In today’s cloud-native environments, data is everywhere. But not all of it matters equally — and trying to treat it all the same often leads to wasted time, bloated risk surfaces, and alert fatigue.
That’s why effective data classification is essential. It’s not just about finding sensitive data — it’s about defining what sensitive means to your business, identifying it accurately, and taking action where it matters most. Classification is how you move from discovery to prioritization to protection.
This blog continues from our first post, where we tackled the question: Where is my sensitive data and who has access to it? Now we turn to the next critical step: How does Wiz classify that data at scale — and why should you trust it?
Wiz offers multiple classification options to align with your organization’s needs:
Built in Classifiers
Wiz provides a broad set of built-in classifiers to detect common types of sensitive data like PII, financial data, credentials, and healthcare identifiers. These are mapped to industry standards like GDPR, HIPAA, and PCI DSS, and offer fast coverage with minimal configuration. Learn more here.
Custom Classifiers
Need to classify proprietary or business-specific data? Wiz allows you to define your own rules using keywords, patterns, or context-specific logic. These can be mapped to internal compliance frameworks or tailored labeling schemes. Learn more here.
Novel Classifiers (New in Wiz - now in Preview!)
Not all sensitivity fits into a static template. Wiz’s new Novel Classifiers uses AI and ML models to learn what "sensitive" means within your environment — surfacing patterns like internal IDs, proprietary structures, or client-specific terms that wouldn't be caught by a prebuilt rule.
New in Wiz: Novel Classifiers use AI to learn and adapt to what sensitive means for your business — uncovering unique patterns beyond static rules.
Novel Classifiers are designed to evolve with your business, helping uncover the unique data signals that matter most to you.
How AI Powers Novel Classifiers
Wiz’s Novel Classifiers use unsupervised learning to understand what “sensitive” means in your environment—without relying on pre-defined templates or training custom models. By analyzing entire file trees and associated metadata, Wiz groups similar data into context-rich clusters. Then, using representative examples from each group, Wiz prompts industry-leading large language models to interpret and label what that data represents.
The power of this approach comes from the quality and depth of context only Wiz can provide—from structural relationships and access metadata to cloud-native usage signals. Rather than training our own models, we focus on continuously evaluating and applying the best available models to deliver fast, flexible, and high-quality classification results.
Classification in Wiz is intelligent and adaptable. Instead of applying a one-size-fits-all approach, Wiz uses different methods based on the structure, scale, and context of your data:
Metadata-only classification for logs and predictable data types
Sparse sampling for structured, repetitive datasets like backups
Full content scanning for unstructured and mixed-content files
Wiz uses a tiered classification approach based on the structure, scale, and risk of your data — balancing speed, precision, and trust.
But classification is more than just detection. Before Wiz classifies any data, it performs foundational steps to ensure results are context-rich and accurate. This starts with discovering where data lives, analyzing object structure and relationships, and clustering similar items together — creating the groundwork for meaningful classification at scale.Here’s what happens under the hood:
Discovery: Inventory objects and assets across cloud and DBaaS
Analysis: Choose the right method for each object, classify accordingly
Enrichment: Add sensitivity levels, compliance tags, and ownership context
Precision Controls: Apply confidence scores, validate false positives, refine over time
These are just the high-level stages of Wiz’s powerful classification process. Want to see how each step works—and what makes the engine so effective? Download the white paper.
Wiz makes classification results actionable across the product:
Data Findings View: See what data was found, where, and why
Labels & Filters: Filter by classification, severity, compliance framework. Apply built in or custom labels.
Ignore Rules: Suppress false positives or define custom logic
Human Validation: Validate findings manually at the finding or issue level
Continuous Learning: Wiz adapts based on feedback to improve over time
Classification is noisy if it’s not grounded in context and precision. Wiz is built to prioritize both. Our classification engine is:
Scalable across multi-cloud and hybrid environments
Context-aware, using metadata, enrichment, and relationship mapping
Performance-optimized through tiered methods like sampling and format detection
Adaptable, thanks to innovations like Novel Classifiers
And it’s not standing still. From AI-powered capabilities like Novel Classifiers to real-time enrichment and policy-driven action, Wiz’s classification engine is designed to evolve—continuously improving in performance, accuracy, and adaptability.
As data sprawl continues to grow, classification is how you reduce noise and focus on what matters. Wiz combines broad discovery, intelligent detection, and business-aware context into a classification engine that’s accurate, adaptable, and usable.
Whether you're just getting started or scaling a data security program, Wiz helps you know what’s sensitive, why it matters, and what to do next.
For a deeper technical dive, read the full white paper.
This post is part of our data security blog series. If you missed it, start with Part 1: Where is my sensitive data and who has access to it?, and stay tuned for the final post, where we’ll cover how Wiz helps security teams respond to data risks with context-driven workflows, reporting, and automation.
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