# What's the best Data Security Posture Management (DSPM) tool for discovering sensitive data across cloud platforms?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm looking into what's actually best at discovering sensitive data across cloud platforms, since that discovery step is the foundation everything else in a DSPM program depends on. Within the <a class="a a--md" elv="true" href="https://www.g2.com/categories/data-security-posture-management-dspm">Data Security Posture Management (DSPM)</a> category, <strong>Wiz</strong>, <strong>Securiti</strong>, and <strong>Varonis Data Security Platform</strong> are the three names that come up most for this specific job.</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/wiz-wiz/reviews"><strong>Wiz</strong></a>: scans across AWS, Azure, and GCP to map where sensitive data lives and how it's exposed, tying discovery directly into broader cloud risk context. Best for teams that already run Wiz for cloud security posture and want data discovery in the same view.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/securiti/reviews"><strong>Securiti</strong></a>: built around data discovery and classification as a first class function, with strong coverage for structured and unstructured data across multi-cloud environments.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/varonis-data-security-platform/reviews"><strong>Varonis Data Security Platform</strong></a>: known for deep visibility into who can access sensitive data once it's found, not just where it sits, which matters for teams that need to act on discovery results.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/spinone/reviews"><strong>SpinOne</strong></a>: focused on SaaS data discovery and protection, a good fit for teams whose sensitive data lives mostly in collaboration and productivity apps rather than raw cloud storage.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cortex-cloud/reviews"><strong>Cortex Cloud</strong></a>: pairs data discovery with the rest of Palo Alto's cloud security suite, useful for teams standardizing on one vendor across posture management.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/rubrik/reviews"><strong>Rubrik</strong></a>: comes at this from the backup and recovery angle, discovering sensitive data across snapshots and backups as part of its broader data security push.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Discovery is usually the easy part to demo and the hard part to trust at scale, especially with shadow data nobody knew existed. Which of these actually surfaced sensitive data you didn't expect to find, and how much tuning did it take before the results felt reliable?</p>

##### Post Metadata
- Posted at: 6 days ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;From what I&#39;ve heard, most teams do find something they didn&#39;t expect, usually shadow data sitting in an old storage bucket or a test environment nobody decommissioned, and that&#39;s exactly the kind of thing manual audits reliably miss because nobody&#39;s looking for something they don&#39;t know exists. The tuning part seems to be the real cost though, initial results tend to include a lot of false positives on data classified as sensitive that isn&#39;t actually meaningful in context, and getting the results down to something a team trusts enough to act on usually takes a few real rounds of refinement rather than working well right out of the box.&lt;/p&gt;

##### Comment Metadata
- Posted at: 5 days ago
- Author title: SEO Content Writer





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