Top 10 DSPM Vendors Compared: Choosing the Best DSPM Vendor

Sensitive data has never been harder to control. As organizations expand across multi-cloud environments, SaaS applications and on-premises infrastructure simultaneously, visibility gaps multiply and the risk of a breach grows with them. Traditional data protection tools were not built for this level of complexity or scale.

Data Security Posture Management (DSPM) was created to close that gap. It gives security teams continuous visibility into where sensitive data lives, who can access it, how it moves and where exposure is growing — before attackers or auditors find it first. But with a growing field of best DSPM software options competing for your attention, choosing the right vendor requires more than a feature checklist.

This guide compares the top 10 DSPM vendors based on capability depth, hybrid environment support, classification accuracy and integration with broader data security ecosystems. To understand the full scope of what these platforms do and how to evaluate them, see our complete DSPM guide.

Top 10 DSPM Vendors Overview

The table below compares leading data security posture management vendors across the capabilities that matter most for enterprise deployments. Forcepoint is evaluated first because it provides the most complete feature set across all criteria.

✅ Supported    ⚠️ Partially supported    ❌ Unsupported

#1. Forcepoint DSPM: Best overall DSPM vendor for hybrid enterprises

Forcepoint DSPM delivers the most complete capability set among today's data security posture management vendors and is the clearest choice for organizations that cannot afford visibility gaps between their cloud and on-premises environments.

Its defining differentiator is AI Mesh: a proprietary, networked AI classification architecture that combines a Small Language Model (SLM), deep neural network classifiers and lightweight AI components to classify both structured and unstructured data with exceptional accuracy. This coverage applies equally across cloud environments and on-premises infrastructure, including file servers, databases, SharePoint, SaaS platforms and AI workflows, all within a single platform. That breadth matters because several vendors technically support on-premises environments but limit scope to structured or unstructured data — not both simultaneously. Forcepoint handles both. For more on its approach to structured environments, see structured data support.

What makes AI Mesh viable for on-premises and air-gapped environments is its underlying architecture. The models are engineered to be 10 times smaller than even the smallest large language models, enabling the system to classify a file in roughly 200 milliseconds on a standard CPU with no GPU hardware required. This is not a minor operational detail. Most AI-driven classification engines require GPU infrastructure — hardware that is impractical and often prohibited in restricted on-premises environments. Because Forcepoint's AI Mesh runs entirely on standard CPUs, the same classification accuracy available in cloud deployments operates on any on-premises server without special hardware investment.

For air-gapped environments, Forcepoint DSPM can be deployed entirely within the customer's network perimeter, with no content exchanged with Forcepoint systems at any point. For defense contractors managing controlled unclassified information under CMMC, government agencies with classified network requirements and financial institutions with strict data residency mandates, this is not a preference. It is the only acceptable architecture.

AI Mesh also adapts over time through federated learning. During deployment, Forcepoint spins up organization-specific model components that train on customer data patterns — proprietary file formats, industry-specific terminology and unique identifiers unique to that business. The model becomes increasingly precise about what sensitive data actually looks like in that environment. A financial services firm gets a model tuned to its specific account number formats; a defense manufacturer gets one tuned to engineering file types and export-controlled technical documents. This federated approach means classification accuracy improves continuously rather than degrading as the data landscape evolves.

This hybrid architecture also enables something no cloud-only DSPM can deliver: unified policy enforcement across the boundary. When Forcepoint DSPM classifies a file on an on-premises file server, that classification tag travels with the data. If that file later moves into a SaaS application or is accessed from a cloud-connected endpoint, Forcepoint DLP already knows what it is and enforces policy against it. DSPM classification data also enriches Forcepoint DDR alerts directly, so a user bulk-downloading files tagged as "export-controlled IP" on an on-premises system triggers a high-priority alert rather than going undetected until the next scheduled scan.

Beyond classification, Forcepoint DSPM delivers the full enterprise feature set: rapid discovery across cloud and on-premises storage with no additional per-scan charges; granular file-level permissions visibility with single-click access to view access controls on any scanned file; built-in remediation workflows for access revocation, quarantine and archival; ROT data identification and hygiene; and compliance automation across GDPR, HIPAA, CCPA, CMMC and dozens of other frameworks. For more on compliance automation, see automate compliance reporting.

