Systems | Development | Analytics | API | Testing

DreamFactory 7.5.0 is focused on two audiences that have been growing fastest in our user base: teams wiring LLM agents to production databases through MCP, and security and platform teams hardening those deployments for real-world traffic.

Unaccounted data copies within non-production environments can make enterprises vulnerable to cyber theft. Non-production environments — which are often less secure than production environments — are treasure troves for hackers seeking to steal customer data. How many copies of test data are currently floating around your organization’s non-production environments?

Key takeaways: For AI workloads, pooling must handle long connection hold times and heavy traffic. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. Combined with tools like PgBouncer, these solutions free connections faster and improve scalability. Simple tweaks, such as segmenting pools and setting timeouts, can boost efficiency.

80% of enterprise apps still use decades-old systems, but accessing their data for AI is tough. The challenge? Security risks, outdated interfaces, and slow performance. Here's the solution: API abstraction. This method creates a secure, no-code layer between AI and legacy systems. It keeps your old code intact while enabling AI to access data safely and efficiently.

Healthcare is no longer compared to other hospitals. It’s compared to digital-first experiences across industries. Speed, transparency, and self-service are now baseline expectations. Recent insights from McKinsey & Company show that consumers are taking a far more active role in managing their health and expect easier, digitally enabled interactions across their care journey. At the same time, health systems are under pressure to modernize.

FTI audits are designed to ensure sensitive tax data is properly protected. But in modern enterprises, they’re about much more than passing inspections. Today, you need to manage FTI securely while still enabling fast, reliable access to data across DevOps, analytics, and increasingly, AI workflows. Treating FTI audits as part of a broader data strategy helps teams reduce risk without slowing innovation or creating bottlenecks.

If you’ve ever debugged a Node.js application in production, you’ve likely seen this: Sourcemaps were supposed to solve this. And technically, they do. But in practice, most teams still struggle to make sourcemaps available when they’re actually needed.

Dynamic Data Masking (DDM) is a real-time solution to protect sensitive information when AI systems access enterprise data. It intercepts database queries and applies masking rules based on user roles, ensuring sensitive fields like Social Security numbers or credit card details are hidden without altering the original data. This approach prevents accidental exposure, ensures compliance with regulations like HIPAA and GDPR, and safeguards against attacks like prompt injection (successful 91% of the time).