Transform cyber threat intelligence operations with multi-agent AI workflows. Automate intelligence collection, corroborate threat signals, apply structured analysis, and produce tailored, high-fidelity intelligence at scale. Key Features Build multi-agent AI pipelines to automate the cyber threat intelligence lifecycle Automate threat intelligence workflows for collection and cross-source corroboration Apply Structured Analytic Techniques to automated threat intelligence workflows Book DescriptionCyber threat intelligence teams face growing volumes of data, evolving adversaries, and increasing demands for timely analysis. Applied AI in Cyber Threat Intelligence is your engineering toolkit for transforming manual intelligence processes into scalable workflows using agentic AI and Python. Designed for threat intelligence analysts, security engineers, and SOC practitioners, this book takes a practical approach to operationalizing threat intelligence with multi-agent AI systems. You will build specialized AI agents that automate the intelligence lifecycle. Using the Google Agent Development Kit (ADK) alongside Machine Learning techniques, you will build automated systems that score intelligence requirements, generate structured collection plans, and corroborate cross-source signals to determine breach fidelity. You will also integrate Structured Analytic Techniques (SATs) into your AI pipelines to mitigate cognitive bias. You will build agents that generate visual argument maps, tailor intelligence products for different audiences, automate secure primary research, forecast threat actor behavior, and strengthen threat hunting operations. By the end of this book, you will be able to develop practical AI-powered cyber threat intelligence workflows that improve scale, consistency, and decision support.What you will learn Build multi-agent AI pipelines for CTI workflows Automate intelligence requirements and collection planning Corroborate cross-source threat signals to sc
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