Generative AI-Driven Security Analytics for Enterprise Data Protection in Sovereign Environments Using Cloud
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Abstract
Generative artificial intelligence (GenAI) is increasingly being integrated into enterprise security analytics to improve the identification, interpretation, and response to cyber threats affecting sensitive organizational data. At the same time, the growing adoption of cloud computing has created new opportunities for scalable security analytics while introducing concerns about data sovereignty, jurisdiction, privacy, regulatory compliance, and dependence on external infrastructure providers. Sovereign cloud environments address these concerns by providing greater control over data location, infrastructure operations, access policies, and applicable legal jurisdictions. The integration of GenAI-driven security analytics with sovereign cloud infrastructure can therefore provide enterprises with advanced capabilities for monitoring data environments while maintaining stronger control over sensitive information. GenAI can analyze security events, identify relationships among seemingly unrelated activities, summarize incidents, support threat investigation, and generate contextual security intelligence. However, the use of GenAI in sovereign environments also introduces challenges involving model security, sensitive-data exposure, hallucinations, access control, explainability, regulatory compliance, and the governance of automated decisions. This study examines the potential of GenAI-driven security analytics for enterprise data protection in sovereign cloud environments. It focuses on the interaction between generative AI, cloud sovereignty, security analytics, data governance, cybersecurity, and organizational readiness. A mixed-methods research design is proposed to evaluate technological effectiveness, security performance, governance maturity, and organizational challenges associated with implementing GenAI-based security analytics while maintaining data sovereignty.
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