Cyberhaven vs Proofpoint: what DLP teams miss in SaaS and AI

At a glance

What this is: This comparison contrasts endpoint lineage DLP with email-first cross-channel DLP and finds both are incomplete for SaaS, browser GenAI, and AI-agent traffic.

Why it matters: It matters because identity, access, and data-governance teams need controls that follow sensitive data across endpoints, cloud apps, and AI workflows, not just across email or a single device.

By the numbers:

• The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities.
• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.

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

Context

Data loss prevention only works when it matches where sensitive data actually moves. In many enterprises, that now includes SaaS applications, browser-based GenAI, endpoint devices, and AI-agent tool calls, which traditional channel-first models often treat unevenly.

This article is a comparison of two established DLP approaches, but the real issue for identity and security programmes is governance scope. When data protection is tied too closely to email or endpoint telemetry, the organisation can miss the identity, privilege, and workflow paths that expose sensitive content across modern work surfaces.

Key questions

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. That leaves teams unable to answer audit, retention, or incident response questions without manual exports and guesswork. The missing control is content visibility at rest, not just message filtering.

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. When policy is only attached to users or endpoints, it misses the runtime path the data takes through the agent. Teams need controls that understand the transaction, not just the identity that initiated it.

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. If the programme still depends on blocking events at the edge, it is probably measuring activity rather than reducing exposure.

Q: Should organisations keep separate controls for email, endpoint, SaaS, and AI use?

A: Separate tools can be justified operationally, but separate policies create blind spots if each channel is governed differently. The better question is whether all four surfaces share one policy model for classification, exception handling, and remediation. If they do not, attackers and careless users will route around the weakest path.

Technical breakdown

Data lineage versus channel-based DLP

Data-lineage DLP traces a file, field, or record as it moves, transforms, and is copied across systems. That gives investigators context about origin, movement, and propagation, which is especially useful on endpoints and in insider-risk cases. Channel-based DLP instead inspects traffic at control points such as email gateways, cloud connectors, or endpoint agents. It can be broader in coverage, but it often treats each channel separately rather than tracking the data object itself across the full lifecycle.

Practical implication: teams should decide whether they need investigation depth, channel breadth, or both before standardising controls.

Why SaaS data at rest breaks older DLP assumptions

SaaS data at rest sits inside collaboration, ticketing, CRM, and document platforms where the sensitive content may not traverse a classic network choke point. Older DLP assumptions expect data to leave through email, endpoint copy actions, or file transfer events. In reality, access permissions, sharing links, synced copies, and embedded content can create exposure without a single obvious exfiltration event. That is why SaaS governance increasingly overlaps with identity, entitlement, and secret-detection controls.

Practical implication: extend monitoring from transport paths to stored data, access grants, and sharing states.

Browser GenAI and MCP traffic as a new exposure surface

Browser prompts sent to public LLMs and AI-agent traffic over MCP create a new class of data movement that is neither classic endpoint activity nor traditional cloud egress. MCP, or Model Context Protocol, connects agents to tools and data sources, which means sensitive content can be passed into tool calls, context windows, and retrieval steps. If DLP treats these interactions as ordinary web traffic, it misses the governance problem: the system is moving regulated or confidential content into a machine decision loop.

Practical implication: apply specific policy controls to browser GenAI use and AI-agent tool calls, not just to outbound network events.

NHI Mgmt Group analysis

Channel-first DLP is no longer enough for modern data exposure. Email and endpoint remain important, but they are no longer the only places where sensitive data moves. SaaS collaboration, browser AI use, and AI-agent tool calls create exposure paths that bypass legacy inspection assumptions. For IAM and data-security teams, the practical conclusion is that DLP strategy now has to follow workflow location as well as content type.

Data lineage is valuable, but lineage without remediation leaves risk in place. Knowing where content came from and where it went helps investigations, yet many organisations now need action at the moment of exposure. Detection-led tooling can support evidence gathering, but it does not automatically reduce blast radius when content is being entered into AI tools or copied across SaaS. The field is moving toward data-native control, where the sensitive object is governed inline.