"Forcepoint DSPM gives us full visibility into where sensitive data lives and how to protect it before it is too late." — Enda Kyne, Chief Technology & Operations Officer, FBD Insurance

Key features: AI Mesh classification for structured and unstructured data on-premises and in the cloud; CPU-native architecture with no GPU requirements; air-gapped deployment support; federated learning that adapts to customer data patterns; no additional charges for discovery scans; granular file-level permissions visibility; native DDR and DLP integration; ROT data hygiene; comprehensive compliance automation; DLP-grade labeling and fingerprinting.

Pros: Only DSPM vendor with full coverage across all six evaluation criteria; strongest hybrid and on-premises architecture; AI classification that runs locally without special hardware; unified platform with DLP and DDR; classification tags persist across environment boundaries.

Cons: Breadth of capabilities requires more onboarding investment for teams seeking a narrow point solution.

Best for: Enterprises managing data across hybrid cloud and on-premises environments, regulated industries with data residency or air-gap requirements and organizations seeking unified DSPM, DDR and DLP in a single platform.

#2. Varonis: Best for Microsoft-centric file security

Varonis has deep heritage in file-level analysis and Microsoft 365 integration. Its DSPM capabilities focus on unstructured data access governance, identifying over-permissioned configurations and usage patterns across SharePoint, OneDrive and on-premises Active Directory environments. Compliance reporting and data hygiene within these ecosystems are strong. Note that Varonis has publicly committed to ending support for its legacy self-hosted platform by December 31, 2026, consolidating entirely onto a SaaS-delivered control plane. This is a significant consideration for organizations in cloud-restricted or private cloud environments.

Pros: Strong file-level permissions analysis; mature Microsoft ecosystem support; solid data hygiene.

Cons: Classification customization is limited; SaaS-only roadmap creates risk for hybrid and cloud-restricted organizations.

Best for: Enterprises that prioritize Microsoft 365 and file-share security and can accommodate a SaaS delivery model.

#3. BigID: Best for privacy-driven data governance

BigID pairs data discovery and classification with consent management, data rights workflows and governance automation. It excels at classifying data across mainframes, data lakes and complex structured environments and provides visual data maps that help compliance and legal stakeholders trace sensitive data through pipelines. BigID recently expanded into AI governance to track model training data. Remediation tends toward ticketing workflows rather than automated policy enforcement, which can slow response on high-priority exposures.

Pros: Strong privacy compliance toolset; good structured data coverage; mature DSAR workflows.

Cons: Remediation is workflow-based rather than automated; classification customization is partial.

Best for: Organizations with heavy privacy compliance requirements managing DSAR workflows or complex regulatory obligations.

#4. Securiti: Best for unified data and AI governance

Securiti combines DSPM with privacy intelligence and AI governance, mapping sensitive data across hybrid and multi-cloud environments with strong compliance reporting and DSAR automation. On-premises coverage is partial compared to its cloud capabilities, limiting its value for organizations with significant legacy infrastructure.

Pros: Strong compliance reporting; AI governance capabilities; broad cloud coverage.

Cons: Partial on-premises support; limited classification customization.

Best for: Enterprises seeking a unified data and AI governance platform with embedded privacy operations.

#5. Cyera: Best for cloud-native discovery speed

Cyera is an agentless, cloud-native platform built for rapid time to value across major cloud providers and SaaS applications. Its privacy automation is strong for regulated cloud deployments and it has expanded to monitor AI model training data and data lineage. Cyera does not support on-premises environments, making it a poor fit for hybrid enterprises.

Pros: Fast deployment; strong cloud and SaaS coverage; good privacy automation.

Cons: No on-premises support; limited classification customization; requires additional tools for insider risk and endpoint coverage.

Best for: Cloud-first organizations with no on-premises footprint that need fast, broad visibility across cloud platforms.

#6. Rubrik: Best for combining backup with data posture

Rubrik integrates DSPM with backup and data resilience, offering a combined view of data risk and recovery readiness. ROT analysis and identification of risky duplicate data are solid. It supports hybrid deployments following its acquisition of Laminar, but classification AI is not customizable and DLP-grade labeling is not supported.

Pros: Combines backup and posture management; solid ROT hygiene; hybrid support.

Cons: Classification AI is not customizable; no DLP-grade labeling; DSPM is secondary to its backup focus.

