AI Governance

Purpose

AI governance defines how models and automated agents are constrained, monitored, and overseen to ensure safe, accountable, and lawful operation. The goal is not “maximum automation,” but reliable, policy-compliant executionthat procurement and risk teams can evaluate.

Governance principles

• Human accountability: responsibility for outcomes always rests with humans and the customer’s designated owners.
• Policy-constrained autonomy: agents operate within enforceable guardrails for data access, tool usage, and cost/impact limits.
• Proportional controls: higher-risk actions require stronger oversight (approvals, dual control, or pre-validated playbooks).
• Traceability: AI-assisted actions are attributable, reviewable, and reproducible via run logs, policy versions, and decision summaries.
• Continuous evaluation: model behavior and policy effectiveness are reassessed as risks, regulations, and use cases evolve.

Risk tiers and oversight

Oversight is applied based on risk. This allows low-risk actions to run automatically while ensuring high-impact actions receive explicit governance.

• Low: read-only analysis, drafting, summarization. Allowed within policy; sampled review.
• Moderate: reversible changes (e.g., config suggestions, staged workflows). Require stronger logging and post-action review.
• High: irreversible actions or actions affecting sensitive systems. Require explicit approval (HITL), optional dual control, and step-by-step execution constraints.

Policy enforcement (guardrails)

• Policy-as-code checks before tools run (allowed actions, denied actions, and scope boundaries)
• Tenant and role boundaries enforced for data access
• Cost and rate limits per tenant/run; budget caps for long-running workflows
• Human approval gates for high-risk actions; audit trail for each approval
• Versioned policies with rollback; policy change logs and review cadence

Model lifecycle management

• Selection: model/provider selection is reviewed for security, data handling, and intended use.
• Evaluation: regression checks on safety and task performance before rollout; rollback criteria defined.
• Change control: versioning and release notes for prompts, policies, and model routing.
• Monitoring: drift detection via operational metrics and sampled run reviews (deployment dependent).

Common AI risks addressed

• Prompt injection and tool hijacking (policy checks + scoped tools + deny-by-default patterns)
• Data leakage (tenant isolation, redaction patterns, and restricted tool scopes)
• Over-automation (risk tiers, approvals, and reversibility checks)
• Misleading outputs (human accountability, review paths, and evidence-backed decision logs)

Pilot-stage notice

AI governance practices reflect pilot-stage implementation and are refined as UmamiMind approaches General Availability. Where features are deployment-dependent, procurement-ready descriptions are provided under NDA.