Automated Security Data Analysis for Kubernetes
#Kubernetes #Security #Automation #GPT #DataAnalysis #DevSecOps #AI #PromptEngineering #BigData
This project aims to automate the analysis of large Kubernetes security JSON reports (#Polaris, #Trivy, #Kubescape). The client needed a user-friendly tool to transform raw data into a useful report with key findings, best practices, recommendations, and a maturity assessment. The result was to be presentation-ready for both technical and non-technical audiences.
Tasks and Challenges
• Processing huge volumes of data (#BigData) without exceeding the GPT context.
• Separate #Prompts for each tool to enhance accuracy (#PromptEngineering).
• Balancing costs: the initial level of summarization should be cheaper (#gpt-3.5-turbo), the final one deeper (#o1-mini), to maintain quality (#CostOptimization).
• Hierarchical approach (#HierarchicalAnalysis) and caching (#Caching) to speed up reruns.
Solutions
1. Hierarchical analysis: Data is broken into chunks, summarized by gpt-3.5-turbo, then the summaries of the three tools are combined by o1-mini.
2. Separate prompts: Each tool has its own prompt tailored to the data format (#Polaris, #Trivy, #Kubescape).
3. Caching: Reduces time and cost of reruns.
4. Modularity: The code is divided into modules, simplifying future updates (#Modularity).
Results
• Full automation of analysis (#Automation): Huge files are processed without manual labor.
• Cost optimization (#CostEffective): Initial summaries by gpt-3.5-turbo are cheap, caching is fast.
• Quality final report (#QualityOutput): Thanks to the hybrid approach and prompts, the report is accurate, relevant, and presentation-ready.
Conclusion
This case demonstrates the effectiveness of #AI, #LLM, and #PromptEngineering in #DevSecOps and #DataAnalysis tasks. The combination of different models, hierarchical analysis, modular architecture, and clear documentation made the solution flexible, scalable, and cost-effective.
This project aims to automate the analysis of large Kubernetes security JSON reports (#Polaris, #Trivy, #Kubescape). The client needed a user-friendly tool to transform raw data into a useful report with key findings, best practices, recommendations, and a maturity assessment. The result was to be presentation-ready for both technical and non-technical audiences.
Tasks and Challenges
• Processing huge volumes of data (#BigData) without exceeding the GPT context.
• Separate #Prompts for each tool to enhance accuracy (#PromptEngineering).
• Balancing costs: the initial level of summarization should be cheaper (#gpt-3.5-turbo), the final one deeper (#o1-mini), to maintain quality (#CostOptimization).
• Hierarchical approach (#HierarchicalAnalysis) and caching (#Caching) to speed up reruns.
Solutions
1. Hierarchical analysis: Data is broken into chunks, summarized by gpt-3.5-turbo, then the summaries of the three tools are combined by o1-mini.
2. Separate prompts: Each tool has its own prompt tailored to the data format (#Polaris, #Trivy, #Kubescape).
3. Caching: Reduces time and cost of reruns.
4. Modularity: The code is divided into modules, simplifying future updates (#Modularity).
Results
• Full automation of analysis (#Automation): Huge files are processed without manual labor.
• Cost optimization (#CostEffective): Initial summaries by gpt-3.5-turbo are cheap, caching is fast.
• Quality final report (#QualityOutput): Thanks to the hybrid approach and prompts, the report is accurate, relevant, and presentation-ready.
Conclusion
This case demonstrates the effectiveness of #AI, #LLM, and #PromptEngineering in #DevSecOps and #DataAnalysis tasks. The combination of different models, hierarchical analysis, modular architecture, and clear documentation made the solution flexible, scalable, and cost-effective.
Khmelnitskyi 
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