Best for: Organizations that want to consolidate backup and recovery with data posture management.

#7. Palo Alto Networks (Prisma Cloud): Best for CNAPP-integrated posture

Palo Alto Networks embedded DSPM into Prisma Cloud following its 2023 acquisition of Dig Security, giving organizations a data-layer view within its broader cloud-native application protection platform (CNAPP). It is most valuable for organizations already standardized on Prisma Cloud. On-premises environments are not supported and classification AI is not customizable.

Pros: Tight integration with Prisma Cloud; good multi-cloud coverage; DLP labeling support.

Cons: No on-premises support; DSPM is a secondary feature within a broader CNAPP; limited classification flexibility.

Best for: Enterprises already running Prisma Cloud that want to extend posture management to the data layer.

#8. Zscaler: Best for organizations already on the Zscaler platform

Zscaler offers DSPM capabilities focused on cloud environments as part of its broader security platform, providing access visibility and basic data hygiene insights. On-premises support is absent and classification AI is not customizable. This functions as a supplementary capability rather than a purpose-built DSPM solution.

Pros: Convenient for existing Zscaler customers; access visibility for cloud environments.

Cons: No on-premises support; no classification customization; not competitive as a standalone DSPM solution.

Best for: Organizations standardized on Zscaler wanting basic data posture capabilities without a separate vendor.

#9. Netskope: Best for SaaS security teams extending to data posture

Netskope integrates DSPM into its cloud security suite for organizations already relying on it for SaaS and cloud security. On-premises environments are unsupported, and both classification customization and DLP-grade labeling are absent.

Pros: Convenient for existing Netskope customers; solid SaaS visibility.

Cons: No on-premises support; no classification customization; no DLP-grade labeling.

Best for: Organizations invested in the Netskope ecosystem seeking supplementary data visibility.

#10. Microsoft Purview: Best for Microsoft-only data estates

Microsoft Purview provides unified visibility into data across Microsoft 365 and Azure, with expanding multi-cloud connectors for AWS and Google Cloud. Capabilities are partially supported across most evaluation criteria, and organizations with significant on-premises, non-Microsoft or multi-cloud environments will encounter meaningful coverage gaps.

Pros: Native Microsoft integration; no additional vendor for Microsoft-centric shops; broad framework coverage within the Microsoft ecosystem.

Cons: Partially supported across all evaluation criteria; significant gaps for non-Microsoft and on-premises environments.

Best for: Organizations with heavily Microsoft-centric data estates that want native governance within their existing investment.

6 Key Features to Look for in a DSPM Vendor

Not all DSPM solutions deliver equal value. The features below determine whether a platform will reduce real risk or simply generate reports. For a deeper dive on the selection process, see our guide on choosing a DSPM solution.

1. Data discovery across all environments

A DSPM platform must find sensitive data wherever it lives: cloud object storage, relational databases, SaaS platforms, on-premises file shares, data warehouses and AI pipelines. On-premises coverage should extend to both structured data — databases, data warehouses and structured repositories — and unstructured data, including documents, emails and file shares, simultaneously. Several vendors that technically support on-premises limit their scope to one or the other, which creates blind spots in legacy environments where the most sensitive intellectual property and regulated data often reside.

Discovery should also be continuous rather than point-in-time. An organization that scans quarterly has no visibility into the sensitive data created, moved or duplicated in the intervening months. Continuous discovery ensures that new data stores, shadow copies and over-permissioned files surface automatically. Equally important: confirm whether the vendor charges per discovery scan. Some vendors meter every scan, which creates cost friction that discourages the frequency needed for meaningful posture management. Forcepoint does not charge for additional discovery scans.

2. Accurate, customizable AI classification

Discovery without accurate classification produces noise, not insight. When a DSPM platform flags thousands of files as sensitive, security teams cannot prioritize effectively. Enterprise-grade platforms combine AI and machine learning to classify structured and unstructured data with high fidelity — including data that lacks obvious keywords, follows proprietary formats or carries industry-specific context that generic pattern matching misses entirely.

The critical distinction is whether classification models are customizable and whether they improve over time. Generic, one-size-fits-all classifiers produce false positive rates that bury real risk in alert fatigue. AI-native platforms that can spin up organization-specific models — trained on the customer's own data patterns, file types and terminology — deliver dramatically better accuracy. A healthcare organization's sensitive data looks different from a defense contractor's, and classification should reflect that. Platforms that offer this level of customization and federated learning are a different category from those offering fixed, vendor-defined classifiers.

