Identity and least privilege
Users and agents receive scoped access to the smallest necessary set of data and actions.
Engineering controls
Trust is designed into the workflow.
We define what an AI system may know, recommend, decide, and change, then test those boundaries before production release.
Data and retrieval boundaries
Source permissions, data classification, retention, and model-provider handling are defined before information reaches a model.
Human authority
Consequential, uncertain, or exceptional decisions route to named reviewers with context and override controls.
Evaluation and auditability
Representative tests, action logs, monitoring, and incident evidence support responsible release and ongoing operation.
Release discipline
Controls follow the system from discovery through operation.
- 01Discovery
Classify data, users, actions, risk, and contractual constraints.
- 02Design
Set permission boundaries, approval thresholds, provider choices, and failure behavior.
- 03Validation
Test quality, access isolation, prompt attacks, unsupported answers, and tool misuse.
- 04Operation
Monitor outcomes, latency, cost, exceptions, overrides, and incidents.
What is defined per engagement
No generic badge replaces your actual requirements.
Architecture and controls depend on the data, users, jurisdictions, systems, model providers, and impact of the workflow.
- Deployment and network boundaries
- Model-provider and data-processing terms
- Authentication, roles, and action permissions
- Retention, redaction, and deletion rules
- Evaluation, monitoring, and incident evidence
- Human approval and escalation responsibilities