Overview
This framework turns responsible-AI principles into lifecycle decisions for OpsRabbit use cases, models, prompts, agents, tools and releases. Governance effort increases with data sensitivity, operational authority and consequence of error.
The framework covers intake, risk classification, approval, evaluation, release, monitoring, incidents, exceptions and retirement.
Lifecycle governance
- Define purpose, users, data, actions and accountable owner.
- Classify risk and identify security, privacy, legal and customer obligations.
- Approve model, endpoint, prompts, tools, permissions and deployment boundaries.
- Test accuracy, failure modes, prompt injection, data leakage and action controls.
- Release through controlled change management and customer authorization.
- Monitor incidents, performance, material changes and emerging risks.
Required records
- Use-case and risk assessment
- Model/provider register and deployment configuration
- Data-flow and shared-responsibility records
- Testing, approvals and release evidence
- Incident, exception and corrective-action records
Change control
Material changes to models, prompts, skills, agents, tool permissions, data flows or hosting require reassessment and proportionate regression testing before release.
Governance tiers
Low-risk changes are bounded, reversible and do not materially change data, provider or agency. Moderate-risk changes affect model behavior, prompts, retrieval or non-privileged tools. High-risk changes introduce sensitive data, new providers or regions, write/destructive actions, significant autonomy, or material customer and legal impact. Review depth, evidence and approval increase with tier.
Approval authorities
| Decision | Required participation |
|---|---|
| New AI use case or provider | Product, Engineering, Security and Privacy/Legal as applicable |
| Prompt, skill or agent material change | Product Owner, Engineering and QA |
| Privileged tool or action | Security Architecture, Product Owner and customer authorization |
| Risk acceptance or exception | Named risk owner and appropriate executive authority |
Evaluation requirements
Evaluation uses representative operational cases and adversarial scenarios. Measures may include evidence relevance, unsupported-claim rate, task success, unsafe-action rate, permission enforcement, prompt-injection resistance, sensitive-data leakage and human-approval effectiveness. Acceptance criteria and known limitations are recorded before release.
Inventory and continual improvement
The AI inventory records owner, purpose, users, model/provider, deployment, data, tools, risk tier, approvals, evaluation, monitoring and retirement status. Incidents, customer feedback, provider changes and new threats feed corrective actions and periodic governance review.