How to safely adopt Microsoft 365 Copilot and other AI agents
For many enterprises, the first real exposure to generative AI comes through tools like Microsoft 365 Copilot and AI assistants embedded in SaaS platforms. These systems inherit years of permissions, shared drives, and forgotten repositories, suddenly making sensitive data searchable and synthesizable by AI.
Security teams already struggle with fragmented visibility across cloud, SaaS, and legacy environments. DLP, IAM, and logging tools each provide part of the picture but lack an AI-aware view of how copilots and agents access data. AI Data Readiness helps organizations understand where sensitive data lives and how AI systems interact with it before enabling AI at scale.
In this guide, you’ll learn how to:
Sentra helps organizations achieve AI Data Readiness by discovering sensitive data, mapping AI-accessible datasets, and connecting identities, groups, and AI service roles into a unified risk view.
For many enterprises, the first real exposure to generative AI comes through tools like Microsoft 365 Copilot and AI assistants embedded in SaaS platforms. These systems inherit years of permissions and shared repositories, making sensitive data searchable by AI. Security teams already struggle with fragmented visibility across cloud, SaaS, and legacy environments. DLP, IAM, and logging tools show part of the picture but lack an AI-aware view of how copilots and agents access data. AI Data Readiness helps organizations understand where sensitive data lives and how AI systems interact with it before enabling AI at scale.
- Discover and inventory AI copilots, agents, and applications
- Identify which datasets and repositories AI systems can access
- Classify sensitive data across cloud, SaaS, and legacy environments
- Treat AI agents as identities and enforce least-privilege access
- Provide audit-ready evidence that AI access to sensitive data is governed