Highlights
- Identify governance gaps in AI deployments
- Enforce acceptable use via controls
- Implement role-based AI access
- Configure centralised AI logging
- Build a usage dashboard
- Set cost controls and alerts
- Detect and alert on policy violations
- Manage API credentials securely
- Produce a compliance audit report
- Design a scalable governance framework
Course Details
Governance gap audit:
mapping current AI tools against a standard control checklist to identify what is unmonitored or uncontrolled
Acceptable use policy workshop:
translating written policy intent into specific, enforceable technical controls that can be tested
RBAC configuration lab:
setting up access tiers for AI tools and APIs based on role, team membership, and data sensitivity level
Centralised logging build:
routing all AI API calls through a gateway or proxy that captures structured logs in one place
Usage dashboard lab:
building a live view showing model usage volumes, cost by team, and individual user activity over time
Budget controls:
configuring spend limits, alert thresholds, and hard stops per project inside your AI platform or gateway
Policy violation detection:
writing detection rules that flag unusual usage patterns, prohibited content, or out-of-hours access
Secrets management lab:
migrating API keys into a vault, setting up automated rotation, and producing an access audit
Audit report build:
generating a structured compliance report from your logs that is readable by a legal or regulatory audience
Scaling framework design:
governance patterns and architecture choices that work for 10 users today and remain viable at 1000
Who should attend
IT and Compliance
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
