Top 10 Thales Vormetric Tokenization with Dynamic Data Masking Alternatives & Competitors

(5)4.3 out of 5

If you are considering Thales Vormetric Tokenization with Dynamic Data Masking, you may also want to investigate similar alternatives or competitors to find the best solution. Other important factors to consider when researching alternatives to Thales Vormetric Tokenization with Dynamic Data Masking include reliability and ease of use. The best overall Thales Vormetric Tokenization with Dynamic Data Masking alternative is Salesforce Platform. Other similar apps like Thales Vormetric Tokenization with Dynamic Data Masking are Privacy1, Satori Data Security Platform, Oracle Data Safe, and IBM InfoSphere Optim Data Privacy. Thales Vormetric Tokenization with Dynamic Data Masking alternatives can be found in Data Masking Software but may also be in Data De-Identification Tools or Cloud Platform as a Service (PaaS) Software.

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Best Paid & Free Alternatives to Thales Vormetric Tokenization with Dynamic Data Masking

Salesforce Platform
Privacy1
Satori Data Security Platform
Oracle Data Safe
IBM InfoSphere Optim Data Privacy
Clonetab
VGS Platform
Informatica Data Security Cloud

Top 10 Alternatives to Thales Vormetric Tokenization with Dynamic Data Masking Recently Reviewed By G2 Community

Browse options below. Based on reviewer data, you can see how Thales Vormetric Tokenization with Dynamic Data Masking stacks up to the competition, check reviews from current & previous users in industries like Computer Software, Consumer Goods, and Market Research, and find the best product for your business.

Salesforce Platform

By Salesforce

4.5/5

(3,903)

Product Description

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

By Privacy1

Product Description

Privacy1's Zero Trust Data Protection solution offers a comprehensive approach to safeguarding personal data by applying privacy-aware security directly to the data assets. This method shifts the focus from traditional perimeter defenses to a data-centric strategy, ensuring that sensitive information remains protected regardless of its location within the system. By encrypting data and implementing purpose-specific access controls, Privacy1 enables organizations to manage data access based on legal purposes, approved systems, and authorized personnel. This approach not only enhances data security but also ensures compliance with privacy regulations and builds trust with customers. Key Features and Functionality: - Consistent Protection: Maintains a uniform level of data security as information moves across various systems, regardless of differing perimeter security measures. - Purpose Control: Allows access to sensitive personal data solely for specific legal purposes, ensuring that data usage aligns with organizational policies and regulatory requirements. - Privacy Awareness: Integrates privacy considerations into data protection, enabling control over data usage across the organization from a legal standpoint. - Data Encryption: Ensures that data is encrypted, making it accessible only to legitimate systems and users for authorized purposes, both at rest and during transit. - Automated Privacy Rights Management: Facilitates the automation of data subject rights requests, such as access, erasure, and consent management, reducing manual overhead and enhancing compliance. Primary Value and Problem Solved: Privacy1's Zero Trust Data Protection addresses the critical challenge of data breaches and unauthorized access by implementing a data-centric security model. By encrypting data and enforcing purpose-specific access controls, it ensures that even if perimeter defenses are compromised, the data remains unreadable and secure. This solution not only mitigates the risk of data misuse but also simplifies compliance with privacy regulations, reduces operational costs associated with manual data protection processes, and enhances customer trust by demonstrating a commitment to data privacy and security.

