AI Governance
Public overview of how QUOKKA AI approaches model safety, evaluation, access control, and human review.
We build AI-enabled products with the assumption that unsafe automation, weak guardrails, and unverified outputs create real operational risk.
Last updated: August 20, 2026
We scope safety controls as part of the delivery architecture, not as a cosmetic layer after shipping. This includes access boundaries, environment separation, auditability, and explicit approval paths for sensitive actions.
High-risk automations are designed to fail safely, degrade clearly, and surface human review checkpoints when confidence or context is insufficient.
We use environment separation, permission boundaries, logging, usage monitoring, and rollback paths to reduce harm from bad prompts, weak integrations, or unexpected model behaviour.
Where appropriate, we also include rate limits, content moderation, retrieval constraints, and tool-use restrictions.
Model-assisted systems should be tested against realistic failure modes, not only happy-path demos. We encourage evaluation on edge cases, hallucination risks, policy boundaries, and operational escalation paths.
When automation touches customer support, business workflows, or compliance-sensitive data, human oversight remains a first-class design requirement.
