"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
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
IT and Compliance
"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
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This practical course focuses on establishing effective governance, security, and operational controls for enterprise AI systems. Participants will assess existing AI usage, identify governance gaps, and align AI deployments with organisational policies and compliance requirements.
The course covers access management, role-based permissions, usage monitoring, and centralised logging for AI tools and APIs. Learners will implement budget controls, policy enforcement mechanisms, and monitoring solutions to manage risk and operational costs.
Hands-on labs explore secrets management, audit logging, compliance reporting, and the detection of unusual or non-compliant AI activity. The course also examines governance frameworks that balance innovation, security, and accountability across the organisation.
By the end of the course, participants will have a scalable AI governance model capable of supporting secure and compliant AI adoption from small teams to enterprise-wide deployments.
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