Key takeaways
- Treat DSPM as the foundation: The strongest platforms pair discovery with access analysis, real-time threat detection, and automated remediation.
- Evaluate coverage by depth, not connector counts, and confirm full scanning, effective permissions, and support for the file, SaaS, and IaaS stores that hold your critical data.
- Validate every vendor claim with a POC on production-scale data, a real sample risk assessment, and verified peer reviews before you buy.
Where does your sensitive data live, who can reach it, and would you know if an AI agent started pulling it? For most security teams, the answer has grown increasingly tangled thanks to data stores spanning dozens of SaaS apps, such as Microsoft 365 and Google Workspace, plus multiple clouds, on-prem file shares, and a growing set of agents.
According to the Varonis 2025 State of Data Security Report, which analyzed 1,000 real-world IT environments, 99% of organizations have exposed sensitive data that can easily be surfaced by AI. What's more, an agent with broad access doesn’t just surface overexposed data. It can act on that data with limited human oversight.
That combination of sprawl, exposure, and autonomous access is exactly why Data Security Posture Management (DSPM) has become a frontline requirement. This guide explains the main types of DSPM solutions, the capabilities that separate them, and how to run an evaluation that tests vendor claims against your own data.
What DSPM does today
Data security posture management (DSPM) gives security teams continuous visibility into where sensitive data lives, who can access it, how it’s used, and how exposed it is. DSPM builds on long-standing disciplines, including data discovery, access control, and activity monitoring, and has become a mature category, according to Forrester’s 2026 Data Security Platforms Landscape.
The stakes are rising as AI moves from answering questions to taking action. Gartner predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024.
Types of DSPM solutions and what to look for
Many products now carry the DSPM label, but they start from different places. Knowing which type you’re evaluating helps you judge whether it will reduce risk or merely report on it because not every DSPM will improve your data security posture.
Discovery-only DSPM
Some DSPM products grew out of data privacy and governance tools. They find and catalog sensitive data well, but many measure posture by counting sensitive data findings without mapping exposure, detecting threats, or fixing issues. The result can be a long list of findings with little context on which ones put data at risk.
What to look for: A DSPM vendor that pairs discovery with exposure mapping, access context, and remediation, so findings translate into prioritized action rather than another list to triage."
IaaS-only DSPM
Many DSPM vendors focus on the big three IaaS platforms (AWS, Azure, and GCP) and offer limited coverage of other critical data domains, such as cloud file storage, on-prem file shares, SaaS apps, and email.
What to look for: A DSPM vendor that spans all your data domains so you can have unified visibility and apply consistent policies across the board.
DSPM without security research and response
Just as you’d expect your EDR or XDR vendor to have strong threat intelligence and research capabilities, your DSPM should have dedicated research teams focused on finding vulnerabilities, tracking threat actors, and developing new threat models, plus an incident response function that can help when something goes wrong.
What to look for: A DSPM vendor that combines dedicated threat research with a built-in incident response capability, not just a dashboard that flags issues and leaves you to handle them alone.
Pressed on time? Download the full DSPM Buyer's Guide.
DSPM features to prioritize
1. Coverage across all data domains and data types
Most large organizations store critical data in three big domains: File, SaaS, and IaaS.
Buying a DSPM that only covers a single domain would be like purchasing an EDR that only worked for Macs.
It might be impossible to find a single DSPM that covers every single data store your business uses. Instead, follow the 80/20 rule: Ask yourself where your most mission-critical data lives and prioritize those data stores.
2. Accurate and scalable data classification
Data discovery and classification are a foundational element of DSPM. Many classification projects fail because the scanning engine can’t process large data sets or they produce too many false positives to be trusted.
Look for a DSPM with customers that match your size and scale. During your POC, ensure their classification can produce complete, contextual, and current results.
- Is your data classification complete? Does your DSPM scan all your data, or is it over-reliant on sampling or “predictive” scanning? Sampling can be effective for databases, but doesn’t work for large file stores such as NAS arrays or object stores such as S3 and Azure Blob. Unlike a database, you can’t assume that just because you scanned 2TB of an S3 account and found no sensitive content, the other 500TB of data is not sensitive.
- Does your data classification have context? Once you find sensitive data, what happens next? Is the data exposed? Is it being used? Who is the data owner? Most organizations are surprised at the number of sensitive files and records they find, and the list will be different tomorrow and the next day. If your DSPM does not map permissions or track access activity, it’s hard to act on the finding.
- Is your data classification current? Does your DSPM scan and classify data as it is created and modified? If your DSPM does not keep a real-time audit trail of data activity, its classification engine must check the last modified date on every single object to know whether it must be re-scanned or perform a full re-scan at specific intervals (usually monthly or quarterly).
3. Deep analysis beyond data classification
Many DSPM vendors will check the coverage box for any platform they can connect to, regardless of whether they provide actual DSPM capabilities. Some vendors will even purport to cover a data store, but in reality, they simply provide you with a developer SDK, and you have to build your own connector.
DSPM must go beyond answering whether a file or object is sensitive, taking into account whether data is at risk of a breach, and answering questions such as:
- Is our data being used, and by whom? Are there any abnormal access patterns that could indicate compromise?
- Is our sensitive data labeled correctly so that our downstream DLP controls work?
- Is sensitive data exposed publicly, to all employees, or to people who don’t require access?
- Is our sensitive data stored in unsanctioned repositories? Are we in violation of any data residency requirements?
- What is the likelihood that a compromised user could exfiltrate sensitive data?
- What data is stale and can be archived or deleted?
