Overcome AI Governance Challenges with Automation

According to Gartner, half of organizations say their biggest Responsible AI hurdle is turning governance principles into consistent, repeatable processes.

What’s Inside

AI governance only works when policies translate into consistent controls over workspaces, permissions, and data. This eBook explores three ways automation helps organizations put governance into practice.

• Workspace provisioning. Delegate authority to reduce IT bottlenecks while maintaining governance standards
• Access management for Microsoft Teams and Groups. Use automated security reporting and policy enforcement to keep permissions in check and uphold business policies.
• Data lifecycle management. Apply automated information lifecycle policies to control sprawl and determine when content should be archived or deleted.

What You’ll Learn

• Establish a delegation framework that distributes access management responsibilities while maintaining governance standards.
• Automate security reporting and policy enforcement for Microsoft Teams and Groups.
• Monitor permissions to identify restricted data or out-of-policy access.
• Implement information lifecycle policies to control content growth and reduce data sprawl.

Strengthen AI Governance with Automation

Reduce manual AI governance work while maintaining consistent controls. See how AvePoint can help streamline workspace provisioning, access management, and data lifecycle processes so IT teams can focus on higher-value priorities.

Frequently Asked Questions

Why is AI governance a challenge for organizations using generative AI?  

Many organizations roll out generative AI before they have responsible AI measures in place. Generative AI also speeds up content creation and sharing, which makes it harder to control workspaces, access, and sensitive data.

How does automation help with AI governance in Microsoft 365?  

Automation turns governance policies into consistent controls. It helps IT teams manage workspace provisioning, Microsoft Teams and Groups access, and data lifecycle processes with less manual work.

Why does data lifecycle management matter for generative AI?  

Generative AI can create large volumes of content, and old workspaces and stale data add to data sprawl. Automated information lifecycle policies help decide when content should be archived or deleted. The AvePoint eBook explains how to put this into practice.