Confluent Simplifies Building and Securing Scalable Real-Time AI

Confluent, Inc., the pioneer of data streaming, has announced new features for Confluent Intelligence and Confluent Cloud, streamlining the process of building and securing real-time AI applications. These updates remove the security and complexity barriers that prevent enterprises from moving AI workloads into production. Confluent integrates the AI lifecycle with the tools developers use daily. It integrates Apache Flink® pipelines with dbt, introduces a fully managed Model Context Protocol (MCP) server, and Agent Skills to manage AI-driven streaming operations. Through automated Personally Identifiable Information (PII) redaction and private connections to external models via Azure Private Link, Confluent embeds enterprise-grade governance directly into data streams. Sean Falconer, Head of AI at Confluent, stated, 'Most AI projects fail before reaching a single customer because the data layer fails. Teams have models and clear missions, but security risks and fragmented data hinder releases. We are solving this by making the streaming layer a secure, production-ready foundation for AI.' According to a McKinsey report, '8 out of 10 companies cite data limitations as a barrier to scaling agentic AI.' The root cause lies in security teams blocking data flow due to leakage concerns, and developers spending hours switching tools to manage data streams. Consequently, what should be a rapid iteration cycle becomes a slow, manual bottleneck. As an engine for secure and scalable AI, Confluent Cloud and Confluent Intelligence form a production-ready data streaming foundation, processing historical and real-time data to provide trusted context to AI applications. The new features include natural language operations, automated data privacy, secure connectivity, integrated engineering workflows, and flexibility through additional model and vector database support.