A self-contained DataMasque Ready-to-Run Sample Set. Launches with a bundled PostgreSQL database and PGAdmin so you can mask realistic sample data and inspect before/after results in minutes - no setup, no data of your own required.
Protect sensitive data for development, testing, analytics and AI/ML ops without compromising on data quality and innovation with DataMasque. Automatically discover and mask sensitive data - including PII, PHI and PCI - to generate privacy compliant, realistic and irreversible synthetically identical data. No matter where your data lives - in databases, files, the cloud, on-prem or across environments - DataMasque ensures it is masked consistently. DataMasque supports masking of primary and unique keys and automatically maintains referential integrity of foreign keys.
This edition ships with a PostgreSQL sample database and the pgAdmin database client alongside the DataMasque engine, with a connection and a comprehensive masking ruleset already configured. Launch the instance, open DataMasque, run the pre-built masking job, then open pgAdmin straight from the run log to compare the data before and after. No configuration, no data preparation and no registration.
Register to manage your own connections, mask files and use in-flight masking against your own databases. Moving to production is a licence change, not a redeployment. Choose Bring Your Own License (BYOL) or a contract subscription and the bundled PostgreSQL and pgAdmin containers are automatically retired, leaving the same engine running against your own data.
DataMasque is fully air-gapped with no telemetry and no phone-home. Your data stays entirely within your own AWS account.
Highlights
Self-contained, hands-on in minutes - a bundled PostgreSQL sample database and pgAdmin client ship alongside the DataMasque engine, with a connection and masking ruleset already configured. Run a masking job out of the box and compare before and after directly in pgAdmin.
Data masking in action - generate privacy compliant, realistic and irreversible synthetically identical data from PII, PHI and PCI, with referential integrity preserved across keys and tables. This edition masks up to 1,000 rows per job.
Scale up when you're ready - register to mask your own data with your own connections, then move to Bring Your Own License (BYOL) or a contract subscription for unlimited volume. Fully air-gapped with no telemetry and no phone-home.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay by the hour based on the AWS instance size you run this software on. The five options are compute instances that differ in processing power and memory. The t3.2xlarge is a general-purpose instance, while the c5 options are compute-optimized and scale up from c5.2xlarge to c5.12xlarge. Larger instances handle bigger or faster masking workloads. You pick the instance that fits your data volume and speed needs, then pay only for the hours you use. There is no upfront commitment or fixed term with this usage-based model.
Top-of-mind questions for buyers
What data volume can this ready-to-run sample set process per masking run?
This sample set handles up to 1000 rows per run and includes one data source of any type, such as a relational database, NoSQL database, or data warehouse. It suits trial or evaluation workloads rather than production-scale masking of full datasets.
Am I charged when the instance is stopped or idle?
You pay hourly only while the instance runs. Stopping the instance halts the software hourly charge. Underlying AWS storage or resource fees may still apply while the instance is stopped, but the software meters running time only.
How do I choose between the t3.2xlarge and the c5 instances?
The t3.2xlarge is general-purpose and fits lighter or bursty workloads. The c5 options are compute-optimized and scale from c5.2xlarge to c5.12xlarge for heavier processing. All bill hourly, so you match instance power to your masking speed needs.
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Table References
A table reference points at an existing table or a file of key-to-value rows, and a ruleset can read it as a lookup while masking. Used as a masking seed, it keeps masked values consistent across separate systems.
Condition Values from a Table Reference
The in and not_in conditions can now read their values from a table reference column, so the list of permitted values lives outside the ruleset instead of being repeated inside it.
Shared Hash Columns Across Foreign Keys
A new ruleset generator option, on by default, applies your Table Hash Column selection to the matching column in every table it joins to, letting related tables share a single hash column.
Cross-Account Resource Tagging
PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, IBM Db2 LUW and Amazon Redshift connections can now hold an IAM role ARN, which DataMasque assumes to tag an RDS, Aurora or Redshift resource in another AWS account. Tagging now also works in the China and ISO partitions.
File Discovery Summary in the Run Log
Discovery runs now report how many files were scanned and how many were skipped, with a per-extension count for each reason a file was passed over.
Filter Run Logs by Multiple Statuses
The run logs can now be filtered by more than one Run Status at a time.
Shared In-Data Discovery Settings
A discovery configuration library can hold an idd_settings block, letting discovery configurations import their In-Data Discovery settings instead of repeating them.
In-Data Discovery On by Default
In-Data Discovery is now enabled by default for discovery runs and in new discovery configurations. Safe Data Preview remains opt-in, and saved discovery configurations keep whatever setting they already have.
