Top Varonis DSPM Alternatives in 2026: A CISO’s Evaluation Guide

Varonis was designed for an era defined by Windows file servers, SharePoint, and large NAS environments. The core questions were straightforward: what data exists, who has access to it, and where are permissions too broad.

In 2026, enterprise data risk looks very different. Sensitive content moves continuously across SaaS applications, cloud data stores, endpoints, collaboration tools, code repositories, and generative AI platforms. When a material incident occurs, executive leadership does not ask for a heatmap of storage locations. They ask:

• What exactly was exposed
• Where that data originated
• How it moved across systems, users, and devices
• Whether it could have been stopped in real time

This shift exposes architectural tradeoffs across the DSPM market. Sampling versus full scans. API throttling constraints. Snapshot visibility gaps. Endpoint-only content inspection. Acquired DSPM modules that remain loosely integrated. Limited or nonexistent data lineage.

Below is a structured assessment of leading Varonis DSPM competitors in 2026, written for CISOs, security architects, and data security leaders evaluating strategic platforms rather than point solutions.

Top Varonis DSPM Alternatives

Cyberhaven

Category focus: Data lineage–driven data security platform spanning DSPM, DLP, insider risk, and AI security.

Cyberhaven differentiates itself architecturally by centering its platform on data lineage rather than storage scanning. Instead of starting with where data sits, it tracks how content originates and moves across systems.

The platform observes content flows from structured data sources and SaaS applications into files, endpoints, browsers, collaboration tools, tickets, code repositories, and generative AI systems. DSPM, DLP, insider risk analytics, and AI controls operate on top of this shared lineage engine.

The architectural implications are significant:

• DSPM reflects which datasets actually feed risky activity, not just which storage locations contain sensitive fields.
• DLP policies can reference data origin and propagate enforcement wherever that content appears, including renamed, compressed, or partially copied versions.
• Insider and AI risk analytics incorporate provenance and usage context rather than relying solely on pattern matching.

Because the same engine powers posture and enforcement, changes to data sensitivity definitions can immediately influence controls across endpoints and SaaS environments without external orchestration layers.

Evaluation questions to consider with Cyberhaven:

• Can you demonstrate tracing a real incident from a source system through SaaS and AI, and show enforcement at the moment of risk?
• Where does the lineage engine operate, and how do DSPM and DLP consume it?
• Can one policy apply to all content originating from specific datasets across endpoint, browser, and SaaS?
• In an alert, do analysts see data origin and movement history without pivoting across tools?

Cyera

Category focus: Cloud-native DSPM with agentless discovery.

Cyera is frequently positioned as a modern Varonis alternative for cloud environments. It provides fast OAuth-based onboarding across IaaS, PaaS, and SaaS platforms and surfaces sensitive data stores, access exposures, and configuration issues.

Strengths include rapid visibility and intuitive risk mapping for large cloud estates.

However, agentless discovery across petabyte-scale environments introduces practical constraints. Full byte-level inspection at scale can drive API throttling and cost concerns, leading to reliance on sampling and metadata-first analysis. This produces probabilistic coverage rather than deterministic inspection across all objects.

Cyera’s primary value remains posture visibility. Prevention and enforcement often require integration with external DLP or cloud control systems. Activity occurring between scans or outside cloud stores, such as SaaS to endpoint to AI workflows, can fall outside primary coverage.

Evaluation questions to consider with Cyera:

• Are all objects scanned fully, or is sampling used in large environments? What are the compliance implications?
• How are cross-surface flows from cloud to endpoint to AI handled?
• What API cost and performance impacts occur at full-scan scale?
• If sensitive data is created and exfiltrated quickly, will it be visible?

Sentra

Category focus: Cloud-only DSPM for cloud-native data stores.

Sentra provides agentless scanning across cloud storage and data platforms such as S3, RDS, and Snowflake. It performs well in identifying shadow data and misconfigurations within cloud accounts.

For organizations whose primary data risk resides in cloud-native storage, Sentra can represent an upgrade from legacy file-centric models.

Limitations appear in hybrid estates. On-premises NAS systems, endpoints, and user-driven SaaS workflows are outside its architectural center of gravity. Remediation workflows frequently focus on alerts, posture hardening, and ticketing rather than inline blocking at user interaction points.

Evaluation questions:

• How much of enterprise data risk resides outside cloud stores, and how is that addressed?
• What is the strategy for hybrid and on-prem environments?
• What enforcement capabilities exist beyond cloud configuration changes?
• How many consoles are required to investigate cross-surface incidents?

Securiti

Category focus: Privacy and governance platform with DSPM capabilities.

Securiti’s Privacy Center is widely adopted by privacy and data governance teams for regulatory mapping, data subject request workflows, and compliance reporting.

Security teams evaluating it as a Varonis replacement often encounter operational complexity. Deployments at scale may require professional services and infrastructure tuning. User workflows are often optimized for privacy professionals rather than SOC analysts responding to live incidents.

It can be well suited for privacy-driven programs. It is less commonly the primary console for incident response involving rapid data movement.

Evaluation questions:

• Is this primarily owned by privacy or security teams in practice?
• What deployment timelines are typical for large M365 and cloud estates?
• What infrastructure footprint is required at scale?
• Can it reconstruct a clear movement path for a specific document?

