Introducing the OCSF Normalization Playground for Preparing Data for...
summary
Tool demonstrates rapid generation of OCSF 1.1.0 transformations using a GenAI assistant and produces runnable Python extraction/transformation code that validates against the spec, reducing manual mapping time from days to minutes for a log source. It uses OCSF Normalization Playground front end with a Django backend, calls Claude via Bedrock for entity mapping, and outputs Data Prepper pipelines plus index templates and S3 routing; generated code validates and runs in demos.
AI-written summary · about
chapters (13)
- 0:00OCSF and Open Search intro
- 4:44OCSF challenges in Open Search
- 7:13Data Prepper limitations
- 10:26OCSF Normalization Playground intro
- 13:36Meet Chris Halma and demo setup
- 17:07OCSF Playground interface
- 19:13Building transformations with AI
- 23:26Python code for OCSF compliance
- 27:25Validating transformations
- 32:30Playground flexibility and future
- 37:03AI validation and hallucinations
- 38:06OCSF Playground roadmap
- 41:14Conclusion and Q&A
related talks
13mStreamline security operations and improve threat detection with OCSF
32mFrom Logs to Living Timelines: AI-Assisted Incident Response in Hybrid Cloud Using OCSF
42mSimplifying Data Analysis with GitHub Codespaces, Jupyter Notebooks & Open AI
21mFine-tune the COE process with generative AI
31mTowards an open operations data community
24mContained Intelligence: Secure Sandboxing for Gen AI Code
30mPrompt to Performance Speeding Up Database Administration with AI and Claude Code
35mDynamic, Safe Ops Using AI to Generate Trusted Operations from Text Prompts