Artificial Intelligence and Machine Learning Algorithms for Advanced Threat Detection and Cybersecurity Risk Mitigation Strategies

This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in advancing threat detection and mitigating cybersecurity risks, while concurrently highlighting their application in public health optimization to enhance healthcare outcomes in underserved communities. The study underscores the dual capability of AI-driven frameworks to address critical challenges across cybersecurity and public health, aligning with sustainable development goals (SDGs).

In cybersecurity, the research identifies AI and ML as pivotal in real-time threat detection, anomaly analysis, and predictive risk mitigation. Key findings demonstrate how advanced algorithms, such as deep learning and reinforcement learning models, can anticipate and neutralize cyber threats with unparalleled precision, minimizing vulnerabilities in digital ecosystems.

Concurrently, the paper examines the adaptation of AI-driven methodologies in public health optimization. By leveraging predictive analytics and resource allocation algorithms, AI frameworks are shown to improve access to healthcare, enhance disease prevention strategies, and optimize patient outcomes in resource-limited settings. The integration of these technologies fosters equity, reduces disparities, and contributes to achieving SDGs related to health and well-being.

The study concludes by emphasizing the interdisciplinary application of AI and ML as a cornerstone for innovation. Recommendations include strategic investments in AI infrastructure, cross-sectoral collaborations, and ethical guidelines to ensure the responsible and sustainable deployment of these technologies. Through this integrated approach, the research establishes a roadmap for leveraging AI and ML to address global challenges, driving progress in both cybersecurity and public health sectors.