Artificial Intelligence for Adaptive Risk Assessment in Cloud-Based Security Frameworks

With emerging cloud computing, conventional risk assessment techniques based on static rules and manual inspection are no longer effective. This study explores cloud security architecture to enable adaptive risk assessment, enabling real-time threat detection, anomaly identification, and proactive risk mitigation. AI-driven techniques based on security through continuous analysis of network traffic, system logs, and threat intelligence to forecast vulnerabilities and dynamically adjust defenses. AI-driven frameworks leverage supervised and unsupervised learning to identify known and unknown threats, thus improving incident response and security automation. Data privacy, transparency of AI, and adversarial attacks are challenges, and a balance between automation and human intervention is required. Ultimately, AI-driven risk assessment improves the resilience of cloud security, enabling organizations to remain ahead of emerging cyber threats and protect key assets in an increasingly dynamic digital environment.