QDA Miner & WordStat 2026.1: Greater Control Over AI Processing of Sensitive Research Data

2026-05-29 · Source: Provalis Research · Field: Science & Research — Research Methodology & Innovation, Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, quick

Summary

Provalis Research has released QDA Miner and WordStat versions 2026.1, introducing enhanced controls for generative AI processing of sensitive research data. This update addresses concerns from researchers and institutions regarding data location and usage when integrating AI into qualitative and text analytics. The new version allows AI processing to be directed to organization-approved environments, moving beyond single vendor-controlled services. Key options include local AI processing using tools like LM Studio or Ollama, private AI environments connectable via OpenAI-compatible servers (e.g., Azure OpenAI, Google Vertex AI through a gateway), and additional routing options for public OpenAI API users, such as European processing endpoints. Furthermore, administrative settings enable organizations to disable generative AI features and internet-based tools entirely. These features empower researchers, institutional review boards, privacy officers, and IT departments to better manage data processing locations and conditions.

Key takeaway

For IT Professionals or AI Architects managing sensitive research data, QDA Miner & WordStat 2026.1 offers crucial controls. You can now direct generative AI processing to approved local or private environments, ensuring compliance with data retention and privacy policies. Coordinate with research teams to configure connections to internal AI servers or secure cloud gateways like Azure OpenAI. This update helps you mitigate risks associated with public AI services and maintain data sovereignty.

Key insights

The 2026.1 update for QDA Miner and WordStat provides granular control over generative AI data processing locations for sensitive research.

Principles

• Data sovereignty is critical for sensitive research.
• Organizations need flexible AI deployment options.
• Administrative controls enhance data governance.

Method

The article describes options for configuring AI processing: local execution (LM Studio, Ollama), private server connections (OpenAI-compatible, Azure OpenAI), or public API routing (European endpoints).

In practice

• Run open-weight models locally with LM Studio.
• Connect to Azure OpenAI via a private gateway.
• Disable AI features for specific projects.

Topics

• QDA Miner
• WordStat
• Generative AI
• Data Privacy
• Local AI Processing
• Private AI Environments
• OpenAI API

Best for: CTO, Research Scientist, AI Architect, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Provalis Research.