Ground Labs Enterprise Recon is among the most accurate data discovery solutions on the market, delivering lower false positive rates and reliable detection of sensitive data across end user devices, on-premises systems and cloud environments. It achieves this using a comprehensive set of validation mechanisms that include common check digits like mod 10 (the Luhn algorithm), format verification and proprietary techniques.
Most organizations undertake data discovery when they need to identify and protect data from unauthorized access, compromise or misuse. This may be driven by industry standards, laws and regulations, or in response to a data breach, contractual commitments or merger and acquisition (M&A) activities. This could include personal information (PII); electronic protected health information (ePHI); cardholder information (for PCI DSS); credentials, secrets and keys; as well as other sensitive business information.
In all these scenarios, organizations need to be able to discover these data assets wherever they are stored - reliably and accurately. Advanced data discovery solutions like Ground Labs Enterprise Recon use a range of techniques to ensure that potential data matches are validated to reduce the number of erroneous findings – false positives – in their final results.
What are false positives in data discovery?
A false positive is a test result that is incorrectly classified as positive
False positives in data discovery are reported sensitive data findings that are not accurate matches of the target data type.
For example, most VISA-issued payment cards have a 16-digit PAN (payment account number). When scanning for VISA cards, false positives would include 16-digit numbers that do not correspond to a VISA payment card.
In another example, scans for birth dates may incorrectly report the presence of any date identified. It is likely that many of these findings will be false positives, since dates occur routinely throughout business communications, systems and processes.
What are the top five problems of false positives in data discovery?
False positives create many problems for data discovery, ranging from resource drain to alert fatigue, and increased operating costs to obscured risk.
The top five reasons false positives are a challenge for data discovery are:
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They waste time and resources – The teams responsible for discovery scanning spend significant time reviewing and validating accurate findings and removing false positives before they can progress to meaningful remediation. This increases both the overall cost of discovery and the time it takes to address real findings.
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They make it harder to quantify and prioritize real risk – When results include a lot of inaccurate findings, it becomes almost impossible to identify areas of genuine high-risk and act quickly to address exposures. At worst, false positives risk diverting remediation efforts to the wrong targets, while genuine findings are overlooked.
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They lower trust in the results – Too many inaccurate findings rapidly diminishes confidence in the discovery process, risking stakeholder support for the discovery program – and ultimately the security and compliance status of the business
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They create unnecessary disruption – Many organizations today lack the resource capacity to perform manual cleanup of false positives. As a result, choosing instead to remediate all findings with controls like encryption, quarantine, deletion, or access restriction. The cost and disruption associated with unnecessary remediation efforts can be significant.
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They increase compliance effort and scope – In addition to escalating resource costs and business disruption, organizations can end up bringing data into scope that is not actually necessary. This adds complexity and further cost to audits, privacy programs and security standard assessments.
“False positives undermine the value of data discovery by wasting resources, obscuring real risk, reducing trust, disrupting operations and increasing compliance overhead.”
How does Enterprise Recon manage false positives?
Ground Labs Enterprise Recon is an award-winning data discovery and data management solution, offering fast, accurate discovery for PCI DSS data, PII data and other business sensitive data. It uses a suite of techniques to minimize false positives and effectively support our customers’ data security and compliance goals.
Check digits
A common data validation technique is to use a check digit. These are standardized for many kinds of identifiers including payment card numbers, IBANs and some country identity numbers. A check digit allows simple input errors to be identified, but also to validate discovery scan findings. Solutions like Ground Labs Enterprise Recon utilize check digits including Modulo 10, Modulo 11 and Modulo 97 to validate findings, in conjunction with other techniques for improved performance.
The luhn algorithm
The most well-known check digit algorithm is the luhn algorithm, which is used to validate credit card numbers (among other data types). The luhn check, in association with the strict PAN format mandated by ISO/IEC 7812 for payment cards worldwide, ensures that Enterprise Recon can detect and remove false positives from cardholder data discovery scan results, enabling effective scope monitoring and revalidation for PCI DSS compliance.
Strict format validation
As well as check digits, Ground Labs uses format validation techniques to identify matches for some data types. This includes data types such as UK postcodes, or bank BIN and IIN numbers, which follow a very strictly defined structure
Context-based verification
More advanced discovery solutions add context awareness to validate scan findings. For example, Ground Labs Enterprise Recon uses contextual data analysis to improve the accuracy of its matches, by either bringing in or filtering out data based on its surrounding context. This means that, when scanning for a data pattern, the solution will also investigate data alongside potential matches for additional context that allows it to accept or reject findings more accurately.
Proprietary techniques
Underpinning Enterprise Recon is Ground Labs’ powerful discovery engine – Ground Labs Accurate Search Syntax (GLASS). GLASS Technology™ encompasses the techniques explained above alongside and other proprietary validation mechanisms to deliver fast, accurate scan results for reliable scoping and remediation for security and compliance.
Discover more with Enterprise Recon, powered by GLASS™
Why validation techniques matter for managing false positives
“In data discovery, false positives create noise, consume analyst time, distort risk prioritization, inflate compliance scope and reduce trust in the discovery process itself.”
False positives can quickly lower the value of data discovery and affect confidence in its results. They take up time, add unnecessary effort and cost, and make it harder to focus on genuine findings that need attention.
This is why it is important to choose data discovery tools that provide comprehensive validation techniques to remove false positives before they are reported as findings.
Ground Labs Enterprise Recon uses a wide range of common validation techniques such as check digits, algorithms, format validation alongside proprietary contextual analysis and GLASS Technology to improve accuracy and further reduce false positives. This means that organizations can rely on clear results, with less noise, to support their data security, compliance and remediation initiatives.
Want to reduce false positives and improve confidence in your discovery results?
Book a demo today, to learn how Ground Labs Enterprise Recon helps organizations achieve more accurate, reliable sensitive data discovery.