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Research Paper | Information Technology | United States of America | Volume 12 Issue 9, September 2023 | Popularity: 4.7 / 10
Zero-Day Threat Protection: Advanced Cybersecurity Measures for Cloud-Based Guidewire Implementations
Sateesh Reddy Adavelli
Abstract: The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system unperforming. Key performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance.
Keywords: Cloud Security, Machine Learning, Threat Detection, Behavioral Analytics, Data Encryption
Edition: Volume 12 Issue 9, September 2023
Pages: 2219 - 2231
DOI: https://www.doi.org/10.21275/SR23092085343
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