3. Hybrid and on-premises deployment flexibility

Many enterprise organizations operate hybrid environments where critical data lives on-premises alongside cloud and SaaS deployments, and some cannot move sensitive data to public cloud infrastructure at all. A DSPM vendor that covers only cloud environments leaves those organizations without posture management for their most sensitive data. This is not a niche requirement. Most large enterprises, and virtually all government and defense organizations, have on-premises data stores that cannot be migrated for regulatory, contractual or operational reasons.

When evaluating hybrid support, look beyond whether the vendor technically connects to on-premises environments. Ask whether the classification engine runs locally, what happens to data in transit during scanning and whether fully air-gapped deployment is supported. For organizations with classified networks, strict data residency requirements or zero-trust architectures that prohibit external data flows, air-gapped operation — where no content leaves the customer's network perimeter at any point — is the only acceptable model. Few DSPM vendors offer this. Forcepoint does.

Also confirm whether the vendor's AI classification requires GPU infrastructure to operate on-premises. Most AI-based classification engines do require GPUs, which are impractical to deploy in many private data centers and prohibited in some regulated environments. A platform whose classification runs on standard CPUs can operate anywhere, while one that requires GPU hardware is effectively cloud-only in practice, regardless of what its documentation says.

4. Permissions analysis and least privilege enforcement

Knowing where sensitive data lives is only half the picture. Understanding who can access it, and whether that access is appropriate, is equally critical. DSPM solutions should provide granular, file-level visibility into permissions across the organization, identifying users and groups with access they no longer need, files shared externally with no expiration, and configurations that violate least privilege principles.

This capability is especially important in environments where permissions are rarely revoked once granted. Over time, access sprawl accumulates silently: former employees retain file access, broad group permissions persist after project completion, and sensitive documents remain in shared drives with public links that nobody remembers activating. DSPM surfaces all of this. The strongest platforms allow remediation directly from the DSPM interface — revoking access, quarantining files or adjusting permissions — without requiring a handoff to a separate tool or ticketing queue.

5. Built-in remediation workflows

A DSPM platform that identifies risk but cannot act on it creates reporting overhead rather than security improvement. Evaluate whether remediation is native or outsourced to ticketing systems, and how much manual intervention stands between a finding and a fix. Native remediation capabilities — access revocation, file quarantine, archival of ROT data and automated policy enforcement — close the gap between discovery and protection without delays.

Also evaluate the data hygiene capabilities bundled with remediation. Redundant, outdated and trivial (ROT) data inflates storage costs and unnecessarily expands the blast radius of a potential breach. DSPM platforms that identify and help eliminate ROT data reduce exposure before a threat ever materializes. This is particularly valuable during compliance audits, where demonstrating that unnecessary sensitive data has been removed is as important as proving that retained data is properly protected.

6. Integration with DLP, DDR and your security stack

DSPM operating in isolation produces visibility. DSPM integrated with enforcement produces security. The most valuable deployments are those where DSPM classification data feeds directly into DLP policies for data-in-motion enforcement, DDR monitoring for real-time behavioral detection and CASB controls for cloud application governance. When these components share a classification context, the entire platform becomes more accurate: DLP policies apply based on what data actually is rather than static rule sets, and DDR alerts are prioritized based on the sensitivity of the data being accessed rather than generic behavioral thresholds.

Confirm whether integration is native within the same platform or dependent on APIs between separate products. API-based integrations introduce latency, synchronization dependencies and operational complexity that native integrations avoid. Also confirm integrations with your existing SIEM, SOAR and IAM infrastructure, and verify that classification tags persist when data moves across environment boundaries so that a file classified on-premises remains classified when it moves to a cloud application.

Why Organizations Need to Implement Data Security Posture Management

According to IDC, 80% of data globally is unstructured and 90% of that data is never analyzed, leaving organizations largely blind to what sensitive information they hold, where it lives and who can access it. DSPM addresses this directly by providing continuous, automated visibility across the entire data landscape.

To understand where DSPM fits in the broader security stack, it helps to compare it with two adjacent disciplines that buyers frequently encounter: Data Loss Prevention (DLP) and Cloud Security Posture Management (CSPM).

