An organization’s first scans for sensitive data across its digital estate can return millions of matches across a vast number of locations. The team then has to narrow those matches down to the ones that matter, so it can focus on real exposure without the noise of incidental and low-risk data.

In this post, we look at what scale means for a data discovery and intelligence program and how Ground Labs solutions and services help organizations deliver it successfully.

Scale brings new challenges

IDC estimates that the world generates more than seven petabytes of data every second and expects that rate to more than double by 2029. Large organizations are a microcosm of the global data landscape. They generate and process ever increasing volumes of data across file servers, endpoints, databases, productivity suites, cloud storage and SaaS applications, distributed across several legal jurisdictions.

As the estate grows, data discovery and intelligence programs break down in four predictable ways:

  1. Coverage gaps leave parts of the estate unscanned.

  2. Results are too numerous to analyze efficiently.

  3. Performance limits stop large repositories from being scanned fully or often.

  4. Weak stewardship leaves no one clearly accountable for decisions on findings.

Coverage counts

Many programs focus on core systems that already have strong controls and overlook the upstream and downstream systems that may be more exposed. Payment card data that is well protected in the cardholder data environment can also turn up in a CRM export, a support ticket or a finance team’s reconciliation spreadsheet.

The data types an organization scans for may legitimately vary by region, since national identifiers differ from one jurisdiction to the next. The problem arises when critical data can’t be identified across all the relevant parts of the digital estate. For example, a PCI DSS program needs to find payment card data wherever it exists, whichever business unit owns it and its geographical location.

If platforms or systems are out of reach, the organization only has access to a partial view of their potential exposure.

Quality over quantity

A wide-reaching scan almost always returns more results than organizations expect. Findings stay noisy until the solution has been properly baselined.
Some of that noise is by design. Dependable data intelligence solutions treat a missed match as more harmful than a false alarm. A false positive costs analyst time. A false negative can leave card data outside the assessed PCI DSS scope, or leave personal data unprotected and missing from data protection impact assessments (DPIAs).

Many early results are benign matches. These are true matches against a data type definition that, once confirmed, don’t require remediation and can be marked as exclusions. Examples include published test card numbers, sample records in training material and system-generated values that pass the same validation checks as real identifiers. The scan is working correctly, but the data doesn’t present any risk.

Deploying across the business before baselining creates more results than teams can review, and real exposure gets lost among benign matches. Baselining tunes standard scan configurations to the organization’s own data environment and record conventions. It needs repeating as the program expands to new business units, platforms and global regions.

Separating false positives and benign matches from real exposure takes judgment and industry knowledge, and is where most of the effort in a new deployment goes. Ground Labs Professional Services can fast-track this phase by defining custom data types, tuning exclusion rules and reviewing results with data owners.

Keep it current

Data within an organization changes constantly, so a scan only reflects the estate at the time it ran. Frequent scanning keeps results current and finds new data stores as they appear. At enterprise scale, scanning that often can affect the performance of production systems.

Enterprise Recon divides large repositories across multiple scanning agents that run in parallel, which shortens scan times for the largest targets. Scans can also be scheduled around business hours in each region to limit the load on production systems.

Ownership enables action

Data intelligence only reduces risk when someone acts on it. Once results have been baselined and benign matches set aside, what remains needs a decision.
A central security team can identify sensitive data, but it often doesn’t have the authority to decide how that data should be treated. Those decisions belong to the people who own the data. When findings have no clear owner, they stall and the exposure stays where it is.

Effective stewardship means each finding reaches an accountable owner with the authority to decide on the most appropriate treatment. Enterprise Recon supports this in two ways. Role-based access lets data owners review results for the systems they’re responsible for. Remediation options let them act on data where it’s found.

Achieving data intelligence at scale

Expanding before the foundations are in place adds noise faster than insight, but building from a solid baseline extends coverage in a meaningful way that the organization can act on.

As you consider your data intelligence program, make sure it stacks up against these four challenges:

  1. Can it reach every in-scope system for the data types that matter?

  2. Are you able to baseline the solution effective to separate false positives and benign matches from real findings?

  3. Can your solution support high-volume scanning on a frequent basis without undue performance impacts?

  4. Can you redirect findings to the relevant data owner?

Ground Labs is uniquely placed to support enterprise data intelligence, helping you deploy faster with the coverage you need and results you can trust. Book a call and speak to one of our experts.