At the inaugural QA Financial Healthcare & Insurance Forum London 2025, Amitai Richman, Director of Product Marketing at K2view, delivered a clear message to testing and engineering leaders.
The slowest and riskiest part of the AI-enabled delivery pipeline is not the tools or the infrastructure. It is the test data.
Richman opened by introducing K2view as a Visionary in Gartner’s Magic Quadrant for Data Integration and highlighted the company’s strength in connecting to any data source.
He emphasised that integrating with complex, multi-system environments is well understood, but the real challenge for regulated industries is delivering PHI-safe, production-like test data quickly enough to support modern delivery pipelines.
Richman pointed to findings from K2view’s 2025 State of Test Data Management survey. Although leadership teams often believe their organizations are compliant, 93 per cent of respondents admitted they are only “mostly compliant.”
As he noted, “mostly compliant” still leaves sensitive data exposed across QA, development, and training environments.
Richman described the multi-step, failure-prone process many QA teams follow today. Test data must be discovered, profiled, masked, stitched together across fragmented systems, validated, and kept in sync with changing schemas.
He explained that there are a dozen points where things can break, most often during PII and PHI discovery, masking, and the preservation of referential integrity. Legacy tools and homegrown scripts cannot keep up with the volume, variety, and regulatory requirements of healthcare and insurance data.
Richman then walked the audience through the evolution of TDM approaches, from manual scripting to legacy repositories to virtualization and synthetic data.
Synthetic data is essential when production data is off limits, when rare or edge-case clinical or claims scenarios are needed, when performance and load testing requires large volumes of safe data, or when organizations must eliminate PHI entirely from their lower environments.
Richman cautioned that it must be generated carefully. Poorly created synthetic records can produce inconsistencies, unrealistic patterns, implausible clinical scenarios, and broken relationships across systems. These issues reduce test coverage and increase downstream risk.
He explained that K2view takes a business-entity approach, organizing data around each member, patient, or claim. Every entity is stored in its own encrypted Micro-Database, which unifies the relevant data from all connected systems.
Because masking, subsetting, and synthetic generation happen at the entity level, QA teams receive consistent, compliant, and production-like test data regardless of system complexity. This model also enables self-service access for testers and seamless integration with CI/CD pipelines.
Richman noted that different types of testing require different types of data. Functional testing may rely on masked production data. Edge-case testing may require rule-based synthetic data. AI model testing may require GenAI-based synthetic data that preserves statistical patterns without exposing real PHI.
Looking ahead
Richman said generative AI is reshaping how test data will be consumed. He described a near future where a tester can simply request, in natural language,
“Find 20 members with pending claims and move them to UAT,” and the system provisions the data automatically. At the same time, AI is increasing the burden on QA teams because agentic systems generate a range of possible outcomes. Testers must learn to validate not a single expected result but a spectrum of acceptable results.
Richman closed with a direct message to the industry. Test data is one of the most overlooked bottlenecks in healthcare and insurance.
Without fixing PHI-safe test data at the source, organisations cannot secure lower environments, automate delivery, or trust AI-driven systems, he argued. Accelerating release cycles and achieving AI-enabled transformation requires high-quality, compliant test data available on demand.
Richman’s session was one of the central discussions at the Forum, where test data, compliance, and AI readiness emerged as defining themes for 2026 and beyond.
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REGULATION & COMPLIANCE
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