Governance

Governance gives teams the controls needed to operate AI safely in production: policy rules, ownership, audit trails, SLA visibility, sensitive data handling, and enterprise controls.

What Governance Does

Anosys connects governance rules to traces, evals, alerts, dashboards, process units, and cost intelligence. Instead of reviewing behavior after the fact, teams can monitor and enforce expected behavior continuously.

When To Use It

Use governance when:

AI workflows touch regulated, sensitive, or customer-impacting processes.
You need audit trails for model, prompt, tool, and policy behavior.
Teams need ownership and SLA tracking.
Sensitive data must be redacted or encrypted.
Leadership needs evidence that AI is controlled in production.

Prerequisites

Define policy expectations.
Capture workflow, owner, environment, model, user, and data sensitivity fields.
Decide what should alert, block, report, or require review.

Step-By-Step Setup

Open Governance or Rules.
Create policies for sensitive data, model use, eval scores, approved tools, SLA, or ownership.
Scope policies by workflow, environment, team, customer tier, or region.
Attach actions: alert, report, route, tag, or create incident.
Review audit trails and failed examples.
Tune thresholds and owners.

Example Policy

name: production-sensitive-data-control
scope:
  environment: production
  workflow: support-agent
rule:
  sensitive_data_detected: true
action:
  redact: true
  alert: governance-review
  owner: security

What Appears In The Console

Governance views show policy status, violations, affected workflows, trace evidence, owners, SLA health, audit history, and incident summaries.

Common Mistakes

Defining governance as static documentation instead of live production rules.
Missing ownership fields.
Alerting on policy failures without evidence links.
Not separating development, staging, and production scopes.