AI-agent traffic should be treated as a governance boundary, not just another telemetry source. MCP-enabled workflows change how content reaches tools, plugins, and downstream systems. That creates a governance gap between identity, data handling, and runtime decision-making. The named concept here is AI data relay sprawl: sensitive data flowing through multiple assisted channels without a single control point that can inspect, classify, and remediate it consistently. Practitioners should treat that sprawl as a control-design problem.

Suite consolidation does not solve the policy problem by itself. Broad DLP platforms can reduce tooling fragmentation, but they do not automatically fix inconsistent classifications, stale access grants, or overbroad exceptions. Organisations still need clear policy ownership across identity, data, and collaboration systems. The conclusion for security leaders is that control consolidation must be matched by governance simplification, or the same exposure simply moves into a larger stack.

Remediation speed is now a competitive control dimension. In a world where secrets can be abused within minutes and leaked credentials may persist for weeks, the organisation that can redact, mask, revoke, or block at the point of exposure has a materially different risk profile. Teams should treat speed of remediation as a core design criterion when evaluating DLP and identity-adjacent data controls.

What this signals

AI data relay sprawl: organisations now need a control model that follows sensitive data across SaaS, browser AI, endpoint, and AI-agent paths without assuming email is the primary exit route. That means policy, classification, and remediation have to be defined once and enforced consistently across multiple runtime surfaces.

For identity-led security teams, the practical shift is that access governance and data governance are converging. A user, service account, or AI agent may have valid access to a source system, yet still create unacceptable exposure by moving content into an unmanaged AI workflow. Teams should use the NIST Cybersecurity Framework 2.0 as the broader governance anchor and align data-path controls with the NIST SP 800-53 Rev 5 Security and Privacy Controls where classification and access restrictions intersect.

Inline remediation is becoming the differentiator for programmes that want measurable reduction rather than just better alerting. Where sensitive content can be redacted or blocked before it reaches an AI model or leaves a SaaS boundary, the organisation reduces downstream remediation burden and investigation load.

For practitioners

• 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.
• Review SaaS access and sharing states Audit entitlements, links, synced files, and external sharing settings alongside DLP rules so stored data exposure is governed, not just transmitted data.

Key takeaways

• Modern DLP has to govern data across SaaS, browser AI, endpoint, and AI-agent traffic, not only in email and file transfer paths.
• Detection and lineage help investigations, but inline remediation is what reduces exposure when sensitive data is moving in real time.
• The governance test is no longer how many channels a tool watches, but whether it can enforce one policy model across the channels that matter.

Key terms

• Data Lineage DLP: Data lineage DLP tracks sensitive content from creation or download through copy, rename, upload, and share events. It preserves the file’s identity across systems so security teams can prove where data went, who handled it, and whether enforcement occurred at each step.
• AI Data Relay Sprawl: The spread of sensitive data across multiple AI-assisted paths such as prompts, tool calls, retrieval steps, and browser extensions. It creates a governance problem because the same content can pass through several runtime surfaces without one consistent policy enforcement point.
• Inline remediation: Inline remediation is the practice of presenting security guidance directly in the developer environment where code is written. It reduces context-switching and can speed up fixes, but it only improves governance when the guidance is accurate, explainable, and consistently adopted by engineering teams.
• Channel-First DLP: A protection model that inspects data at predefined transport or application channels such as email, cloud connectors, or endpoint agents. It can be effective when the organisation's risk is concentrated in those channels, but it becomes less complete when data moves through SaaS apps and AI workflows.

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

👉 The full Strac article covers channel coverage, remediation options, and AI-specific control gaps in more detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity, and secrets management for teams building modern access controls. It helps security practitioners connect identity discipline to the broader governance problems that data and AI workflows now create.