Confluent simplifies secure, real-time AI deployment for enterprises

Confluent announced new capabilities in Confluent Intelligence and Confluent Cloud that streamline how real-time artificial intelligence (AI) applications are built and secured.

These updates remove the security and complexity barriers that stop organisations from moving AI workloads into the real world:

• Confluent unifies the AI life cycle with tools that developers already live in, integrating Apache Flink pipelines with dbt (data build tool) and introducing a fully managed Model Context Protocol (MCP) server and Agent Skills that let AI manage streaming operations.
• 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.

“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 says, “… 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 due to exposure risks and developers losing hours to tool-switching to inspect and manage the data streams their AI depends on. The resulting slow, manual process 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 Confluent MCP 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 organisational 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 such as financial services, healthcare, and insurance. Available in early access for Confluent Intelligence.
• Secure connectivity: Support for Azure Private Link ensures that 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 such as 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 that 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 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.

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.

These capabilities extend recent announcements at IBM Think that integrate Confluent Cloud further into IBM solutions. With Confluent, watsonx.data delivers an AI-ready data foundation and a real-time context layer for AI across hybrid environments.