Cyberhaven vs Proofpoint: what data security teams should rethink

TL;DR: Cyberhaven and Proofpoint solve different halves of the modern DLP problem: endpoint lineage versus email-first cross-channel coverage, while both still leave SaaS data at rest and AI-agent traffic unevenly protected, according to Strac. The real governance issue is that detection-led models do not fully address inline remediation across browser prompts, MCP traffic, and data-native workflows.

NHIMG editorial — based on content published by Strac: Cyberhaven vs Proofpoint: DLP and insider risk compared

By the numbers:

• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.

Questions worth separating out

Q: What breaks when DLP is the only control on email risk?

A: DLP can reduce outbound leakage, but it does not inventory what sensitive data already lives in mailboxes.

Q: Why do AI-agent workflows complicate data protection and access governance?

A: AI agents can move sensitive content through prompts, retrieval steps, and tool calls faster than traditional review cycles can react.

Q: How can security teams tell whether DLP is actually reducing risk?

A: Look for better prioritisation of high-value data, fewer noisy alerts, and clearer visibility into which identities can reach sensitive content.

Practitioner guidance

• Define the primary exposure surface Map whether your dominant risk is email, endpoint copy, SaaS storage, browser GenAI, or AI-agent traffic before choosing a DLP operating model.
• Separate investigation from remediation Use lineage and detection for forensic context, but require inline redaction, masking, tokenisation, or revoke actions for high-value data paths.
• Extend policy to AI workflows Add explicit controls for browser prompts, MCP tool calls, and AI-assisted copy-paste paths so sensitive data is not handled as generic web content.

What's in the full article

Strac's full article covers the operational detail this post intentionally leaves for the source:

• Channel-by-channel remediation workflows for email, SaaS, endpoint, browser, and AI-agent data paths
• Implementation details for redaction, masking, tokenisation, block, warn, delete, revoke access, quarantine, and label actions
• Product-specific coverage claims across Slack, Gmail, Google Drive, Microsoft 365, Salesforce, and MCP connectors
• Deployment and compliance details for teams evaluating how the platform fits existing data controls

👉 Read Strac's comparison of Cyberhaven vs Proofpoint for DLP and insider risk →

Cyberhaven vs Proofpoint: what data security teams should rethink?

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