The End of Classification as We Know It: Data Awareness Over Data Labels

Key Takeaways:

• Legacy classification is collapsing under the weight of GenAI, unstructured data, and insider risk.
  
• A new era of data awareness is emerging, where understanding the context and intent behind data matters more than static labels.
  
• Cyera Research Labs pioneers this shift, offering precision, business context, and human relevance that legacy DSPMs can’t match - and we’re doing it at scale.
  

Classification Was Never Designed for This

Data classification was once a cornerstone of information security - a clean, logical framework. Data was sorted into neat buckets: public, internal, confidential, restricted, and the amounts of data were manageable so you could trust your employees to manually and accurately tag documents according to this framework. These labels were tied to content types and governed by policies about who could access them, when, and under what conditions.

This model worked - until the world changed.

Today, data is sprawling, lives in unstructured formats, and deeply contextual. Think about the volume of customer documents, spreadsheets, PDFs, product roadmaps, deal memos, or even Slack messages that are all filled with sensitive data, and largely unmanaged. The perimeter no longer exists. GenAI is everywhere. And LLMs are ingesting everything, while generating massive amounts of data.

According to Gartner’s 2025 Data Exposure Predictions, by 2026, 75% of organizations with GenAI projects will shift their focus to unstructured data security. That’s because AI models are trained on exactly the kind of unstructured data that traditional data security tools ignore .

This shift is exposing the cracks in Classification 1.0.

What Legacy Classification Misses

Imagine a global music company with more than 1,100 business units across regions and labels. Ask their security team what data matters most, and you won’t hear “credit card numbers” or “email addresses.” You’ll hear one answer:

Artist contracts.

These contracts define revenue splits, rights ownership, exclusivity clauses, and release timelines. They exist in wildly different formats - PDFs, scanned documents, Word files, localized templates - written in different languages, structured differently by region, label, and legal team.

Legacy classification tools immediately struggle to identify this data due to: 

• Legacy classifiers don’t understand what makes a document sensitive beyond surface patterns.
  
  
  • No consistent regex to match patterns when classifying unstructured data.
    
  • Keywords change by geography and legal framework.
    
  • File names are unreliable.

As a result, an artist contract might be tagged as a generic “document” or, at best, “confidential” - even if it’s sitting in a broadly accessible cloud bucket, shared across teams that have no business seeing it, or quietly pulled into a GenAI workflow.

At that point, the risk is no longer theoretical.

An exposed artist contract isn’t just a document leak - it’s:

• A commercial and legal liability
  
• A competitive intelligence risk
  
• A high-impact insider threat vector
  
• And increasingly, a GenAI training input that was never meant to exist outside legal boundaries
  

This is where traditional DSPM`s - including those layered onto cloud DLP or CASB platforms - break down. They can label data, but they can’t understand it.

Cyera’s approach goes deeper, combining:

• Holistic data awareness (not field-by-field pattern matching)
  Sensitivity isn’t always defined by PII or PCI. A product strategy document, an artist contract, or a release roadmap may contain no regulated data at all - yet still represent a company’s most critical assets.
  By analyzing the file as a whole, including structure, semantics, and intent, Cyera identifies “crown-jewel” data that legacy tools miss entirely because they’re looking for the wrong signals.
  
• Human association
  We don’t only ask what the data contains, but who it relates to. Is this contract tied to a specific artist? Does this document define obligations, ownership, or exclusivity? Understanding human relevance turns abstract sensitivity into concrete risk.
  
• Business association
  Context matters more than labels. Our models associate data with the correct business unit, label, region, product line, or legal domain - even across hundreds of operating entities - surfacing exposure that keyword- or regex-based systems can’t see.
  

Taken together, this shifts data security away from static pattern detection and toward contextual understanding. This is not just classification.

This is data intelligence - designed to protect what actually matters, not just what’s easy to recognize by providing structure, depth and meaning of the actual data.

Practitioner Tip:

Surface business context automatically. Don’t rely on manual tagging. Use classification engines that map documents to business units or initiatives using content and metadata.

How We Got Here: The Evolution from Classification to Intelligence

Let’s break down how we pioneered a classification conceptual tectonic shift from static labels to intelligent data understanding.

In Classification 1.0, the goal was to answer basic questions:

1. What is this data?
   
2. Is it sensitive?
   

This worked well for rows in a structured database, but failed to scale to today’s cloud-native world.

So at Cyera Research Labs, we evolved those same three questions - and gave them new dimensions.

➤ What is this data?

We now analyze at both the file and data-record level, using LLMs to classify sensitive fields within documents, 

A PDF might contain names, account numbers, VINs, addresses - all structured differently. We extract and classify these inline.

Cyera was the first to introduce document-level classification with real-time context recognition - enabling protection of unstructured data at the same level as databases .

➤ Whose data is it?

Understanding the identity association behind the data - such as role, residency, or age - is critical for regulation-driven use cases like GDPR or HIPAA.

We identify toxic combinations, such as seemingly anonymous data that, when combined with other records, becomes identifiable.

This is an area where precision matters - and Cyera’s ability to connect identifiers to real-world identities at scale remains a core differentiator.

➤ What’s the business context?

A file’s sensitivity and policies around it, often depends on who it belongs to and the related business association inside the organization - the business unit, customer account, project name, or product initiative.

We were the first DSPM to map data to internal organizational constructs - addressing “inside the perimeter” risks and enabling contextual decisions, like whether a developer has access to production customer data .

Practitioner Tip:

Watch for toxic combinations. Classification shouldn’t just flag individual fields - it should detect how data becomes sensitive in context.