Sensitive Classification Takes Precedence
When a column or file field matches both a sensitive and a non-sensitive rule, discovery now reports the sensitive classification. Existing discovery results may show a different classification after upgrading.
New Default Matcher Priority
The default matcher priority for unstructured masking is now context_sources, regex, checksum, seed_files, ai_detect. Masked output can change for an existing ruleset where two matchers find the same text.
Smaller PDFs and Lower Memory Use
PDF masking now embeds one copy of each substitute font per document rather than one per page, producing smaller masked PDFs and using considerably less memory on long documents.
Multi-Table and Parallel DynamoDB Masking
An Amazon DynamoDB ruleset can now mask more than one table, run its task blocks in parallel, and split the masking of a single table across several workers.
Explicit Label Masking in Ruleset Generation
Each label in a ruleset generation configuration must now set exactly one of preset_mask, mask, skip_generation or the new use_data_type_default. A label setting none of them stops the configuration from generating a ruleset; add use_data_type_default: true to preserve its previous behaviour.
Direct Navigation to a Run
Opening a run from a link now loads its page in the run logs immediately, instead of stepping through each page to find it.
API Keys Survive Password Changes
A user's API key is now replaced rather than removed when their password changes, so View run command always has a token available. New accounts receive their first key when the temporary password is replaced.
XML CDATA Masking Fix
Fixed XML masking leaving an element's text unmasked when wrapped in CDATA, and masking only part of the text when a child element or comment split it. A text transform now masks an element's whole text as a single value and writes CDATA sections back as plain text.
File Discovery Sensitive Results Fix
Fixed file discovery hiding a result from the sensitive-only view unless the displayed match was itself sensitive. A result now counts as sensitive unless every one of its matches is non-sensitive, so its data no longer drops out of the review or the generated ruleset.
IBM Db2 LUW Foreign Key Discovery Fix
Fixed foreign key discovery on IBM Db2 LUW reporting a table's own primary key as a foreign key when another table held a constraint of the same name.
PDF Image Placement Fix
Fixed PDF masking discarding the flip and rotation carried by an image's placement. Open vector paths are also no longer closed when the document is rebuilt.
Unstructured Preview Non-ASCII Fix
Fixed the Unstructured Ruleset Builder preview masking the wrong part of text containing non-ASCII characters, which could leave some of the original text visible. Masking runs were not affected.
Additional details
Usage instructions
Please follow the steps below to complete setting up your DataMasque instance:
Access the application via a web browser at https://<instance-ip-or-hostname>. The application may take a few minutes to start. Please refresh the page if you encounter the "Unexpected Error" message.
Complete the first-time installation page by providing the following information:
Email address of the DataMasque admin user. This email address is stored on the DataMasque EC2 instance and is used for the purposes of providing 'Forgotten Password' account recovery and critical system notifications. DataMasque will not have access to this information.
Password for the admin user.
Hostnames or IP addresses to access the DataMasque instance.
The SMTP settings specific to your organisation.
The instance ID of your EC2 instance.
You will be re-directed to the DataMasque login screen. Please proceed to login with the admin password you have just configured.
Note:
This product allows masking up to a total of 500GB across unique data sources.
When you use our software we may receive and store usage data and information relating to the performance and use of the Software. We will not disclose any system information which identifies the user or the user environment to third parties.
DataMasque provides full product and installation support within 72 hours of making an enquiry. Contact the support team at support@datamasque.com for any enquiries you may have.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
DataMasque helps enterprises accelerate development, testing, analytics and AI with synthetically identical customer data. Fully functional, realistic and privacy compliant.
DataMasque is a data masking platform that transforms sensitive production data into realistic, fully functional and privacy-compliant datasets.
Its synthetically identical data preserves the statistical characteristics, complexity and edge cases of your original data while maintaining referential integrity and data consistency - without sensitive information ever leaving your secure environment.
DataMasque for Guidewire Cloud enables insurers to de-identify sensitive Guidewire policyholder and claims data. DataMasque generates synthetically identical data that can be safely used across testing, analytics and AI use cases. Fully functional, realistic and privacy compliant.
Combining direct technical delivery with targeted organizational and stakeholder engagement, this service is for organizations seeking to implement, extend, or operationalize DataMasque data masking. Scope covers technical build and enterprise integration, including direct collaboration with compliance, legal, business, and technical teams to review risk, define requirements, map data flows, and align with organizational processes. The service includes discovery and classification of sensitive data, development and configuration of reusable architectures, and integration with AWS services. Knowledge transfer is delivered through documentation, runbooks, mentoring, and internal showcases, preparing staff for sustainable operation. The approach can be tailored to any stage of a client’s DataMasque journey.
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