AI-driven cloud security is a critical area of research as organizations increasingly rely on cloud computing for storing and processing sensitive data. This paper explores the integration of artificial intelligence (AI) and cloud security, highlighting AI-driven solutions as the future of safeguarding sensitive data in the digital age. AI algorithms and machine learning techniques enable cloud security to adapt and respond to emerging threats in real-time, improving detection, prevention, and response capabilities. The paper discusses various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics, emphasizing their effectiveness in mitigating risks and ensuring compliance with regulatory standards. It also addresses the challenges and ethical considerations associated with AI-driven cloud security, emphasizing the importance of transparency, accountability, and ethical AI principles. The paper reviews various machine learning techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cybersecurity. It discusses the use of deep learning, genetic algorithms, and genetic programming in cybersecurity. The integration of cloud-based machine learning systems for cybersecurity is explored, highlighting the challenges of handling large-scale data and minimizing false alarms in cloud environments. The paper also examines the role of AI in intrusion detection systems (IDS), cybersecurity frameworks, and the prevention of cyber-attacks using AI. It discusses the application of AI in various domains, including IoT-based cyberattack prevention systems, blockchain technology, and the integration of AI in public sector applications. The paper also addresses the challenges of cybersecurity in the context of digital transformation, including the increasing sophistication of cybercriminal tactics and the need for comprehensive security data collection for forensic analysis. It highlights the importance of AI in enhancing cybersecurity measures, particularly in areas such as intrusion detection, malware analysis, and phishing detection. The paper concludes that the convergence of cloud computing and machine learning holds significant potential for strengthening cybersecurity measures across various domains, but also necessitates proactive measures to address evolving cyber threats and vulnerabilities.AI-driven cloud security is a critical area of research as organizations increasingly rely on cloud computing for storing and processing sensitive data. This paper explores the integration of artificial intelligence (AI) and cloud security, highlighting AI-driven solutions as the future of safeguarding sensitive data in the digital age. AI algorithms and machine learning techniques enable cloud security to adapt and respond to emerging threats in real-time, improving detection, prevention, and response capabilities. The paper discusses various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics, emphasizing their effectiveness in mitigating risks and ensuring compliance with regulatory standards. It also addresses the challenges and ethical considerations associated with AI-driven cloud security, emphasizing the importance of transparency, accountability, and ethical AI principles. The paper reviews various machine learning techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cybersecurity. It discusses the use of deep learning, genetic algorithms, and genetic programming in cybersecurity. The integration of cloud-based machine learning systems for cybersecurity is explored, highlighting the challenges of handling large-scale data and minimizing false alarms in cloud environments. The paper also examines the role of AI in intrusion detection systems (IDS), cybersecurity frameworks, and the prevention of cyber-attacks using AI. It discusses the application of AI in various domains, including IoT-based cyberattack prevention systems, blockchain technology, and the integration of AI in public sector applications. The paper also addresses the challenges of cybersecurity in the context of digital transformation, including the increasing sophistication of cybercriminal tactics and the need for comprehensive security data collection for forensic analysis. It highlights the importance of AI in enhancing cybersecurity measures, particularly in areas such as intrusion detection, malware analysis, and phishing detection. The paper concludes that the convergence of cloud computing and machine learning holds significant potential for strengthening cybersecurity measures across various domains, but also necessitates proactive measures to address evolving cyber threats and vulnerabilities.