Cyber Threat Intelligence for Cloud Systems: A Comprehensive Guide to Becoming an Analyst begins with the foundational study of asset vulnerabilities, laying the groundwork for understanding the weak points within cloud environments. It then introduces tools and methodologies for the automated collection and supervision of data from assets, culminating in the exploration of advanced techniques, such as Artificial Intelligence and Machine Learning for data analysis and automated threat response. Alongside theoretical concepts, the book includes practical examples, hands-on exercises in Python, existing tools, and real-world case studies to help readers solidify their understanding and apply what they’ve learned.

Cybersecurity is one of the fastest-growing fields in today’s technological landscape, and with the increasing complexity and pervasiveness of cyber threats, especially in cloud environments, the need for qualified professionals such as Cyber Threat Analysts has never been more critical. Here, author Gianni D’Angelo provides the technical knowledge required to analyze and defend digital infrastructures, covering both traditional and modern approaches, highlighting how CTI supports decision-making and proactive defense strategies.

• Offers a structured learning path that guides readers from the fundamentals of cybersecurity to the specialized knowledge required for working in Cyber Threat Intelligence (CTI) within cloud environments
• Introduces the foundational principles of CTI, including threat modeling, vulnerability assessment, data collection and correlation, and the lifecycle of threat intelligence
• Presents a mix of theoretical frameworks, practical exercises, and real-world scenarios, providing readers with the essential skills and mindset to identify, analyze, and respond to cyber threats effectively
• Covers advanced techniques involving Artificial Intelligence (AI) and Machine Learning (ML) to automate the detection and analysis of cyber threats
• Demonstrates how to use tools and frameworks to process large volumes of threat data, recognize patterns, and enable rapid, automated responses to security incidents
• Includes hands-on Python exercises and case studies where readers can learn how to apply AI-driven techniques to real-world cloud environments, thus improving detection accuracy and response speed