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DreamFactory 7.4.5 ships the aggregate_data MCP tool — a purpose-built tool that lets AI agents compute SUM, COUNT, AVG , MIN, and MAX directly on the database server in a single call. This release also adds Cursor IDE OAuth compatibility, a desktop OAuth success page for smoother onboarding, server-side aggregate expression support across all SQL connectors, and critical MCP daemon stability improvements including request timeout guards and global error handlers.

Both methods have their place, but identity passthrough is the go-to for modern, secure, and compliant data access.

In an era where API breaches make headlines weekly, choosing the right authentication mechanism isn't just a technical decision—it's a business-critical one. OAuth 2.0 has emerged as the industry standard for securing API access, and when combined with Azure Active Directory and DreamFactory, it creates a robust, enterprise-ready security architecture that protects your data while streamlining user access.

As organizations increasingly recognize the value of generative artificial intelligence, many are moving away from cloud hosted models in favor of on premises Large Language Models. This shift is primarily driven by the need to protect sensitive corporate data, maintain regulatory compliance, and reduce latency. However, an isolated local model offers limited utility. To truly unlock the potential of an on premises LLM, enterprises must connect it to their internal databases and APIs.

In March 2026, security researcher isfinne discovered that LiteLLM version 1.82.8—the most popular open-source LLM proxy in the Python ecosystem, with approximately 97 million monthly downloads—contained credential-stealing malware published to PyPI. Within hours, version 1.82.7 was confirmed to carry a similar payload through a different injection method.

In the span of five days in March 2026, a single threat actor—TeamPCP—compromised a vulnerability scanner (Trivy), a code analysis platform (Checkmarx), and the most widely used LLM proxy in the Python ecosystem (LiteLLM). The attack chain was surgical: each compromised tool provided credentials to attack the next target.

Enterprise adoption of large language models introduces a fundamental security challenge: how do you grant AI agents access to internal data without creating a backdoor that bypasses your existing access controls? Traditional database connections rely on service accounts with broad permissions, but when an LLM queries your customer records or financial data on behalf of a user, it must respect that user's specific entitlements.

Government and defense agencies require extreme security measures to protect sensitive data like classified intelligence and military operations. Air-gapped systems, which are physically isolated from external networks, provide a robust solution by ensuring no remote access is possible. These systems are critical for deploying large language models (LLMs) safely in secure environments, enabling advanced AI capabilities like intelligence analysis and mission planning without risking data breaches.

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. It offers automated RBAC, precise field-level access control, configurable rate limits, and detailed audit logging that connects with SIEM and compliance systems.

An AI Data Gateway is a secure intermediary that connects enterprise data sources (like databases and file systems) with AI systems. It simplifies how AI accesses data while enforcing strict security, compliance, and governance measures. Instead of allowing direct access to sensitive data, the gateway uses secure REST APIs to control and monitor all interactions.