Additionally, one of the most critical data security questions is: who can access sensitive data? It's not an easy question to answer. A DSPM’s ability to visualize effective permissions is absolutely critical for breach investigations, compliance audits, and other data security use cases. This visualization is impossible without building specialized connectors for each data store and application.
4. Automated remediation
CISOs don’t need another product to tell them they have problems without offering an automated way to fix them. Look for a DSPM solution that goes beyond visibility and automates fixes on the data platforms it's monitoring.
When a vendor says they offer automated remediation, ask:
- Do you commit changes to the target platform to remediate the risk?
- Can you simulate the change before committing?
- What are the specific data risks that you can remediate automatically?
- Can you automate remediation natively, or does it require clicking a button or executing a homegrown script?
Often, vendors will claim automated remediation when they simply open a ServiceNow ticket (often called a “finding” or a “case”) for a human data owner to investigate, fix, and close manually.
5. Real-time behavioral alerts and incident response
Posture work reduces exposure, but it won’t stop an attacker or insider who already has access. That’s why a DSPM also needs to monitor data access, alert you to abnormal behavior, and help stop threats in real time.
Look for a DSPM vendor that incorporates data detection and response (DDR) and can achieve the following:
- Log all actions on data, not just alerts. During an investigation, you want a full, searchable forensic audit trail inside your DSPM, so you don’t have to jump into your SIEM or come up empty when asked to show every action on a specific data set.
- Alert on behavior anomalies. If your DSPM only has static rule-based alerts (e.g., alert when a user modifies more than 100 files in under a minute), you risk missing stealthy attacks and insider threats. Look for data-centric user and entity behavior analytics (UEBA).
- Help you respond to incidents. Does the vendor have a team that can help you investigate an incident? At a minimum, can its alerts be sent to your SIEM, SOC, or SOAR so your own incident response team can act on threats to sensitive data?
6. Pricing model and total cost of ownership
DSPM vendors price in different ways. Some charge by data volume, so cost grows as your data estate grows. Others price per user. Ask each vendor to model cost at today’s data volume and at the volume you expect in three years. Include costs outside the license, such as cloud egress fees when a vendor pulls data out of your environment to scan it.
3 evaluation tips straight from the field
As you get closer to choosing a DSPM, make sure to do the following:
1. Run a proof-of-concept (POC)
Validate vendor claims with a POC before committing. A vendor that refuses to run one should raise red flags. Where possible, run the POC on production systems or a sandbox that mirrors your production environment's scale, and specifically test data classification results for false positives.
2. Ask for a sample risk assessment
Request to see an anonymized risk report from a real customer rather than relying on marketing materials. A sample report reveals whether the vendor delivers the level of granularity and depth you need, and can help you decide whether a full POC is worth the time.
3. Read real customer reviews
Look for validated DSPM reviews from trusted sources like Gartner and Forrester, ask to speak directly with reference customers, and confirm the vendor has published case studies.
How Varonis delivers on DSPM
Varonis' DSPM is purpose-built to secure enterprise data wherever it lives, offering deep, unified coverage across structured, unstructured, and semi-structured data. Beyond identifying risk, Varonis continuously reduces it through automated remediation, deep context, and services like 24x7 incident response.
Varonis also extends that same classification and identity context to two connected products: Varonis Atlas, which secures AI systems and agents, and Varonis Interceptor, which stops phishing and social engineering attacks aimed at the people who hold access to that data.
Varonis was named a Customers’ Choice in the 2026 Gartner Peer Insights “Voice of the Customer for Data Security Posture Management” for the third consecutive year, with a 97% willingness-to-recommend score from verified customers.
Full-stack data protection
Varonis covers structured, unstructured, and semi-structured data across all three DSPM domains: data classification, access intelligence, and risk mitigation. The platform protects sensitive data across hybrid environments and understands the attack paths that lead to it: from Active Directory and Entra ID to VPNs, proxies, and API/OAuth connections.
Context-aware data classification
Varonis provides complete, enterprise-scale data discovery and classification. The platform scans massive, multi-petabyte environments and contextualizes sensitivity with real-time activity and access rights. Classification remains accurate and current thanks to continuous auditing. That's because Varonis scans the full data estate rather than sampling it, then uses its own audit trail to reclassify only the objects created or modified since the last scan, which cuts out costly full rescans. Scanning is agentless.
Teams can scan cloud-to-cloud for the fastest start, or run a lightweight, Kubernetes-based private collector inside their own cloud so the data being classified never leaves their environment and only metadata returns to Varonis. That option avoids egress fees and keeps sensitive data inside boundaries the organization already controls. Here’s how Varonis scans at scale.
Varonis data classification
Visibility for better decision-making
With more than 150 patents, Varonis combines rich metadata from eight key sources to answer the most critical data security questions, including about access, permission, and risk.
This depth of insight also powers the platform's ability to automate at scale, revoking unused or risky permissions with confidence that business continuity won’t be disrupted.
Built-in automated remediation
Varonis continuously remediates risky access, misconfigurations, ghost users, and public or overshared links with out-of-the-box policies that can be tailored to your needs. No manual effort required.
Customize ready-made policies to enable remediation actions automatically in Varonis.
Real-time threat detection with data-centric UEBA
Varonis gives security teams a complete audit trail of all data activity—across cloud and on-prem environments. Hundreds of expert-built threat models detect abnormal behavior like unusual access patterns, geo-hopping, and suspicious permission changes. Plus, with Managed Data Detection and Response (MDDR), Varonis actively helps you investigate and respond to threats before they escalate.