Why Labels Aren’t Enough Anymore

In an age where data flows into LLMs, risk is no longer only about who can access a file - it’s about how that data can be reused, misused, or leaked downstream. It’s about all the policies you need to enforce within your organization to keep certain data away from certain areas.

LLMs don’t care about your sensitivity tags. They don’t stop and ask if a file marked “confidential” should be part of their training corpus.

This is why Cyera’s real value lies in combining classification with intent - answering what the data means in a real-world context.

Building the New Foundation for AI Security

To secure data in a world where machines learn from it, we must rethink what classification is meant to achieve.

For years, classification was treated as a sorting mechanism - a way to assign labels and move on. That approach worked when data was static, structured, and primarily consumed by humans. But AI has changed the rules. Data is now fluid, unstructured, and increasingly consumed by systems that don’t understand labels, policies, or compliance frameworks. In this new reality, traditional classification collapses under its own assumptions.

What’s needed instead is a foundation that allows organizations to see all sensitive data, understand what it means in context, and act on that understanding at scale. At Cyera Research Labs, we’ve spent years building exactly that - a new classification foundation designed not as a control, but as an intelligence layer for AI security.

That foundation rests on four pillars.

Pillar I: Automatically Surfacing Sensitive Data Without Blind Spots

Sensitive data rarely looks sensitive at first glance.

In modern environments, it lives inside unstructured documents, internal presentations, contracts, logs, and machine-generated artifacts - spread across clouds, SaaS platforms, and endpoints. Much of it has no predefined label, no consistent schema, and no obvious regulatory marker. Worse still, entirely new data types emerge continuously as AI systems generate, remix, and reuse information.

Legacy classification tools were never built for this. They depend on predefined templates, known patterns, and manual tuning - which means they only ever find what teams already expect to find.

Cyera takes a fundamentally different approach. Instead of asking security teams to predict every sensitive data type in advance, the system infers sensitivity directly from the environment. By analyzing content, structure, and usage patterns, it continuously surfaces regulatory data, proprietary information, and previously unknown sensitive elements - even when no formal definition exists.

This ability to uncover the unknown is critical for AI security. When models are trained, queried, or augmented with enterprise data, blind spots become liabilities. Automatically surfacing sensitive data everywhere it exists is the first step toward meaningful control.

Pillar II: Understanding Context - Not Just Content

Knowing that data is sensitive is only half the story.

A document may contain no PII, no financial fields, and no regulated elements - yet still represent one of the organization’s most valuable assets. Product strategies, exclusivity agreements, internal pricing logic, or acquisition plans often derive their sensitivity from what they mean, not from what they technically contain.

This is where traditional classification breaks down. Pattern matching can detect fields, but it cannot interpret intent, ownership, or business relevance.

Cyera addresses this by placing data in context. The system evaluates who the data relates to, which part of the business it belongs to, how it is used, and what role it plays within broader workflows. Human associations and business relationships are treated as first-class signals, not secondary metadata.

By understanding context, classification moves beyond mechanical detection and into interpretation. Security teams can distinguish between true risk and background noise, and policies can reflect real-world usage rather than theoretical sensitivity. In an AI-driven enterprise, this contextual awareness is what separates meaningful protection from false confidence.

Pillar III: Delivering Multi-Dimensional Data Intelligence

Traditional classification flattens complexity.

Once data is assigned a label, much of its nuance disappears. Relationships between documents, usage patterns across teams, and accumulative exposure over time are lost in favor of a single sensitivity tag. For AI security, that simplification is dangerous.

Cyera was designed to preserve - and leverage - complexity.

Instead of evaluating data along a single axis, Cyera interprets it across multiple dimensions simultaneously: sensitivity, business purpose, relationships, and scale. This multi-dimensional intelligence reveals how data is created, shared, reused, and exposed across the organization.

Over time, this creates a richer understanding of the data landscape. Security teams can see not just which files are sensitive, but how sensitive information clusters, where access concentrates, and which combinations of exposure and context create systemic risk. This intelligence enables more precise policies, clearer ownership, and controls that align with how the business actually functions.

In the AI era, where risk often emerges from interaction effects rather than isolated files, this depth of understanding is essential.

Pillar IV: Operating with Precision and Recall at Scale

None of this matters if it doesn’t work at scale.

AI systems operate across entire environments, ingesting data continuously and at speed. Classification must match that reality. High precision without coverage leaves blind spots. Broad coverage without accuracy overwhelms teams with noise. Both outcomes erode trust and stall action.

Cyera is built to balance precision and recall across massive, distributed environments. By applying the appropriate classification techniques for different data types and contexts, it delivers reliable results efficiently - without forcing organizations to choose between accuracy and scale.

This consistency is what enables downstream execution. When classification is dependable, remediation accelerates. Access monitoring becomes meaningful. Compliance validation reflects reality. Risk scoring becomes actionable.

In short, classification stops being an abstract exercise and becomes an operational foundation for AI security.

From Classification to Data Intelligence

Taken together, these four pillars redefine what classification delivers.

No longer a static labeling function, it becomes a continuously operating intelligence layer - one that surfaces sensitive data, understands its context, interprets it across dimensions, and does so at the scale AI demands.

This is the foundation Cyera has built.

Not to classify data for its own sake - but to secure what truly matters in an AI-driven world.

The future of data security depends on smarter understanding

The future of data security doesn’t rest on tighter rules-it depends on smarter understanding. And that starts with asking not just, what is this data?-but what does it mean, and how could it be misused?

At Cyera Research Labs, that’s the lens we’re building through: more intelligence, less guesswork.

Because in a world where AI learns from your data, what you know about your data will define your security posture.