DSPM Dirty Little Secrets: What Vendors Don’t Want You to Test
Discover What DSPM Vendors Try to Hide
Your goal in running a data security/DSPM POV is to evaluate all important performance and cost parameters so you can make the best decision and avoid unpleasant surprises. Vendors, on the other hand, are looking for a ‘quick win’ and will often suggest shortcuts like using a limited test data set and copying your data to their environment.
On the surface this might sound like a reasonable approach, but if you don’t test real data types and volumes in your own environment, the POV process may hide costly failures or compliance violations that will quickly become apparent in production. A recent evaluation of Sentra versus another top emerging DSPM exposed how the other solution’s performance dropped and costs skyrocketed when deployed at petabyte scale. Worse, the emerging DSPM removed data from the customer environment - a clear controls violation.
If you want to run a successful POV and avoid DSPM buyers' remorse you need to look out for these "dirty little secrets".
Dirty Little Secret #1:
‘Start small’ can mean ‘fails at scale’
The biggest 'dirty secret' is that scalability limits are hidden behind the 'start small' suggestion. Many DSPM platforms cannot scale to modern petabyte-sized data environments. Vendors try to conceal this architectural weakness by encouraging small, tightly scoped POVs that never stress the system and create false confidence. Upon broad deployment, this weakness is quickly exposed as scans slow and refresh cycles stretch, forcing teams to drastically reduce scope or frequency. This failure is fundamentally architectural, lacking parallel orchestration and elastic execution, proving that the 'start small' advice was a deliberate tactic to avoid exposing the platform’s inevitable bottleneck.In a recent POV, Sentra successfully scanned 10x more data in approximately the same time than the alternative:
Dirty Little Secret #2:
High cloud cost breaks continuous security
Another reason some vendors try to limit the scale of POVs is to hide the real cloud cost of running them in production. They often use brute-force scanning that reads excessive data, consumes massive compute resources, and is architecturally inefficient. This is easy to mask during short, limited POVs, but quickly drives up cloud bills in production. The resulting cost pressure forces organizations to reduce scan frequency and scope, quietly shifting the platform from continuous security control to periodic inventory. Ultimately, tools that cannot scale scanners efficiently on-demand or scan infrequently trade essential security for cost, proving they are only affordable when they are not fully utilized. In a recent POV run on 100 petabytes of data, Sentra proved to be 10x more operationally cost effective to run:
Dirty Little Secret #3:
‘Good enough’ accuracy degrades security
Accuracy is fundamental to Data Security Posture Management (DSPM) and should not be compromised. While a few points difference may not seem like a deal breaker, every percentage point of classification accuracy can dramatically affect all downstream security controls. Costs increase as manual intervention is required to address FPs. When organizations automate controls based on these inaccuracies, the DSPM platform becomes a source of risk. Confidence is lost. The secret is kept safe because the POV never validates the platform's accuracy against known sensitive data.
In a recent POV Sentra was able to prove less than one percent rate of false positives and false negatives:
DSPM POV Red Flags
- Copy data to the vendor environment for a “quick win”
- Limit features or capabilities to simplify testing
- Artificially reduce the size of scanned data
- Restrict integrations to avoid “complications”
- Limit or avoid API usage
These shortcuts don’t make a POV easier - they make it misleading.
Four DSPM POV Requirements That Expose the Truth
If you want a DSPM POV that reflects production reality, insist on these requirements:
1. Scalability
Run discovery and classification on at least 1 petabyte of real data, including unstructured object storage. Completion time must be measured in hours or days - not weeks.
2. Cost Efficiency
Operate scans continuously at scale and measure actual cloud resource consumption. If cost forces reduced frequency or scope, the model is unsustainable.
3. Accuracy
Validate results against known sensitive data. Measure false positives and false negatives explicitly. Accuracy must be quantified and repeatable.
4. Unstructured Data Depth
Test long-form, heterogeneous, real-world unstructured data including audio, video, etc. Classification must demonstrate contextual understanding, not just keyword matches.
A DSPM solution that only performs well in a limited POV will lead to painful, costly buyer’s regret. Once in production, the failures in scalability, cost efficiency, accuracy, and unstructured data depth quickly become apparent.
Getting ready to run a DSPM POV? Schedule a demo.
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David Stuart is Senior Director of Product Marketing for Sentra, a leading cloud-native data security platform provider, where he is responsible for product and launch planning, content creation, and analyst relations. Dave is a 20+ year security industry veteran having held product and marketing management positions at industry luminary companies such as Symantec, Sourcefire, Cisco, Tenable, and ZeroFox. Dave holds a BSEE/CS from University of Illinois, and an MBA from Northwestern Kellogg Graduate School of Management.
