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14/07/2026 4:23 pm
TL;DR: As Microsoft 365 Copilot and other GenAI tools spread content across SharePoint, OneDrive, Teams and Exchange, traditional pattern-based data security platforms struggle with false positives, slow scans and limited visibility, according to Proofpoint. The governance problem is no longer just data discovery, but controlling how human and AI interactions can surface, transform and exfiltrate sensitive information.
NHIMG editorial — based on content published by Proofpoint: Copilot data exposure and the case for modern DSPM
By the numbers:
- AI-powered verification reduces Personally Identifiable Information (PII) false positives by up to 99%, reducing alert fatigue
Questions worth separating out
Q: What breaks when Copilot is added to a legacy data security stack?
A: Legacy stacks usually break at the point where discovery, classification and enforcement stop sharing the same view of risk.
Q: Why does excessive permissions matter more when AI assistants are enabled?
A: AI assistants can search and summarise content across many locations quickly, so old permission mistakes become faster exposure paths.
Q: How do security teams know if AI governance is working?
A: Look for evidence that access decisions are reviewable, permissions are revocable, and exceptions are not becoming permanent.
Practitioner guidance
- Map Copilot-accessible data first Inventory the SharePoint, OneDrive, Teams, Exchange and SaaS locations that Copilot can reach, then rank them by sensitivity and business impact.
- Reassess over-privileged identities before broadening AI access Run entitlement reviews on the accounts and groups that can expose sensitive content to AI assistants, especially where access was accumulated over time.
- Consolidate posture, DLP and insider-risk signals Tie classification, exfiltration monitoring and user-behaviour alerts to a shared data risk view so analysts can see when AI usage changes exposure.
What's in the full article
Proofpoint's full article covers the operational detail this post intentionally leaves for the source:
- Technical validation claims for classification accuracy, false-positive reduction and scanning speed across Microsoft 365 workloads.
- Deployment and licensing implications of moving from an on-premises model to a cloud-native DSPM operating model.
- The vendor's side-by-side comparison of SaaS onboarding, connector complexity and day-to-day administrative overhead.
- The specific product architecture used to unify DSPM, DLP, AI Data Governance and Insider Threat Management.
👉 Read Proofpoint's analysis of Copilot data exposure and DSPM limits →
Copilot data exposure and DSPM gaps: what security teams need now?
Explore further
14/07/2026 4:31 pm
Legacy data security has become a governance mismatch for AI-enabled collaboration. The issue is not that classification no longer matters, but that classification alone cannot govern how GenAI surfaces data across collaboration layers. When tools inherit existing permissions and accelerate content movement, the real control problem shifts from finding data to constraining exposure paths. Practitioners should treat AI-assisted collaboration as a policy and entitlement problem, not just a discovery problem.
A question worth separating out:
Q: Who is accountable when AI tools expose sensitive information or weaken audit evidence?
A: Accountability should sit with the control owner for the workflow, not with the tool itself. Security, IAM, and GRC leaders should define ownership for data-handling rules, approval paths, evidence capture, and exception handling before AI use expands, so responsibility is clear when something goes wrong.
👉 Read our full editorial: Copilot expands data exposure faster than legacy DSPM can govern
