your sandbox or test environment only:
| Failure mode | What it looks like |
|---|---|
| Loops | The agent repeats the same step or tool call without making progress |
| Wrong tool selection | It picks a tool, record or source that cannot answer the question correctly |
| Incorrect parameters | Right tool, wrong inputs — the wrong GSTIN, period, amount or document reference |
| Premature actions | It acts before the required checks are done, such as posting a credit before confirming eligibility |
| Changing records it should not | It edits, reverses or overwrites data outside its task, leaving the system in a wrong state |
| Unsafe execution | It completes an action that should have been held for human approval or refused |
Why professionals, not generic testers
A generic tester can write a tricky question. Only a practitioner knows which situations actually trip people up: the rule with a little-known proviso, the document that looks valid but is not, the approval that a junior would grant and a senior would not. Our red-team scenarios are written by professionals qualified in the specific capability being tested, and the correct response is adjudicated in the same way as all our ground truth.
For examples of this kind of material, see our edge cases and failure data and the GST ambiguity pack.
From one failure to a stronger benchmark
- 01FailureThe scenario your AI got wrong, and how.
- 02ExplanationWhy it is wrong, the correct response and the rule behind it.
- 03Regression testA permanent case re-run after every change.
- 04Future benchmarkAdded to the ground truth for the next evaluation.
The rule: every red-team failure becomes a regression test you receive. Each failure is delivered as a structured case with its certified expected behaviour, added to your regression set and re-run after every change, so the same mistake cannot return unnoticed. Red-team findings feed straight into regression, so the work keeps paying off after the mission ends.
What the red-team report contains
Every finding is recorded with the same fields, so your team can reproduce it, prioritise it and confirm the fix.
| Field | What it records |
|---|---|
| Attack scenario | The professional situation designed to make the AI fail |
| System response | What your AI actually said and did, including tool calls where exposed |
| Expected safe behaviour | What a competent professional would expect: the right answer, a question, a hold or an escalation |
| Actual failure | How the response departs from the expected behaviour |
| Severity | How serious the failure would be in practice |
| Reproducibility | How often the failure recurs when the same scenario is re-run |
| Affected component | Where it went wrong: retrieval, reasoning, tool call or policy layer |
| Recommended mitigation | What your team could change to address it |
| Regression test generated | The permanent case added to your regression suite |
See a sample red-team report → (illustrative, not a customer result)
How a red-team engagement works
- Fixed engagement — an agreed scope, a set of professional adversarial scenarios, and a red-team report at the end. Quoted per mission.
- Optional recurring re-tests — the failures found, plus new scenarios, re-run after major changes or on a schedule you choose.
See packages and how to start for how this fits with evaluation and regression.
When to red team
- Before an agent is allowed to take actions without human approval
- Before AI handles a workflow where an error has financial or legal consequences
- After a major change to the model, tools or permissions
- When your evaluation scores look high, and you want to know what they are missing
AI red team — FAQ
Is this security or penetration testing?+
No. Our red team uses professional scenarios to find where your AI makes the wrong business or professional judgment. We do not test infrastructure, attempt prompt-injection attacks or perform cyber testing. For security testing of your infrastructure, use a qualified security firm.
Which domains can you red-team today?+
Red-team missions are available in our live domain, GST and Indian indirect tax, and on request in other domains where we have qualified professionals. The domains page shows each status.
How is red teaming different from evaluation?+
Evaluation measures your AI on representative cases. Red teaming looks deliberately for the cases where it breaks — the exceptions, traps and pressure that representative samples rarely include.
What do we get at the end?+
A red-team report: each failure found, with the scenario, what your AI did, the expected safe behaviour, severity, reproducibility, the affected component, a recommended mitigation and a regression case. Every failure becomes a regression test you receive. See a sample red-team report.
Do red-team cases use real data?+
No. Every scenario is synthetic and runs only in your sandbox or test environment.