When Classification Goes Wrong: Why Label Accuracy is a Critical Security Control

The Fragility of Label-Based Security

Security and compliance frameworks often rely on one deceptively simple mechanism: labels. Whether it’s a “Confidential” tag in Microsoft Purview or a sensitivity classification in Box, labels are treated as the foundation of unstructured file security. But what happens when those labels are wrong?

The reality is that most labels in the enterprise are based on assumptions—file names or folder paths—and business users’ understanding of what label categories mean. But without accurate labels, everything downstream starts to break.

The Real-World Risks of Mislabeling

Without a common way to classify and apply labels, a file labeled "internal" may contain PII. A “public” folder might house contracts or health data. When label accuracy fails, the impact is wide-reaching:

• DLP rules misfire—over-blocking harmless files or missing sensitive ones

• DSARs return incomplete results, leading to compliance risk

• Records retention applies to the wrong content, creating liability

• Insider threat detection tools like UEBA or SIEM are misled

In short, your control plane becomes compromised. Not because the security stack failed—but because it was fed the wrong signals.

Why Labels Fail: Hidden Root Causes

Mislabeling isn’t a one-off mistake. It’s a systemic outcome of outdated classification models and operational gaps.

Common root causes include:

• User-driven tagging: Over-reliance on individuals to manually apply labels, often without clear rules or understanding of these rules

• Regex-heavy tooling: Rigid patterns that fail on nuanced or multilingual content

• Metadata-only classification: Using file paths, authorship, or extensions instead of content

• Lack of audit: Once a label is applied, it’s rarely revalidated unless something breaks

These gaps create a false sense of control—and the illusion of compliance.

Why Label Accuracy Must be Treated as a Security Control

Organizations treat classification as a supporting task. In reality, it should be treated like an infrastructure layer: always-on, verifiable, and dynamic.

Data X-Ray enables content-aware classification by analyzing files at the content level using:

• Text extraction, including OCR

• NLP and named entity recognition to identify sensitive information

• LLM models to determine file context, such as whether it’s a contract or invoice

This produces high-confidence, file-level classifications that reflect what’s actually inside the file—not what someone assumed based on filename or location. From there, Data X-Ray integrates with systems like Microsoft Purview and Box Shield to suggest or apply corrected labels.

Case Study: Fixing Compliance Blind Spots During a Bank Divestiture

A large financial institution undergoing a divestiture was required to separate sensitive data across 19 million files as part of a regulatory review.

• Challenge: Legacy tools couldn’t classify files at scale or explain how labeling decisions were made. Manual audits were slow, inconsistent, and unauditable.

• Solution: Data X-Ray scanned SharePoint and Office 365 repositories, analyzed file content, and used defined sensitivity categories to classify files based on content and generate metadata for downstream use in labeling systems.

• Result: The bank delivered a fully auditable classification pipeline, met its regulator-mandated deadline, and avoided hiring hundreds of contractors. Classification standards are now maintained as part of day-to-day business operations.

This wasn’t just a one-time audit. It became a sustainable method for maintaining label accuracy at scale.

Rebuilding Trust in Labels

The point isn’t to throw away your labeling systems. Microsoft Purview, Box Shield, and DLP tools all rely on labels. But to make those tools effective—and part of a sustainable data governance strategy—enterprises need to;

• Validate existing labels using content-aware classification

• Correct mislabeled files and route metadata back into enforcement systems

• Establish auditable classification processes to monitor and validate label accuracy over time

Data X-Ray enables this by acting as a metadata intelligence layer—not a policy engine, but a verification and enrichment tool.

Your Labels Are Either Protecting You—or Lying to You

In an environment where security tools depend on accurate labels, classification isn’t a side task. It’s the surface area for modern control. And when it goes wrong, the consequences are costly.

Accuracy matters. Understanding matters. And in today’s AI-powered, multi-cloud environments, your labels are either your first line of defense—or your weakest link.

Treat them accordingly.

Assess your label accuracy: schedule a Data X-Ray demo today.