As of November 2026, Latka tracks 6 data De-Identification tools companies with $1M–$5M in annual revenue. They have combined revenues of $17.2M and employ 156 people.
Every company below sells data De-Identification tools to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
Data De-Identification Tools are specialized software solutions designed to remove or obscure personally identifiable information (PII) from datasets, enabling organizations to protect individual privacy while making data available for analysis and research. These tools are essential in various sectors, including healthcare, finance, and education, where sensitive information needs to be handled in compliance with privacy regulations such as HIPAA and GDPR. The primary use cases for Data De-Identification Tools include preparing data for research, analytics, and machine learning while ensuring that the data cannot be traced back to any individual. Typical features of these tools include data masking, tokenization, pseudonymization, and the application of data minimization principles. Common buyer personas include data privacy officers, compliance managers, and IT security professionals who are responsible for safeguarding sensitive data assets within their organizations. Organizations that utilize these tools benefit from improved data security, enhanced compliance with legal requirements, and the ability to share data in an anonymized form that still retains its utility for analysis and decision-making. The adoption of Data De-Identification Tools is becoming increasingly critical as data breaches and privacy concerns continue to rise in today’s digital landscape.
Sorting: Highest -> Lowest
Showing 6 of 6 companies ranked by annual revenue.
Santa Clara, California, United States
An Artificially Intelligent, humanly impossible, previously unsolvable, hyper-accurate approach to comply with data privacy compliance and prevent synthetic fraud losses.
Arlington, Virginia, United States
CaseGuard is an AI Redaction Solution. Everything you need to redact and enhance any video, audio, image, or document in one redaction solution. Very flexible, affordable, easy to use, secure, fast, multilingual, and ready to integrate with other systems. CaseGuard helps law enforcement agencies, federal agencies, hospitals, schools, shopping centers, airports and private companies manage all their media redaction needs in one easy to use redaction software.
Paris, Ile-de-france, France
Developer of a cryptography platform designed to analyze encrypted data and create a secure data economy. The company's platform allows queries and calculations to be performed on encrypted private data without actually revealing the data itself, enabling companies to analyze private information without revealing the underlying data.
Missoula, Montana, United States
Developer of a cloud based data management platform designed to provide secure information sharing and management service. The company's TeraDactor provides a WebAutomator for mining and extracting data from Web sources, checks, versions, and releases sensitive documents and helps in integrating legacy data, enabling organizations to protect their sensitive information and helps in identifying, evaluating and extracting of information from existing system and sources.
Latka tracks 6 data De-Identification tools companies with $1M–$5M in annual revenue. Together they generate $17.2M in annual revenue and employ 156 people.
Data Safeguard Inc. is the largest, with $4.7M in annual revenue, founded in 2021.
The average data De-Identification tools company in this list makes $2.9M a year, across 6 companies with reported revenue.
Ranked by annual revenue, the leaders are Data Safeguard Inc., CaseGuard, brighter AI, Sarus and Cosmian.
- Must remove or obscure identifiable information to protect individual privacy - Should comply with industry regulations (e.g., HIPAA, GDPR) governing data privacy - Must provide options for data masking, tokenization, or pseudonymization techniques - Should enable organizations to analyze and utilize data without revealing personal identifiers - Not just focused on data storage; must also ensure compliance throughout data processing workflows - Should support multiple data formats and sources to serve diverse organizational needs - Designed for use by data privacy officers and compliance managers in their efforts to safeguard sensitive information
