Mage Data™ Included in Gartner® Market Overview: on Data Security Platforms

AI agents are reaching data that consolidation was supposed to have brought under one policy. Mage Data sets out why assembled platforms leave the gap open.

NEW YORK, NY, UNITED STATES, October 29, 2026 /EINPresswire.com/ — Mage Data, a data security platform for global enterprises, has been included among the Example Vendors in the Gartner’s “Market Overview: Data Security Platforms,” published 14 October 2026.

Disclaimer: The view that follows is Mage Data’s own and is not drawn from the Gartner research referenced above.
Enterprises bought data security platforms to replace the point products they had accumulated, expecting discovery, access control, masking and monitoring to finally work from the same understanding of their data.

Why does data security consolidation keep falling short?
Buying fewer, broader products was the right correction. Teams had accumulated point tools one sensible purchase at a time, until running them cost more than the coverage was worth. The problem is what most of those broader products are made of. The discovery engine came from one company, the masking engine from another, the monitoring from a third. Each one works. None was built to share a policy or a catalog with the others, so the customer supplies the integration.

The cost surfaces where it matters. A field classified as sensitive by the discovery tool is not automatically protected by the masking tool. A report pulled from one module does not reconcile with a report pulled from another. Teams maintain the same decision in three places and fully trust none of them.

What do autonomous AI agents change about unprotected data?
AI has removed the margin for error. An autonomous agent with broad read permissions does not browse data the way a person does. It retrieves at machine speed, across every store it can reach, and it will surface whatever was left unprotected in a store nobody got around to classifying. A gap that used to mean a slow audit finding now means an answer returned to a user who was never entitled to see it.
“Customers tell us they bought a platform and got a portfolio,” said Krishnan Cheerath, VP of Products at Mage Data. “You can see it in how they use it. Discovery runs in one place, masking rules are maintained in another, and nobody can say with confidence that the two agree. Put an AI agent on top of that and the disagreement ends up exposing sensitive data. Mage Data’s discovery, masking and monitoring were all developed in-house and all read from the same catalog. So when an auditor comes asking which systems hold regulated data, the business can show them, and show what was done about it.”
How Mage Data keeps protection consistent from the database to the AI assistant

Protection starts from one classification result. Discovery runs first across databases, data lakes, file storage, packaged applications, endpoints and AI environments, and everything after that works from what it found. Teams stop maintaining a separate list of what to mask where, because there isn’t a second list to maintain.

Each environment gets the protection it can actually work with. Applications that depend on the shape of their data keep working, because context-preserving masking preserves it. Analytics keeps its distributions. Developers get test data in the same week they ask for it, including fully synthetic sets where no masked version of the real data should exist. Production keeps the real data, governed by dynamic data masking, database firewalling and activity monitoring.

One policy covers all of it, so the answer doesn’t change with the route. A field classified as sensitive is treated the same whether it’s read from a database, pulled into a test environment, sitting in a document on a file share, or returned by an AI assistant. In August 2026 Mage Data extended that policy framework to AI workloads, covering training data, employee use of public generative AI tools, agents built in-house, and copilots that cannot be modified.
Read more: https://magedata.ai/products/data-security-privacy-for-ai/

Where does Mage Data run?
Inside the customer’s own environment. Mage Data deploys as a single-tenant installation, on-premises or in a private cloud. There is no multi-tenant service and no requirement to send data out for processing, which matters for organizations under data residency rules or public-sector certification requirements.
For enterprises under both AI pressure and audit pressure at the same time, that combination is the point: one classification that every control acts on, on their premise, with an evidence trail they can hand to a regulator.
For more information, visit www.magedata.ai

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Gartner, Market Overview: Data Masking and Synthesis, Joerg Fritsch, Andrew Bales, Anson Chen, 19 August 2026

About Mage Data
Mage Data is a leader in data security and privacy software for global enterprises. Mage Data has been included in the Gartner Market Overview for Data Security Platforms, Q4 2026, and the Gartner Market Overview for Data Masking and Synthetic Data, Q3 2026. It has also been included as a Notable Vendor in Forrester’s The Data Security Platforms Landscape, Q3 2026. Mage Data has also been named a Leader in KuppingerCole’s Leadership Compass: Data Security Platforms, Q4 2024, and 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 customers include Fortune 500 companies, leading organizations in the financial services and healthcare sectors, and Ivy League universities.

For more information, visit www.magedata.ai

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