Best Data Masking Software

K2view delivers governed business data and context to the systems that test, run and improve critical applications, positioning itself around AI-ready data. It has been named a Visionary in Gartner's Magic Quadrant for Data Integration Tools for three consecutive years, has raised further funding for agentic AI work, and partners with Rocket Software on mainframe modernisation.

Its distinguishing idea is organising data by business entity rather than by table. A customer's records are scattered across a dozen systems, and assembling that one customer on demand, complete and masked, is a different operation from copying whole tables and masking them in bulk.

That approach matters most for test data. A tester needs a realistic customer with a plausible history rather than a random subset of rows, and entity-based extraction produces exactly that while removing the identifying detail.

Mainframe modernisation being a named partnership tells you where the hard problems are. Organisations with decades of data on systems nobody wants to touch are the ones with the worst test data problem, because copying production is unaffordable and inventing data does not exercise the real code paths. Entity extraction is one of the few approaches that works against a system nobody is willing to modify.

Immuta provisions data in real time with full control across a data ecosystem, and its current positioning treats AI agents as first-class participants rather than as an exception. The vendor's argument is that agents are now primary data consumers and must be governed as such, allowing them fast access without abandoning the controls applied to human users.

Policy applied at query time rather than by copying is the technical approach that distinguishes this category. Creating a masked copy for each team produces copies that diverge and multiply, while enforcing a policy as the query runs means one dataset serves everybody with different rows and columns visible to each.

Treating agents as governable participants is a genuinely current problem rather than a marketing frame. An agent acting for a user should see what that user may see, and an agent acting autonomously needs its own identity and limits, and neither works if access control assumes a person at a keyboard.

Speed is the constraint that makes this hard. Policy evaluated on every query sits in the path of every analytical workload, so the interesting engineering question is what it costs in latency at the volumes an organisation actually runs.

Delphix automates data delivery and data protection for DevOps and AI pipelines, providing virtualised copies of production data with sensitive values masked, and scaling that across on-premises and cloud topologies. It is now part of Perforce, whose wider portfolio covers code, content and testing.

Data virtualisation is the technique the product is built on and it changes the economics of test environments. A full copy of a production database for every developer is unaffordable in storage and slow to create, whereas virtual copies sharing common blocks can be delivered in minutes and discarded freely.

Masking applied as part of that delivery is what makes it defensible. A virtual copy is still production data unless the identifying values are replaced, and combining the two operations means a developer cannot receive an unmasked environment by mistake.

The Perforce acquisition is worth knowing before buying. The product now sits inside a portfolio built around development tooling, which is a coherent place for it, and roadmap questions belong to the parent rather than to the original company. Anyone searching for material should read across both names, since the older documentation and reviews sit under Delphix alone.