Mage Data™ Included in the 2026 Gartner® Market Overview: Data Masking and Synthesis

Mage Data sets out what it thinks the shift of unstructured data into AI pipelines now demands of data masking and synthesis vendors

NEW YORK, NY, UNITED STATES, November 10, 2026 /EINPresswire.com/ — Mage Data, a data security platform for global enterprises, today announced its inclusion in the Gartner Market Overview, “Market Overview: Data Masking and Synthesis,” published 19 August 2026, where the company is listed among the Example Vendors in the data masking and synthesis market.

The ground is moving under structured-data masking

Enterprises are now feeding call logs, contracts, support tickets, PDFs and images into AI training pipelines and retrieval systems. That content has never been inside the perimeter of a masking rule written for a database column. At the same time, privacy regulators are moving toward a higher standard of proof for anonymization, so organizations increasingly have to document and defend the methods they use rather than assume a masked field is out of scope.

The result is a market where masking is no longer a database task. It is a control that has to follow data into unstructured content, into AI pipelines, and into an audit trail.

Mage Data’s View of the Market

Across Mage Data engagements in financial services, healthcare and the public sector, the same requirements keep surfacing:

Test data that arrives in hours rather than weeks, because AI initiatives and release cycles are now queued behind the same provisioning bottleneck
Non-production environments that hold no real data at all, rather than real data with controls wrapped around it
A protection method chosen per use case — context-preserving masking, format-preserving encryption or fully synthetic data, depending on whether the environment is built for performance testing, application testing or analytics
Sensitive content found in the forms it actually takes — documents, spreadsheets, PDFs, images and free-text fields — not only structured database columns
Protection policies that can be enforced and evidenced to a regulator, not only logged
Control over what an AI assistant can reveal, tied to the entitlements of the person behind the question

“For two decades this market was judged on how well a product handled rows and columns, and that is no longer the test. The sensitive data moving through an enterprise today sits in contracts, call logs and images, and it is reaching AI pipelines faster than anyone is governing it. Anonymization now has to be defensible, not decorative. That, we believe, is the discipline we have spent years building, and it is the one the market is finally asking for,” said Krishnan Cheerath, Vice President of Products at Mage Data.

Where Mage Data fits

Mage Data is a data security platform built on a single principle: protect the data itself, then monitor what remains. Sensitive data discovery and classification runs first, across structured, semi-structured and unstructured sources, using pattern matching, data matching, NLP for free text and language models for complex content such as call logs, contracts and images. Everything downstream inherits those results, so protection follows the classification rather than a hand-maintained list of columns.

Real data has to live in production. It has no reason to live anywhere else.

So in non-production, Mage Data masks irreversibly as data is provisioned — in place, or in transit as it moves between platforms — with referential integrity resolved automatically. Where masked data is not the right answer, Mage Data generates synthetic identity data: a fabricated set derived from the real one, representative enough to build and test against, with no path back to the source records. Database virtualization applies the same logic to provisioning: copies are created from a masked snapshot, so environments are born compliant instead of fixed afterwards.

Production gets dynamic data masking, database firewalling and activity monitoring instead.

In August 2026 the company extended the same policy framework to AI workloads with Data Security and Privacy for AI, covering training data, employee use of public generative-AI tools, agents built in-house and copilots that cannot be modified. Enterprises apply the policies they already run on the platform to their AI workloads, with no parallel compliance regime for AI.

For more information, visit https://magedata.ai/products/data-security-privacy-for-ai/

Gartner, Market Overview: Data Masking and Synthesis, Joerg Fritsch, Andrew Bales, et al., 19 August 2026.

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About Mage Data

Mage Data is a leader in data security and privacy software for global enterprises. Mage Data has been included as a notable vendor in Forrester’s The Data Security Platforms Landscape, Q3 2026, recognized as a Leader in Data Security Platforms by KuppingerCole, and named a Champion in Test Data Management by Bloor Research. Its award-winning and patented platform helps organizations navigate privacy regulations while protecting sensitive data. Mage Data’s client roster includes Fortune 500 companies, leaders in the financial and healthcare sectors, and Ivy League universities.

For more information, visit www.magedata.ai

Rajesh Parthasarathy
MageData.ai
+91 89392 17140
bards@magedata.ai
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