Author Description

In today’s rapidly evolving digital landscape, traditional security frameworks struggle to keep pace with increasingly sophisticated cyber threats. As cybercrime and espionage surge, the need for innovative, predictive solutions has never been more urgent. Machine learning offers a proactive approach to threat detection, responsive security measures, and access management, supporting global peace and justice in alignment with the United Nations’ Sustainable Development Goal 16 (SDG 16). Security Intelligence and SDG16: Applying Machine Learning for Peaceful and Inclusive Societies presents a timely exploration of how machine learning can enhance security intelligence practices. This edited collection brings together insights from experts worldwide, covering core topics such as advanced threat detection algorithms, predictive risk analytics, and automated access control. Beyond technical applications, the volume delves into the ethical, legal, and social dimensions of AI-driven security, providing a comprehensive framework that aligns security intelligence advancements with the SDG 16 objectives of fostering peaceful, inclusive societies. Addressing the needs of policymakers, cybersecurity professionals, and academic researchers, this collection provides the theoretical knowledge and practical insights essential for building ethical and effective security systems.