Abstract: Integrating Artificial Intelligence (AI) and Machine Learning (ML) into cloud security represents a transformative shift in how organizations approach the protection of their cloud environments. As cloud computing proliferates, the security landscape becomes increasingly complex, necessitating advanced solutions to combat sophisticated threats. This paper studies the impact of AI and ML on cloud security, focusing on their applications in threat detection, anomaly detection, automated response, and risk assessment. AI and ML enhance threat detection by enabling behavior-based analysis and adapting to evolving attack techniques. In contrast, anomaly detection systems leverage these technologies to identify unusual patterns indicative of potential breaches. Automated response mechanisms, driven by AI, expedite the mitigation of security incidents, reducing the reliance on manual intervention.
Additionally, AI and ML facilitate comprehensive risk assessment by analyzing configuration settings, access controls, and historical vulnerabilities. Challenges such as data privacy, false positives, integration complexity, and adversarial attacks must be addressed despite their advantages. This paper provides an overview of current advancements, evaluates the effectiveness of these technologies, and explores future directions for their integration in enhancing cloud security.
Keywords: Artificial Intelligence, Machine learning, Cloud security, Threat Defense, Encryption, Cybersecurity, Advanced Threats
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DOI:
10.17148/IJARCCE.2024.13808