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification

Satori Data Security Platform

By Commvault

4.7/5

(87)

Product Description

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data Masking

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IBM InfoSphere Optim Data Privacy

By IBM

4.6/5

(48)

Product Description

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification

Clonetab

By Clonetab

4.4/5

(48)

Product Description

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data Masking

VGS Platform

By Very Good Security

4.7/5

(47)

Product Description

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification

Data Masking

Informatica Data Security Cloud

By Informatica

4/5

(46)

Product Description

Informatica Data Security Cloud is a comprehensive solution designed to protect sensitive and private data, enhance compliance, and enforce data usage policies without requiring changes to existing applications. By leveraging advanced data masking and anonymization techniques, it ensures that critical information remains secure across various environments, including testing, development, analytics, and customer experience programs. This cloud-native service integrates seamlessly with Informatica's Intelligent Data Management Cloud, providing a holistic approach to data governance and privacy. Key Features and Functionality: - Cloud Data Masking: De-identifies and anonymizes sensitive data to prevent unauthorized access, supporting compliance and data security initiatives. - High-Performance Data Masking: Centrally manages and scales masking processes for large data volumes across diverse data stores and locations. - Robust Data Masking and Encryption: Utilizes various techniques such as substitution, format-preserving encryption (FPE, blurring, sequential, and randomization to protect data. - Broad Connectivity and Custom Application Support: Supports data masking across multiple formats and a wide range of databases, mainframes, and applications. - Automated Data Discovery: Identifies sensitive data locations quickly, ensuring consistent masking across databases. - Self-Service Data Warehouse: Stores, augments, shares, and reuses test datasets to improve testers’ efficiency. - Data Subset: Provisions smaller datasets to minimize infrastructure requirements and enhance performance. - Monitoring and Reporting: Engages risk, compliance, and audit teams to align with data governance initiatives. Primary Value and User Solutions: Informatica Data Security Cloud addresses the critical need for organizations to protect sensitive data while maintaining operational efficiency. By anonymizing and de-sensitizing data, it reduces the risk of data breaches and ensures compliance with data privacy regulations. The solution's ability to manage and scale data masking processes across various environments allows businesses to securely utilize data in testing, development, analytics, and customer experience initiatives without compromising data integrity or security. This empowers organizations to confidently share and use data, fostering innovation and informed decision-making.

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification

By Tonic.ai

Product Description

Tonic.ai frees developers to build with safe, high-fidelity synthetic data to accelerate software and AI innovation while protecting data privacy. Through industry-leading solutions for data synthesis, de-identification, and subsetting, our products enable on-demand access to realistic structured, semi-structured, and unstructured data for software development, testing, and AI model training. The product suite includes: - Tonic Fabricate for AI-powered synthetic data from scratch - Tonic Structural for modern test data management - Tonic Textual for unstructured data redaction and synthesis. Unblock innovation, eliminate collisions in testing, accelerate your engineering velocity, and ship better products, all while safeguarding data privacy.

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification

Product Description

Tumult Analytics is an advanced, open-source Python library designed to facilitate the deployment of differential privacy in data analysis. It enables organizations to generate statistical summaries from sensitive datasets while ensuring individual privacy is maintained. Trusted by institutions such as the U.S. Census Bureau, the Wikimedia Foundation, and the Internal Revenue Service, Tumult Analytics offers a robust and scalable solution for privacy-preserving data analysis. Key Features and Functionality: - Robust and Production-Ready: Developed and maintained by a team of differential privacy experts, Tumult Analytics is built for production environments and has been implemented by major institutions. - Scalable: Operating on Apache Spark, it efficiently processes datasets containing billions of rows, making it suitable for large-scale data analysis tasks. - User-Friendly APIs: The platform provides Python APIs that are familiar to users of Pandas and PySpark, facilitating easy adoption and integration into existing workflows. - Comprehensive Functionality: It supports a wide array of aggregation functions, data transformation operators, and privacy definitions, allowing for flexible and powerful data analysis under multiple privacy models. Primary Value and Problem Solved: Tumult Analytics addresses the critical challenge of extracting valuable insights from sensitive data without compromising individual privacy. By implementing differential privacy, it ensures that the risk of re-identification is minimized, enabling organizations to share and analyze data responsibly. This capability is particularly vital for sectors handling sensitive information, such as public institutions, healthcare, and finance, where maintaining data privacy is both a regulatory requirement and an ethical obligation.

Categories in common with Thales Vormetric Tokenization with Dynamic Data Masking:

Data De-Identification