LONDON — Data streaming provider Confluent today announced new capabilities in Confluent Intelligence and Confluent Cloud that streamline how real-time, artificial intelligence (AI) applications are built and secured.
Confluent unifies the AI life cycle with tools developers already live in, integrating Apache Flink pipelines with dbt and introducing a fully managed Model Context Protocol (MCP) server and Agent Skills that let AI manage streaming operations. Additionally, with automated personally identifiable information (PII) redaction and private connectivity to external models via Azure Private Link, Confluent embeds enterprise-grade governance directly into the data streams.
These updates remove the security and complexity barriers that stop organizations from moving AI workloads into the real world.
“Most AI projects fail before they reach a single customer because the data layer breaks down,” said Sean Falconer, Head of AI at Confluent. “Teams have the models and the mandate, but security risks and fragmented data stop them from shipping. We’re fixing that by making the streaming layer the foundation for secure, production-ready AI.”
The problem is widespread according to a McKinsey report that finds “eight in ten companies cite data limitations as a roadblock to scaling agentic AI.” Root causes are often tied to security teams blocking data from entering AI pipelines over exposure risks, and developers losing hours to tool switching to inspect and manage the data streams their AI depends on. The result is a slow, manual process that turns what should be a fast iteration cycle into a bottleneck.
The Engine for Secure, Scalable AI
Confluent Cloud and Confluent Intelligence form the data streaming foundation for production-ready AI that continuously processes historic and real-time data and delivers it as trusted context into AI applications. New capabilities add the security controls and developer tooling that high-stakes industries require.
- Natural Language Operations: Developers can use a fully managed MCP server as a control plane, allowing AI to build, manage, and debug streaming operations using natural language. Agent skills add a second layer, encoding best practices and workflows so those operations are executed consistently and in line with organizational standards. Together, they enable developers to create and continuously improve real-time applications using AI-powered tools, bringing streaming into modern, agent-driven development workflows. Generally available for Confluent Cloud.
- Automated Data Privacy: A new built-in ML function for PII detection and redaction protects sensitive information directly in Flink SQL, without custom code, external services, or moving data to a warehouse first. This unlocks more AI use cases across highly regulated industries like financial services, healthcare, and insurance. Available in early access for Confluent Intelligence.
- Secure Connectivity: Support for Azure Private Link ensures AI workloads stay off the public internet with secure, private paths to calling external models and querying external tables. Now, Flink jobs can securely connect to Azure-hosted services like Azure OpenAI, Azure SQL, and Cosmos DB over Microsoft’s private backbone. Generally available on Confluent Cloud.
- Unified Engineering Workflows: The free, open-source dbt adapter brings Flink SQL on Confluent Cloud into dbt, the industry-standard framework data engineers use to build and manage data pipelines. Teams can immediately define, test, and deploy streaming pipelines using the same dbt commands and project structure they rely on today. This lowers the barrier to Flink adoption and makes it easier to extend existing data workflows into real-time use cases. Generally available on Confluent Cloud.
- Flexibility with additional model and vector database support: Confluent supports TimesFM models for robust anomaly detection as well as Anthropic and Fireworks AI models, which developers can directly use in Flink stream processing workflows to build sophisticated real-time AI applications. Additionally, support for vector search on Amazon DynamoDB expands the modern AI stack ecosystem.
To learn more about today’s announcements, visit the Confluent blogs for Confluent Intelligence and Confluent Cloud. Highlights include the general availability of the Real-Time Context Engine, which continuously delivers fresh, governed context for AI applications, and new fully managed connectors in Confluent Cloud that further simplify data integration.
