AI red teaming
AI red teaming by domain experts
Normal grading measures typical behaviour. Red teaming hunts for the inputs that break your model. Pathwize puts credential-verified experts on the attack and hands back reproducible failure cases, not vague warnings.
Talk to us about ai red teaming
Where the usual approach falls short
Vague findings you cannot act on
"The model can be unsafe" is not a finding. Without a reproducible case, your team cannot fix the issue or prove the fix held.
Generalists miss domain-specific harm
A security researcher, a clinician, and a lawyer each know where their field hides danger. A generalist red team never thinks to try those inputs.
No audit trail for safety work
Safety reviews, and increasingly regulators, want evidence of what was tested and what broke, not a summary you cannot substantiate.
What you get
Reproducible failure cases
Every finding records the exact input, the harmful output, why it is a problem, and the conditions that trigger it.
Expert adversaries
Credential-verified specialists probe your model where their domain hides risk, surfacing failures a generalist would never reach.
Feeds evals and fixes
Findings plug straight into your safety evaluations and your patch list, so you can measure whether a class of failure is getting rarer.
Signed, auditable record
A tamper-evident trail of who tested what and what broke, exactly the evidence a safety review expects.
How the engagement runs
Scope the risk
Define the capabilities and harms that matter for your model and the domains to probe.
Matched expert red team
Credential-verified specialists in the relevant fields are assembled for the engagement.
Probe and document
Experts attack the model in a sandboxed workspace, capturing each failure with a reproducible case.
Auditable findings
Receive a prioritised set of failure cases plus a signed record of everything tested.
What you receive
- Reproducible failure cases with input, output, and trigger conditions
- Severity-ranked findings mapped to capabilities
- A signed, tamper-evident record of what was tested
- Cases formatted to feed your safety evaluations
FAQ
AI red teaming, answered
Experts deliberately probe a model for failures, jailbreaks and unsafe outputs, and document reproducible failure cases. The output is a set of specific, repeatable failures, not a score.
Effective red teaming often needs domain expertise, not just adversarial instinct. A clinician, a lawyer, and a security researcher each surface failures a generalist would never think to try.
A prioritised set of reproducible failure cases, each with the input, the harmful output, and the trigger conditions, plus a signed record of everything that was tested.
Verifiable ai red teaming, on your data
Source expert data with provenance built in, EU-native and audit-ready. Book a demo with your ML and compliance teams.