BigID

Category focus: Data intelligence for governance, privacy, and security.

BigID offers deep metadata correlation, cataloging, and policy mapping capabilities. It is often positioned as a unified data intelligence layer across governance and security domains.

In large enterprises, deployments can be substantial undertakings requiring sustained onboarding and tuning. Usage frequently concentrates within governance and data office teams, with security teams consuming outputs rather than operating the platform daily.

As a Varonis replacement, it excels at answering what and where questions. It is less optimized for reconstructing how specific content moved across users and applications in time-sensitive scenarios.

Evaluation questions:

• What does a security-led deployment look like without a parallel governance program?
• What is the realistic time to value for petabyte-scale environments?
• What ongoing operational overhead should be expected?
• How quickly can a file’s activity history be reconstructed?

Strac

Category focus: SaaS-focused DSPM and DLP.

Strac provides rapid, agentless integrations across popular SaaS platforms such as Slack, Google Drive, Microsoft 365, Salesforce, and AWS. It combines pattern detection and OCR to identify sensitive content and can execute inline remediations such as redaction or link revocation.

This makes it attractive for mid-market organizations seeking immediate SaaS hygiene improvements.

Architecturally, its model is surface-oriented. Policies and controls are often managed per application. Unified tracking of content across SaaS, endpoints, and AI environments is limited.

Evaluation questions:

• Can sensitive content be tracked consistently across SaaS, endpoint, and AI surfaces?
• How are endpoints and browser-based interactions handled?
• How complex does policy management become across multiple apps?
• Is incident investigation unified or application-specific?

Concentric AI

Category focus: AI-driven semantic classification for unstructured data.

Concentric emphasizes machine learning models that infer document sensitivity without heavy rule authoring. For common formats such as Word and PDF, classification can be effective.

Challenges arise around explainability and auditability. Black-box decisions can be difficult to justify during regulatory or internal audits. Coverage outside traditional document formats may require supplemental rule-based approaches.

The platform centers on classification. Tracking movement and enforcing controls across diverse surfaces remains less developed.

Evaluation questions:

• How are classification decisions explained to auditors?
• How broad is format coverage beyond common document types?
• What is required to adapt models for proprietary templates?
• How is post-classification movement tracked?

Symmetry Systems

Category focus: Data-plane security for cloud data access.

Symmetry focuses on mapping identities and permissions to cloud data objects. It provides strong visibility into who can access specific buckets, tables, or collections and highlights over-privileged access.

This perspective strengthens cloud access governance. It does not extend deeply into how users actually handle and redistribute data once accessed. Endpoint usage, SaaS collaboration, and AI prompts are typically outside scope.

Evaluation questions:

• How are user actions after access monitored and controlled?
• How is this view integrated with endpoint and SaaS telemetry?
• Does this consolidate or add tooling?
• Can screenshots or copied fragments be traced back to source data?

Wiz

Category focus: CNAPP with integrated DSPM features.

Wiz is widely adopted for cloud security posture management and workload protection. Its DSPM capabilities help identify sensitive data in cloud-native stores and correlate infrastructure risk with data exposure.

The platform remains infrastructure-centric. Unstructured data in SaaS applications, endpoints, and AI tools typically requires additional solutions. Data flows are contextual signals within asset risk models rather than first-class entities tracked end to end.

Evaluation questions:

• What percentage of enterprise data risk is addressed by scanning cloud stores alone?
• Can sensitive content discovered in cloud storage be correlated to SaaS copies?
• What functionality is sacrificed by relying solely on integrated DSPM?
• Is this the primary console during a data exfiltration incident?

CrowdStrike

Category focus: Endpoint-centric data protection with cloud DSPM acquisition.

CrowdStrike’s Falcon platform is a leading EDR and XDR solution. Falcon Data Protection provides content-based controls on endpoints. Flow Security, acquired to extend into DSPM, adds cloud data posture capabilities.

Endpoint controls often rely on pattern-based content inspection and rule tuning. Data origin awareness is limited compared to provenance-driven models. Integration between endpoint and cloud DSPM components continues to mature.

CrowdStrike excels at process-level detection and device behavior analytics. Reconstructing a content-level chain of custody across cloud, endpoint, SaaS, and AI environments is less comprehensive.

Evaluation questions:

• How is business context assigned to files beyond pattern matches?
• Is there a single view connecting cloud data sources to endpoint activity?
• What effort is required to maintain content rules?
• How much of a multi-step data journey is visible natively?

Raising the Bar Beyond Asset Maps

Most Varonis competitors improve visibility into cloud data stores compared to legacy file server models. For organizations seeking incremental modernization of storage scanning, several options above may suffice.

For organizations focused on reducing breach and insider risk across modern workflows, three capabilities increasingly define strategic platforms:

• Accurate knowledge of where critical data resides
• Continuous understanding of how it moves across endpoints, SaaS, and AI
• Real-time control when movement crosses policy boundaries

Few platforms were architected from inception to deliver all three as a unified system. During evaluation, the decisive question is straightforward:

After an incident, can the platform explain how the data left, show every step in its journey, and enforce controls to prevent recurrence without stitching together multiple tools?

In 2026, that standard separates posture visualization from true data security architecture.

Explore Cyberhaven’s DSPM in-depth with our whitepaper, Next-Gen DSPM: Built for the AI-Driven Data World.