At a glance
| Supported databases | Oracle, SQL Server, Db2, Teradata, Trino, Snowflake, Redshift, Athena, Databricks, BigQuery, Exasol, Netezza, MongoDB, Couchbase |
| Auditing and activity monitoring | Not documented in detail on the pages checked |
| Masking | Masking as a protection method; anonymization module |
| Encryption or tokenization | Vaultless tokenization and encryption at field level |
| Privileged access | Policy controls who can unprotect data; not a PAM tool |
| Policy enforcement | Central ESA policy enforced by protectors in each platform |
| Compliance reporting | Not documented on the pages checked |
| Deployment | Self-managed protectors on premises or in cloud; container options |
| Licensing | Commercial, by quote; AI Developer, Team and Enterprise editions |
Protegrity suits large enterprises that need field-level protection that travels with the data across Oracle, SQL Server, Db2, Teradata, Snowflake, Databricks and applications, managed from one policy server. It is a heavyweight platform with quote-only pricing, so it pays off when the same policy must apply across many platforms.
Overview
Protegrity is a data protection vendor whose platform replaces sensitive values with tokens or ciphertext and reveals them only to authorised users. Policy is defined centrally in the Enterprise Security Administrator (ESA) and enforced by protectors installed where data is stored or processed. Protegrity lists vaultless tokenization, encryption and masking as protection methods, plus data discovery, anonymization and synthetic data modules.
The documentation groups protectors by platform: a Database Protector for Oracle, SQL Server and Db2; Data Warehouse Protectors for Teradata and Trino; a Big Data Protector for Amazon EMR, Databricks on AWS and Azure and Cloudera CDP; Cloud Protect for Snowflake, Amazon Redshift, Athena and S3; and Application Protectors for Java, Python, .NET, C and JDBC. Recent releases add AI-focused features such as semantic guardrails for prompts and a gateway for MCP and REST endpoints.
Key features
- Vaultless tokenization, encryption and masking of individual fields
- Central policy and key administration in the Enterprise Security Administrator (ESA)
- Database Protector for Oracle, SQL Server and Db2, and warehouse protectors for Teradata and Trino
- Cloud Protect components for Snowflake, Amazon Redshift, Amazon Athena and Amazon S3
- Big Data Protector for Amazon EMR, Databricks (AWS and Azure) and Cloudera CDP
- Application Protectors for Java, Python, .NET, C and JDBC, plus a Data Security Gateway
- Data Discovery, anonymization, synthetic data and semantic guardrails modules
Integrations and ecosystem
Pros and cons
Pros
- Protection travels with the data: tokens stay protected when copied between systems
- One policy across databases, warehouses, big data and applications
- Native protectors for several warehouses and cloud data platforms
- Vaultless tokenization avoids a central token lookup database
Cons
- No public pricing
- Protectors must be deployed and kept current on each platform
- Not every database is covered natively; PostgreSQL and MySQL are not named in the protector list checked
- Applications and queries may need changes where data is tokenized
Pricing
Protegrity publishes no prices. Its documentation lists an AI Developer Edition (described as a self-contained experimentation platform), an AI Team Edition and an AI Enterprise Edition, and the website offers a "Try for Free" option; production use of the Team and Enterprise editions is by quote. The terms of the free option are not stated on the pages checked.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Large enterprises that must tokenize the same sensitive fields consistently across many databases, warehouses and applications.
Typical use cases
- Tokenize card numbers in Oracle and Teradata so analysts work with tokens
- Protect PII columns in Snowflake or Redshift and reveal them only to approved roles
- Apply one protection policy to data moving from on-premises databases to Databricks
- Protect fields inside Java or Python applications before data reaches the database
Who should look elsewhere
- Teams that only need masked test copies: see DataMasque or K2view.
- Teams whose main need is central key management and file encryption: see Thales CipherTrust.
Verdict
Protegrity is a mature choice when tokenization has to follow data across many platforms under one policy, with native protectors for major databases and warehouses. The cost is operational: protectors to deploy, policies to design and quote-only licensing. Check that every platform you run appears in the current protector matrix before you sign.
Frequently asked questions
What is vaultless tokenization?
Vaultless tokenization replaces a sensitive value with a token without keeping a lookup database (vault) of every original value. In Protegrity, authorised users and applications detokenize through the protectors under the central policy.
Which databases does Protegrity support?
Its documentation lists a Database Protector for Oracle, SQL Server and Db2, warehouse protectors for Teradata and Trino, Cloud Protect for Snowflake, Redshift and Athena, and a Big Data Protector for EMR, Databricks and Cloudera CDP. The integrations page also lists BigQuery, Exasol, Netezza, MongoDB and Couchbase.
Is there a free version?
Protegrity offers a "Try for Free" option and an AI Developer Edition described as an experimentation platform. Production editions (AI Team and AI Enterprise) are priced by quote.
Sources
- Protegrity website
- Protegrity integrations
- Protegrity documentation home (editions)
- Protegrity Protectors 10.0 documentation
- Protegrity Cloud Protect 4.1.0 documentation
Checked November 2026.
Listed in: Database Security & Auditing.
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