r/SpringAIDev

Hi, I’m building Spring AI Privacy Guardrails, an open-source library for enforcing privacy boundaries around models, RAG, tools, MCP, and outputs.

GitHub: https://github.com/ultramancode/spring-ai-privacy-guardrails

One design problem I’ve been thinking about is streaming output protection.

Some applications also want a final privacy check on application-facing output, since sensitive data can still appear in model- or tool-generated responses.

Right now, when output protection is enabled, the library buffers the complete response before releasing it to the application.

This provides a strong guarantee: PII can still be detected and protected even when a sensitive value is split across multiple chunks.

The trade-off is that this is no longer true incremental streaming, and the application has to wait longer before receiving output.

A bounded rolling window could preserve incremental streaming for analyzers that have a known upper bound on how much context they need — for example, some bounded pattern-based detectors.

But NER, context-aware detection, complex patterns, or arbitrary custom analyzers may not have such a bound.

So I’m currently considering three approaches:

1. Strict buffering
   Buffer the complete response and protect it before releasing anything to the application.

2. Capability-gated streaming
   Allow incremental streaming only when the active analyzer can declare a safe maximum lookback or context requirement. Otherwise, fall back to full buffering.

3. Best-effort streaming
   Use a configurable rolling window and explicitly document that some PII spanning multiple chunks may escape detection.

For a Spring AI application, which behavior would you expect from a privacy library?

I’m not attached to these three options — if there’s a better streaming/privacy model I’m missing, I’d really appreciate the feedback.

Hi everyone!

My Spring AI course is now available on JetBrains Academy.

The course is designed around practical, real-world tasks completed directly in IntelliJ IDEA using the JetBrains Academy plugin. The project, dependencies, and configuration are already prepared, so you can focus on learning Spring AI and writing code instead of spending time on setup.

I honestly wish I’d had this kind of learning experience when I was starting out: clear tasks, a ready-to-use project, and immediate feedback—all inside the same IDE used for professional development.

I’d be glad to hear your feedback, especially which Spring AI topics or practical use cases you’d like to see covered next.

Course link: https://academy.jetbrains.com/course/32882