License stress cases from policy you have already applied: a user who tried to get a refund they should not, a prompt that tried to skip a check, a robot request that should have been refused. Record the attempt, the policy, the correct refusal or escalation, and the near-miss. This is red-teaming as professional judgment, not a cookbook for harm.
This venue is a Wing in your Human architecture. Every exercise creates Rooms organized by Hall type. Data quality is tracked via Knowledge Graph triples.
Meet all milestones to unlock buyer engagement. Hybrid threshold — both content volume AND time consistency required.
Entries
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Labs and safety vendors pay for private, domain-specific failure cases. Public jailbreak lists are burned. Your workplace near-misses are not.
8–14 hours per module · 4 steps · Each step builds your architecture
Stress cases (JSON: attempt, policy, correct_action, near_miss, severity)
When readiness is achieved, an AAAK-compressed summary of your data is generated for buyer evaluation — privacy-preserving, compact, and instantly readable by any AI system.
Safety and constitutional training consume domain-specific refusal cases.
Red-team and safety eval programs for labs and enterprises.
Automated and human red-teaming for model and agent products.
Safety programs for robots around people. Needs physical-world refusal cases.
Open the Data Builder to complete guided exercises. Each entry builds Rooms, Halls, and Knowledge Triples in your Human architecture — reaching buyer readiness in 30–60 days.