Organizations adopting AI systems face a growing set of regulatory, security, and operational requirements. This guide outlines the practical steps, controls, and considerations that matter most when building a trustworthy AI governance program, without adding unnecessary process overhead.
Why AI Governance Matters
As AI systems take on more consequential decisions, the gap between experimentation and accountable deployment widens. Governance is not a one-time checklist; it is a continuous discipline that spans data handling, model behavior, access control, and audit readiness.
Core Requirements
Most regulatory frameworks converge on a similar set of expectations. The list below reflects the baseline requirements referenced throughout this guide.
- Documented data provenance and lineage for training and inference inputs
- Role-based access controls across model, data, and infrastructure layers
- Auditable logging of model decisions and system changes
- Defined incident response procedures for model failures or misuse
- Periodic review of model outputs against defined risk thresholds
Key Controls
Access Management
Restrict who can query, modify, or deploy models, and maintain records of every permission change.
Monitoring & Logging
Capture model inputs, outputs, and decision paths in a format suitable for internal and external audit.
Change Management
Require review and approval before model updates, prompt changes, or data source changes reach production.
Practical note
Start with the controls that map directly to your highest-risk use cases rather than attempting full framework coverage on day one.
How Trussed AI Approaches This
{{approach_description}}