Automated safety classifiers and benchmark evaluations catch well-known failure modes in well-known formats. They do not catch novel attack strategies, domain-specific vulnerabilities, multi-turn jailbreaks, or the creative social engineering that actual adversarial users will attempt against your deployed model. Only human red-teamers can do that.
Our red-team is made up of ML engineers, security specialists, and domain experts (doctors, lawyers, educators) who understand both how AI models fail and how your specific domain's failure modes manifest. A medical AI has different safety-critical failure modes than a financial AI or a general-purpose assistant — and our red-team is calibrated to your specific deployment context, not a generic checklist.
Every finding in our red-team report is a specific, reproducible example: the exact prompt or prompt sequence that elicited the failure, the model output, the severity classification, the harm category, and the recommended remediation. We do not deliver vague risk assessments — we deliver a graded catalogue of specific vulnerabilities, ordered by severity, with corrective RLHF data you can use to patch the highest-priority findings.
Automated safety filters vs. human red-teaming: what's the difference?
Automated safety filters (like Llama Guard, Perspective API) classify known harmful content patterns. They catch the obvious cases. Human red-teamers attack the seams: multi-step jailbreaks where no individual step is harmful, domain-specific manipulations that require subject-matter knowledge to recognise as dangerous, social engineering patterns, indirect prompt injections, and novel attack vectors that automated tools have never seen. The two are complementary — you need both.
When should you red-team your model?
Before initial deployment, before any significant capability upgrade (new model version, new fine-tuning data, expanded context length), after reports of unexpected model behaviour from users, and periodically for production models (every 6–12 months). The frequency should scale with the stakes of the deployment — a medical AI used for clinical decisions warrants quarterly red-teaming; an internal knowledge assistant warrants annual.
What comes after red-teaming?
The report categorises every finding and provides specific corrective RLHF data: preference pairs where the correct response is the safe rejection and the rejected response is the harmful output the model actually produced. These pairs are ready to mix into your RLHF training pipeline to reduce the frequency of the identified failure modes. Critical findings get corrective pairs in the same delivery; medium/low findings get them within 5 business days.
