# What are the most reliable sensitive data discovery systems for minimizing false positives and negatives, according to verified users?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm comparing which sensitive data discovery systems are most reliable at minimizing false positives and negatives, going off what verified reviewers report rather than vendor claims. Four that came up in the<a class="a a--md" elv="true" href="https://www.g2.com/categories/sensitive-data-discovery"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/sensitive-data-discovery">sensitive data discovery</a> category:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/nightfall-ai/reviews"><strong>Nightfall AI</strong></a>: reviewers say ML detection significantly cuts false positives so teams focus on true positives. Did the accuracy hold once you tuned it to your data?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/ground-labs-enterprise-recon/reviews"><strong>Ground Labs Enterprise Recon</strong></a>: filters reduce noise, though reviewers still confirm a hit is real cardholder data, not a random 16-digit number. Did the filters get you to a trustworthy signal?</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>: automated classification and risk prioritization cut manual review. Did classification stay accurate across cloud and on-prem?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/securiti/reviews"><strong>Securiti</strong></a>: AI-driven classification is praised, but one reviewer needed scripts for industry-specific identifiers. Did custom rules fix your edge cases?</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams that measured this, what actually moved the needle on accuracy, better tuning, better ML, or a narrower scope?</p>

##### Post Metadata
- Posted at: 9 days ago
- Author title: Marketer
- Net upvotes: 1


## Comments
### Comment 1

Going through the Sensitive Data Discovery reviews on G2, I see that the pattern across these tools is that raw ML detection gets you most of the way, but the last mile of accuracy comes from tuning to your own data shape, not from the model itself. A hit that&#39;s technically a sixteen-digit number but not actually cardholder data is the exact failure mode false-positive tuning exists to catch, and reviewers consistently describe needing custom rules or industry-specific identifiers before an edge case actually resolves cleanly. 
Cross-environment consistency is the other quiet variable, since a classifier tuned well on cloud data doesn&#39;t automatically carry that accuracy into on-prem systems with different formatting conventions. The honest answer to what moves the needle is probably a narrower scope before better tuning, since a tool asked to classify everything everywhere will always have a harder accuracy problem than one scoped to a known data type in a known environment.


##### Comment Metadata
- Posted at: 5 days ago
- Author title: Marketing





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