{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Cyber Sentries: AI Insight to Cloud Security","title":"Security Data Pipelines: How to Cut SIEM Costs and Noise with Dina Kamal","html":"","width":"100%","height":180,"duration":1999,"description":"SIEM Speed Without the Sprawl—DataBahn’s Take on Security Data Pipelines\nIn this Cyber Sentries: AI Insights for Cloud Security episode, host John Richards sits down with Dina Kamal, Chief Revenue Officer at DataBahn, to tackle a familiar cloud security problem: teams can’t get the right data into the SIEM fast enough, and when they do, costs and noise spike. After the introductions, John and Dina dig into why data integration and parsing often consume most of the timeline in SIEM projects—and how a security data pipeline layer can compress onboarding from months to weeks.\nThey also explore what “doing more with less” looks like in a modern SOC: filtering and routing data based on detection value, preserving what’s needed for compliance, and keeping flexibility for SIEM migrations. Dina’s bigger point is that AI only becomes truly useful when it’s paired with domain expertise and real operational context—otherwise it’s easy to end up with impressive-looking outputs that don’t hold up under investigation pressure.\nQuestions We Answer in This Episode\nWhy do SIEM projects stall on data onboarding, and what speeds it up?\nHow can you cut SIEM ingestion costs without weakening detections?\nWhat does owning your security data change during SIEM migrations?\nWhere does AI help most in SOC workflows, and where do guardrails matter?\nKey Takeaways\nData pipelines remove SIEM “plumbing” bottlenecks by automating collection, parsing, and transformation.\nCost reduction works best when you filter by security value, not just by volume.\nDecoupling data collection from the SIEM reduces lock-in and simplifies vendor changes.\nAI is strongest when guided by security context and experienced practitioners.\nThe throughline is practical: better detections and faster investigations start upstream with intentional data handling. By treating the SIEM as a high-value analytics destination instead of a dumping ground, teams can regain capacity, reduce noise, and keep options open as tools and...","thumbnail_url":"https://img.transistorcdn.com/Ipg5CALrzv7pPIJnV_OJHWbm0TRU-H5nEd05XgrZwY8/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lZDg5/MzE1MjJkODgxYmJh/MzE2ZDA1ZjI5YmNj/YTM3OC5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}