Secure Enterprise Cybersecurity through Generative AI and Cloud- Native Architecture for Threat Intelligence

Abstract

Enterprise cybersecurity has become increasingly complex as organizations migrate workloads, data, and applications to
cloud-native environments while facing sophisticated and rapidly evolving cyber threats. Conventional security mechanisms
based on static rules, signature detection, and manually curated threat intelligence are often insufficient against polymorphic
malware, zero-day vulnerabilities, identity-based attacks, supply-chain compromises, and highly coordinated advanced
persistent threats. Generative artificial intelligence (GenAI) introduces new capabilities for cybersecurity by enabling automated
analysis of heterogeneous security data, natural-language threat investigation, contextual reasoning, synthetic threat-data
generation, security-code analysis, and adaptive incident-response support. At the same time, cloud-native architecture
provides scalable computing, distributed security controls, containerization, microservices, immutable infrastructure, and
continuous integration and deployment capabilities that can support intelligent cybersecurity operations. The integration of
GenAI with cloud-native security architecture can therefore establish a more adaptive and proactive approach to enterprise
threat intelligence. However, this integration also creates new risks, including prompt injection, model manipulation, data
leakage, hallucinated intelligence, adversarial attacks against AI models, excessive automation, and inadequate governance.
This study proposes a research framework for examining how generative AI and cloud-native architecture can be combined
to strengthen enterprise threat intelligence and cybersecurity resilience. The research focuses on architectural integration,
automated threat detection, intelligence correlation, incident response, security governance, and responsible AI practices. It
argues that secure enterprise cybersecurity requires not merely deploying GenAI tools but embedding them within a zero-trust,
cloud-native, human-supervised security ecosystem capable of continuously learning from evolving threats while maintaining
confidentiality, integrity, availability, accountability, and regulatory compliance.