Designing Scalable Software Automation Frameworks for Cybersecurity Threat Detection and Response
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Cybersecurity threats are rapidly evolving, posing significant challenges to organizations seeking to protect critical digital assets. Traditional security approaches, such as rule-based detection and manual incident response, have proven inadequate in addressing the complexity and scale of modern cyber threats, particularly those involving zero-day vulnerabilities, ransomware, and advanced persistent threats (APTs). In response, scalable software automation frameworks have emerged as a critical solution for real-time threat detection and response.
This paper presents a comprehensive study on designing scalable cybersecurity automation frameworks, integrating artificial intelligence (AI), machine learning (ML), cloud computing, and Security Orchestration, Automation, and Response (SOAR) systems to enhance security resilience. The study examines key architectural principles, including microservices-based security structures, cloud-native deployment models, AI-driven anomaly detection, and automated incident response mechanisms. Furthermore, the paper explores how real-time security monitoring, predictive analytics, and Zero Trust security models contribute to an adaptive cybersecurity defense strategy.
To validate the effectiveness of scalable automation frameworks, the paper presents case studies of Google Chronicle, IBM Security QRadar, and Microsoft Azure Sentinel, analyzing their efficiency in automated threat intelligence, behavioral analytics, and cloud-based security operations. Additionally, we discuss major challenges associated with scalability, performance, AI explainability, and interoperability with legacy security infrastructures.
The proposed framework offers an optimized cybersecurity automation model that enhances detection speed, minimizes false positives, and ensures seamless threat response automation. The findings indicate that integrating AI-enhanced SIEM and SOAR solutions into a cloud-native cybersecurity ecosystem significantly improves cyber threat mitigation, response times, and overall security posture. Future research should focus on advancing federated learning for distributed security intelligence, blockchain for decentralized security enforcement, and explainable AI (XAI) for more transparent cybersecurity decision-making.
This study contributes to the growing body of cybersecurity research by providing a scalable, AI-driven, and cloud-integrated framework for organizations to enhance their security resilience in an increasingly complex threat landscape.
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Copyright (c) 2025 Bhargav Dilipkumar Jaiswal
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