What is a DSPM vendor, and how does it differ from a DLP vendor? DLP is a mature, policy-driven technology focused on data in motion. It monitors data flowing across email, endpoints, web channels and cloud applications, and blocks or encrypts transfers that violate security policies. DLP asks "Is sensitive data leaving the organization through an unauthorized channel?" and enforces against it in real time. DSPM asks a different question: "Where does sensitive data exist right now, who can access it, and is it properly protected?" DSPM is visibility-first and data-at-rest centric. It discovers where sensitive data lives, classifies it by risk and surfaces exposures — including misconfigurations, excessive permissions and redundant copies — before data moves anywhere inappropriately. Used together, DSPM provides the context and inventory; DLP turns that insight into active enforcement across every channel employees use.

How does DSPM differ from CSPM? Cloud Security Posture Management (CSPM) secures cloud infrastructure configurations. It identifies misconfigured storage buckets, overly permissive IAM policies, open network ports and compliance drift at the infrastructure level. CSPM can tell you that an S3 bucket is publicly accessible. What CSPM cannot tell you is whether that bucket contains sensitive customer records, how many users have permission to read it, and what the regulatory exposure would be if it were breached. DSPM fills that gap by looking inside the data stores rather than at their configuration, understanding the content, classifying it by sensitivity and surfacing exposures in a business and compliance context. A modern security strategy needs both: CSPM to secure the infrastructure, DSPM to secure the data within it.

Three specific pressures are making DSPM essential for organizations right now. First, data sprawl is accelerating — 94% of organizations store data across multiple cloud environments, and most retain significant on-premises data stores that are never fully inventoried. Second, AI adoption is introducing new risk pathways as tools like Microsoft Copilot and ChatGPT Enterprise access organizational data on behalf of users, often without security visibility into what data those tools touch or generate. Third, global compliance mandates including GDPR, HIPAA, CCPA and CMMC now require organizations to demonstrate continuously that they know where regulated data lives and how it is protected — a standard that is impossible to meet without automated data discovery and classification. For more on governing AI-related data risk, see DSPM for AI.

Top 4 Real-Life Use Cases of Data Security Posture Management

For a comprehensive look at how organizations are applying DSPM in practice, see our guide to DSPM use cases. The four scenarios below represent the most common and highest-impact deployments.

1. Eliminating overexposed SaaS data

A mid-size financial services firm completes a Microsoft 365 migration and, a year later, runs its first DSPM scan. The results reveal thousands of files containing customer PII and internal financial reports sitting in SharePoint with broad internal sharing permissions — some with links that were never deactivated after project completion. Several files had been shared externally with former vendors who no longer had active contracts. None of this was intentional. It accumulated through normal work patterns over 12 months without anyone noticing.

DSPM identifies the exposure, classifies each file by sensitivity and generates a prioritized remediation list: which files carry regulatory risk, which external shares need immediate revocation and which internal permissions need tightening. The security team closes hundreds of overexposures in days rather than discovering them during an audit or a breach investigation. For a practical example of this kind of discovery, see overexposed SaaS data.

2. Securing hybrid environments with on-premises and cloud data

A global manufacturing company stores its engineering schematics and intellectual property on on-premises file servers — data it cannot move to public cloud infrastructure due to export control regulations. Separately, its HR and finance teams work entirely in cloud-based SaaS applications. The security team has DLP coverage for the SaaS environment but no visibility into what sensitive data exists on the on-premises file servers, who has access to it or whether any of it has been duplicated in unsanctioned locations.

Deploying DSPM with full on-premises support gives the security team a unified view for the first time. The scan reveals that several engineering file shares contain export-controlled technical documents with overly broad access permissions — including permissions granted to contractors whose engagements ended months earlier. It also surfaces ROT data: thousands of duplicate files that serve no current purpose but expand the blast radius if an attacker ever gains access. DSPM's classification tags then feed into the organization's DLP policies, so if any of those flagged files subsequently moves toward an email attachment or a cloud upload, enforcement triggers automatically regardless of which environment the file originated from.

3. Preparing for compliance audits

A healthcare organization subject to HIPAA begins preparing for an audit and discovers that its security team cannot answer basic questions: How much protected health information (PHI) does the organization hold? In how many locations? Who has access? The manual process of answering these questions across on-premises servers, cloud storage and SaaS applications would take weeks of effort and still produce incomplete results.

