Cybersecurity insights and resources
Govern AI agent actions inside third-party applications
Move from agent access inventories to action-level governance across third-party SaaS applications.
Platform-level AI controls may not show the actions agents take in connected SaaS systems. Teams need clear owners, policies, and enforcement points.
Why this matters
An application-level allowlist cannot by itself explain whether an agent should export records, change sharing settings, or modify business data. Governance requires mapping delegated permissions to specific operations and deciding which should be allowed, logged, reviewed, or blocked. Ownership and enforcement points matter as much as visibility.
What you'll take away
- Assign accountable owners
- Define high-risk actions
- Establish approval thresholds
- Monitor and refine runtime controls
Who should read this
For security governance owners, SaaS application administrators, AI agent program leads, and policy engineers defining what agents may do inside third-party apps. Use the eBook to set action-level permissions, approval gates, and monitoring responsibilities.
From insight to action
Choose one AI-agent workflow inside a third-party application. Define the actions the agent may perform without intervention, the operations requiring approval, and those that should be blocked. Assign a policy owner and specify what runtime evidence would demonstrate that the controls work.