At its core, data classification software help organizations locate and label sensitive data (e.g. customer information) across endpoints, cloud services, databases. These systems are offered either as standalone solutions or modules within larger platforms, like DSPM, DLP or cloud data security software.
However, without providing deep context into data exposure, usage, or policy violations, these tools function more like static data catalogs. Thus, I prepared a feature-based comparison of top solutions (sorted based on data classification capabilities).
Data classification capabilities
Vendor | Classification capability | Classification level |
|---|---|---|
ML & AI classification across structured & unstructured cloud data | Automated (ML) | |
Varonis | ML & AI classification across structured & unstructured cloud data | Automated (ML) |
Spirion | Pattern, keyword, and OCR-based classification with persistent tagging | Rule-based + Automated |
Microsoft Purview | Rule-based sensitivity labeling integrated with M365 and Azure services | Rule-based |
Satori | Schema-based classification in real-time, drives masking workflows | Automated (schema-driven) |
Collibra | Metadata-level tags and policy-based classification workflows | Metadata-only |
Classification via DLP-integrated rule sets and custom dictionaries | Rule-based | |
Symantec DLP | Keyword and policy-based data tagging via DLP rules | Rule-based |
Netwrix Auditor | Classification applied through audit event tagging and policies | Rule-based |
Safetica Pro | Rule-based classification with OCR support | Rule-based |
- Automated (ML): Automatically identifies and tags sensitive data using ML
- Automated (schema-driven): Uses AI models for pattern/context-driven tagging
- Rule-based + automated: Combines pattern matching with OCR or other automated tagging methods
- Rule-based: Pattern-based tagging
- Metadata-only: Tags data based on non-content attributes (e.g., file name, type)
Features of data classification tools
Vendor | Smart classification | Shadow data detection | Cloud‑native |
|---|---|---|---|
✅ | ✅ | ✅ | |
Varonis | ✅ | ✅ | ✅ |
Spirion | ✅ | ⚠️ Predefined paths only | ❌ |
Microsoft Purview | ✅ | ✅ | ✅ |
Satori | ✅ | ✅ | ✅ |
Collibra | ✅ | ⚠️ Relies on cataloging | ⚠️ Hybrid / On-prem |
✅ (via DLP tagging) | ✅ | ✅ | |
Symantec DLP | ⚠️ Keyword-based | ✅ | ✅ |
Netwrix Auditor | ✅ | ❌ | ✅ |
Safetica Pro | ❌ | ❌ | ✅ |
Of note all platform support audit‑ready logs and API access.
- Smart classification: Automatically tags sensitive data using predefined rules, patterns, or machine learning models.
- Shadow data detection: Identifies sensitive data that is stored or duplicated in unknown, unmanaged, or unmonitored locations.
Sentra
Among the tools evaluated, Sentra stands out for its ability to go beyond simple data discovery. It’s a DSPM platform with built-in data detection and response (DDR) features.
The DDR adds context and automation to its classification engine. Instead of passively labeling sensitive data, Sentra continuously monitors for changes in data exposure, movement, or risk posture.
The platform is highly effective in identifying sensitive information across complex environments. It detects both structured and unstructured data based on sensitivity levels (e.g., high, low) or category (e.g., financial, credentials, healthcare).
It’s built to manage petabyte-scale data operations and provides extensive coverage across major cloud environments.
Pros
- With 200+ classifiers and 20 pre-built or customizable integrations.
- Extensive support across IaaS and DBaaS environments:
- Azure/Microsoft 365: Comprehensive support for Azure, OneDrive, SharePoint, Office Online, and Teams.
- AWS: Includes S3, DynamoDB, SQL Server, PostgreSQL, Redis, and more.
- GCP: Covers Google Cloud Storage, BigQuery, Cloud Spanner, and Google Workspace.
Cons
- Limited SaaS-native and API integration options, with fewer out-of-the-box connectors compared to mature enterprise platforms.
- Volume-based pricing – Cost scales with data scanned (TBs)
- The AI chatbot assistant provides inaccurate results.
Endpoint Protector by CoSoSys
Endpoint Protector is a cross-platform DLP solution; compatible with Windows, macOS, Linux, thin clients, and DaaS. It focuses on controlling data at endpoints and preventing data leaks.
Endpoint Protector specializes in modules such as USB device control, content-aware protection, e-discovery, and encryption. It is compatible with Windows, macOS, and Linux.
Note that the company is based in Romania with a small team, which might influence support availability and responsiveness due to time zone differences.
Pros
- The e-discovery module is practical, easy to implement, and user-friendly
- Data classification effectively identifies sensitive data and takes actions such as blocking, notifying, or allowing based on predefined rules.
- Administrators can define sensitive data using custom or preset rules through the eDiscovery menu.
