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Detection Factory: AI, Code & the Future of Detection Engineering What would detection engineering look like if it were treated like a repeatable software production process instead of a collection of rules living in someone’s head? In this episode of Detection Dispatch, Alex talks with Tejas Paranjape about what a Detection Factory looks like in practice and what changes when detection engineering becomes a repeatable, code-driven, testable, and reviewable production process. Tejas explains how his Detection Factory breaks detection engineering into specialized stages for writing, tuning, reviewing, testing, and shipping detections, while carrying context and feedback throughout the process. 🔍 IN THIS EPISODE: 🎯 What a Detection Factory actually looks like and what each of its four stations does 🎯 Why detections, workflows, and infrastructure need to be represented as code if you want to automate detection engineering 🎯 How quality gates and backtesting can be built into the detection development process before anything reaches production 🎯 Why context and institutional knowledge need to travel with a detection instead of remaining in the head of whoever built it 🎯 What happens when AI gets a detection mostly right but misses something important, such as an alternate log format 🎯 Why different AI models can have different jobs in the detection pipeline instead of asking one model to do everything 🎯 Where human review still matters, particularly when trying to catch the final few percent of detection problems 🎯 What workflows as code could mean for moving from detection to response at machine speed 🎯 The unexpected connection between PKI, identity, and detection engineering, and why understanding what something actually is matters before building detections around it 🎯 The bigger question: What would we have to build around detection engineering to make the process repeatable, testable, reviewable, and trustworthy? 🤖 AI & DETECTION ENGINEERING AI can accelerate detection development, but a detection that is “mostly right” can still fail in production. Missing log formats, overlooked edge cases, incorrect assumptions, and incomplete context can all create gaps. The Detection Factory approach combines AI, specialized models, automated testing, quality gates, backtesting, contextual knowledge, and human review to create a more reliable detection engineering workflow. 🧩 FROM DETECTION TO PRODUCTION Instead of treating detections as isolated rules, the Detection Factory approach treats detection development more like software engineering—with code, testing, feedback loops, quality controls, context, and specialized automation. 🔗 GET STARTED WITH ADEF Explore the Agentic Detection Engineering Framework (ADEF) and read the methodology here: ADEF — https://github.com/Nebulock-Inc/agentic-detection-engineering-framework/blob/main/docs/methodology.md 🎙️ ABOUT DETECTION DISPATCH Detection Dispatch (Alex’s Version) is an independent, community-first detection engineering and threat hunting podcast featuring real-world projects and practitioners pushing the limits of detection engineering, threat hunting, security automation, AI security, and defensive research. 👉 Subscribe for more conversations about detection engineering, threat hunting, AI security, detection as code, security automation, cybersecurity research, and the future of SOC operations. #DetectionEngineering #DetectionFactory #ThreatHunting #Cybersecurity #AIDetection #SecurityAutomation #DetectionAsCode #ThreatDetection #ADEF #SOC

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