Not All DSPM Solutions Are Created Equal:

As interest in Data Security Posture Management (DSPM) skyrockets, so does confusion. Many vendors jump into the DSPM market with siloed tools that deliver flashy dashboards but fall short on fundamental capabilities like unified data discovery, deep coverage across environments, and actionable protection. 

The truth? Not all DSPM is created equal. 

To help you cut through the noise and evaluate DSPM solutions with confidence, we’ve created a checklist of must-haves and must-ask questions, rooted in the core capabilities outlined by the Cloud Security Alliance and other leading analysts. 

□ Must discover sensitive data wherever it lives, including email attachments, endpoints, on-prem, and cloud…with a high degree of accuracy.  

• Rationale: Cloud-only DSPM tools often ignore where most sensitive data lives — on-prem and endpoints. 

• Pro Tip: Ask if the tool can identify PII in email attachments, network shares, SharePoint folders, and archived files. Also ask about the accuracy of their discovery searches.  

• Explore More: Read the Tolly Report: Data Discovery Accuracy Evaluation 

□ Must classify data based on content and context, not just metadata. 

• Rationale: DSPM must go beyond metadata. True posture management starts with knowing what the data is, not just where it is.  

• Pro Tip: Ask how their technology classifies data. If it relies purely on metadata there will be gaps which can lead to mislabeling of sensitive information.  

• Explore More: Why Data Discovery and Classification Matter More Than Ever 

□ Must cover structured AND unstructured data equally well. 

• Rationale: Many DSPM vendors skip structured data altogether or deliver shallow coverage, which could result in blind spots across your most sensitive systems, increased regulatory risk, and an incomplete view of your overall data security posture.  

• Pro Tip: Ask how their solution discovers, classifies, and protects sensitive structured data across databases and SaaS applications, and if that coverage is equivalent to what they provide for unstructured data.  

• Explore More: Industry-leading Data Discovery  

□ Must identify excessive or risky permissions, especially across unstructured data and visibility into who accessed what data and when. 

• Rationale: Oversharing can be an org’s #1 DSPM blind spot. Without visibility into permissions and user access history, sensitive data is easily exposed to unnecessary risk.  

• Pro Tip: Ask how the solution detects over-permissive access and orphaned data, especially in cloud shares and collaboration tools, and if it can track access over time to detect abnormal usage patterns.  

• Explore More: Monitor Unusual Behavior 
   

□ Must enforce controls to reduce risk, not just report on posture. 

• Rationale: Insight is important but action is essential. Knowing where sensitive data resides and who can access it is only half the equation.  

• Pro Tip: Ask if the DSPM solution can automatically enforce policies like quarantining exposed files, redacting PII, or revoking access. Otherwise, you’ll be left with insight without true protection.   

• Explore More: Playbooks   

□ Must support hybrid environments, not just modern SaaS. 

• Rationale: Many DSPM vendors focus solely on cloud-native SaaS environments, but sensitive data still resides across on-prem systems, file servers, and endpoints.  

• Pro Tip: Ask if the platform can discover and protect sensitive data across on-premises, cloud, and hybrid environments with consistent policy enforcement. If the answer isn’t a confident “YES,” your data security posture may be incomplete from day one.  

• Explore More: Context Aware Data Interrogation Algorithms (CADIA)    

□ Must support GenAI and data sharing initiatives without risking exposure. 

• Rationale: GenAI platforms like Microsoft Copilot thrive on access, but that can quickly lead to exposure if sensitive data is overshared or misclassified.  

• Pro Tip: Ask how the platform controls and governs sensitive data used by GenAI tools to prevent unintended risk.  

• Explore More: Guide: LLM-ready Data Labeling for Effective AI Governance 

□ Must scale with your business across departments, data types, and geographies. 

• Rationale: Static DSPM tools can’t adapt to dynamic, cross-functional environments which means they often break down when your business expands into new geographies, adopts new SaaS tools, or brings more departments into scope. This leads to siloed risk visibility, inconsistent policies, and mounting costs as teams bolt on point solutions to fill the gaps.  

• Pro Tip: Ask how their solution scales to support new business units, data types, and global regulatory requirements without sacrificing performance or policy consistency.  

• Explore More: Scalability  

If your DSPM vendor can’t answer “YES!” to the questions above and prove it, you’re settling for less than world-class data security. 

DSPM must span the data lifecycle: from discovery and entitlement to protection and response. Organizations relying on point tools or cloud-only DSPM solutions face growing visibility gaps, especially as they attempt to operationalize GenAI, comply with new regulations, and manage hybrid infrastructures. 

archTIS’ Spirion Sensitive Data Platform is the gold standard for unified discovery, classification, and protection, no matter where it lives. Ready to see it in action? Build your own custom demo and take control of your data security posture.