See our DLP review for more on Endpoint Protector’s data classification capabilities.
Cons
- No data masking.
- No database fingerprint audit.
- Sometimes, the modules crash, but customer support helps quickly.
FileCloud Data Classification Software
FileCloud provides enterprise file sharing, sync, and collaboration solutions. The platform allows businesses to securely store, access, and share files both within the organization and with external partners or clients.
FileCloud prioritizes data privacy and security, offering features such as end-to-end encryption, granular access controls, and compliance with regulations like GDPR and HIPAA.
Pros
- Robust security features that comply with cybersecurity standards, including ITAR compliance for sensitive data.
- Comprehensive wiki and documentation.
- Supports remote work environments with seamless file sync between cloud and local storage.
Cons
- Lack of flexibility in licensing, as multiple licenses cannot coexist in the same tenant/domain.
- Add-ins and extensions are functional but not fully optimized.
Safetica Pro
Safetica is a data loss prevention (DLP) and insider risk management (IRM) solution that prevents data breaches and defends businesses against insider threats. It is ideal for both small and large enterprises.
Safetica unified categorization uses content analysis and context awareness to detect sensitive information.
It enables you to identify sensitive files based on sensitive content, origin, file type, and even pre-existing third-party data classification.
Safetica’s unified classification classifies:
- Data in use: Refers to actively working with files, such as opening and editing them in various applications.
- Data in motion involves the transfer of files, whether through uploading, sending emails, or sharing across different platforms.
- Data at rest: Safetica scans devices to identify sensitive data that remains stored but has not been accessed or used for an extended period.
Pros
- Comes with ready-to-use data classification categories (e.g., personal or financial data), enabling instant detection and monitoring of sensitive file operations.
- Allows detailed rule creation, combining specific elements and setting thresholds for occurrences, ensuring precise data management.
- Supports optical character recognition (OCR) to classify and detect sensitive information embedded in scanned documents or images.
Cons
- The Linux support is inefficient.
- Policy deployment is not flexible.
- Its use on Mac-supported devices is problematic.
- Its cloud options are limited in comparison to its on-premise options.
Data classification examples
Data classification – public sector
Data classification – enterprises
Types of data classification
1. User-based classification
Users carry out manual data classification. Due to its reliance on manual categorization, lack of analysis, and dynamic nature, this type of data classification is prone to errors.
Source: Gartner
2. Context-based classification
Data is categorized according to its context or intended use. For instance, it can be classified as financial, research, customer, or intellectual property data. Other factors considered for data classification include file type and location.
3. Content-based classification
Content-based data classification, also known as content-aware data classification, involves analyzing the actual content of data to determine its classification.
Instead of relying solely on metadata (data about data) or predefined labels, content-based classification uses algorithms and techniques to scan the contents of files or data streams to identify sensitive or valuable information.
Here are common data rules based on content classification:
RegEx (regular expression) based data rule: Searches for the pattern of characters that are defined by regex rules.
Source: Google
Exact data match (EDM)/ keyword evidence-based data rule: Looks for the precise match of the keyword or combination of keywords prompted.
Source: Microsoft Learn
4. Sensitivity-based classification
Data can be classified into four different sensitivity levels:
1. Restricted: Data labeled as “restricted” is of the utmost sensitivity and requires the highest level of protection. This includes information that, if compromised, could cause severe damage to the organization, such as trade secrets or sensitive personal information.
2. Confidential: Data labeled as “confidential” is sensitive and requires protection from unauthorized access or disclosure. This category includes information that, if exposed, could harm the organization’s reputation, competitiveness, or compliance with regulations.
Examples may include financial records, customer data, or proprietary business strategies.
3. Internal: Data labeled as “internal” is restricted to authorized personnel within the organization. While it may not be classified as highly sensitive, it is intended for internal use and should not be shared externally without proper authorization.
Examples include internal documents, memos, or reports.
4. Public: Data labeled “public” is intended for unrestricted access and can be freely shared with anyone, both inside and outside the organization. This category typically includes information that poses minimal risk if disclosed, such as marketing materials or public announcements.
Common features of data classification software
- Automated and continuous content scanning for data discovery: Scans of data at rest or data in transit for sensitivity.
- Sensitive data compliance: Ensures compliance with regulatory requirements involving sensitive data such as personal information (PI) and personal health information (PHI).
- Audit trail: Monitors and logs agent activity.
- Access control: Administers access permissions to data based on user roles or designated rules.
- Data encryption: Encrypting data at rest, data in transit, or both.
- API/Integrations: Enables integration with APIs and Active Directory (AD).
Further reading
- Top 10 DSPM Vendors in 2024
- DLP Review: Benchmark Testing of 4 DLP Products
- Top 5 NinjaOne Alternatives
- Top 5 Endpoint Management Software
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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