One of the fastest ways to create risk with AI is to deploy Copilot and agents on top of an environment where oversharing, unlabeled data, and incomplete Data Leak Prevention (DLP) coverage already exist.

Most organizations adopting AI assistants at scale have not yet defined which data protection controls to implement, in what order, or how those controls reduce exposure across Microsoft 365 Copilot, custom agents, and third-party generative AI tools.

You’ll get a measured view of where Copilot and agents create data exposure risk today, and a prioritized Microsoft Purview control roadmap sequencing quick wins, medium-term controls, and longer-term governance maturity. Recommendations are mapped to the Microsoft Copilot Control System and ready for executive and technical alignment.

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What This Accelerator Answers:

  • Which controls should be implemented to reduce AI data exposure risk? 
  • How should sensitive data be labeled and protected so classification travels with content? 
  • Where should DLP and related policies be applied for the highest impact? 
  • What governance baseline should be established for Copilot and agents? 
  • What protection roadmap should be executed next, and in what order?

What’s Included?

  • AI Usage and Data Exposure Baseline

    • Current-state assessment of Microsoft 365 Copilot usage and third-party AI tool usage.
    • Sensitive data exposure risks, with a custom data taxonomy where required.
    • Oversharing scenarios amplified by AI, high-risk users, and data patterns connected to AI use.

  • SharePoint Advanced Management

    • Data Access Governance reports.
    • Restricted Content Discovery and Restricted Access Control.
    • Restricted SharePoint Search as temporary mitigation. (not a security boundary).
    • Site access reviews and Everyone-Except-External-Users (EEEU) exposure.
    • Sharing link reports and sensitivity label reports.
    • Agent access to SharePoint and OneDrive content.

  • AI Security Controls Beyond Labels and DLP

    • Data grounding risk for Copilot and agents.
    • Connector and tool access boundaries.
    • Generative AI use involving sensitive data.
    • Browser and endpoint exfiltration paths.
    • Prompt injection as a data exposure scenario.
    • Agent access to high-value SharePoint sites.
    • Secrets, credentials, and regulated data in AI-reachable repositories.
    • Audit readiness for AI interactions.

  • Protection and Governance

    • Current-state view of sensitive-data exposure across SharePoint, OneDrive, Teams, Exchange, and endpoints.
    • Prioritized list of AI-related data risks: over-permissioned sites, stale sharing links, unlabeled regulated content, DLP coverage gaps.
    • Sensitivity-label taxonomy with classification logic and auto-labeling guidance.
    • Governance baseline for Copilot and agents, site privacy, external sharing, guest access, link defaults.
    • Insider Risk indicators, Communication Compliance, and Audit retention recommendations where they support protection.
    • Prioritized protection roadmap sequencing quick wins, medium-term controls, and longer-term governance maturity.