With DSPM deployed, the same questions are answered in hours. The platform has continuously scanned all environments, classified PHI across every location, mapped access permissions and tracked changes over time. Audit-ready reports that demonstrate continuous compliance are generated on demand, including a full accounting of where PHI resides, who has accessed it and what controls are in place. What previously required weeks of manual evidence gathering is reduced to a scheduled report export.

4. Governing AI data access

An enterprise deploys Microsoft Copilot across its organization. Within weeks, its security team starts receiving questions from legal: What data can Copilot access? Can it surface confidential contract terms or unreleased financial data in response to employee prompts? Is the organization compliant with data handling requirements if Copilot generates outputs containing regulated data?

DSPM provides the classification foundation needed to answer these questions and enforce governance guardrails. By mapping which files and data stores Copilot has permission to access and classifying the sensitivity of each, the security team identifies that several confidential merger-related documents and unreleased earnings data are accessible to Copilot by any user in the finance department. Access is tightened before any sensitive data surfaces in an AI-generated output. Ongoing DSPM monitoring ensures that as new data is created and permissions change, the AI governance posture is continuously updated rather than assessed point-in-time.

What to Consider When Choosing a DSPM Vendor for Your Organization

• Data environment coverage. Does the solution cover all your data domains, including cloud, on-premises, SaaS, databases and email? Many DSPM vendors cover cloud only. If any sensitive data lives outside the cloud, confirm that the platform scans on-premises environments natively and that coverage extends to both structured and unstructured data.
• Deployment model. Is an agentless solution preferred, or does your environment require agent-based coverage for endpoints and legacy systems? For highly regulated environments, confirm whether the vendor supports on-premises or air-gapped deployment where no data leaves your network perimeter.
• Classification feature set. Does the platform offer AI-driven classification that is customizable to your specific data types? Can models adapt to your organization's unique data patterns over time? Does coverage extend to both structured and unstructured data?
• Context and integration. Does the solution connect data risk to identity, access and other security findings? Does it integrate natively with your DLP, DDR, SIEM, SOAR and IAM tools, or does it operate as a standalone reporting dashboard?
• Remediation depth. Can the platform automate remediation natively, or does every fix require a manual handoff to a separate ticketing system? Native workflows for access revocation, quarantine and archival are measurably faster.
• Scanning cost structure. Some vendors charge per discovery scan, which creates cost friction that discourages the scanning frequency needed for meaningful posture management. Confirm the pricing model before committing.
• Compliance framework coverage. Confirm the platform maps to the specific frameworks your organization is subject to and can generate audit-ready evidence on demand.

Why Forcepoint Is the Top Choice for CISOs

Most enterprise organizations are not purely cloud-native. Legacy infrastructure, on-premises file servers, private cloud environments and regulated data that cannot leave internal systems are operational realities for the majority of large enterprises and near-universal requirements in government and defense. A DSPM vendor that stops at the cloud boundary leaves exactly those environments without coverage — and those environments are typically where the most sensitive data lives.

Forcepoint DSPM was built for this reality. It discovers and classifies both structured and unstructured data across on-premises file shares, databases and legacy systems alongside cloud storage and SaaS platforms, delivering a unified risk picture across the entire data landscape. Its AI Mesh architecture classifies data on standard CPUs without GPU infrastructure, making full-fidelity, AI-powered classification feasible in any server environment including air-gapped networks where most vendors cannot operate at all.

Critically, Forcepoint's hybrid architecture is not just a deployment option. It is a connected enforcement layer. Classification tags applied on-premises travel with data as it moves into cloud environments, ensuring that DLP policies and DDR alerts apply based on what data actually is regardless of where it originated. For organizations managing data that spans regulatory boundaries — defense contractors, financial institutions, healthcare systems and government agencies — this persistent classification across environments is the difference between posture management that works everywhere and visibility that stops at the cloud edge.

When that posture management connects natively to Forcepoint DLP for data-in-motion enforcement and Forcepoint DDR for real-time breach detection, the result is the only platform that delivers unified visibility and active protection across every state of data, at rest, in use and in motion, from a single management console. That is the standard every DSPM vendor should be held to.

Ready to see where your sensitive data is hiding? Book a demo and explore Forcepoint DSPM today.