Building Cyber Resilient Enterprise Architectures through Machine Learning Driven API Security and DevSecOps Automation
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Abstract
The rapid digital transformation of enterprises has significantly increased the adoption of cloud computing, microservices, application programming interfaces (APIs), and continuous software delivery pipelines. While these technologies enhance organizational agility and scalability, they also introduce complex cybersecurity challenges that conventional security mechanisms struggle to address. This study explores the role of machine learning-driven API security and DevSecOps automation in building cyber resilient enterprise architectures capable of preventing, detecting, responding to, and recovering from sophisticated cyber threats. Machine learning algorithms facilitate intelligent anomaly detection, behavioral analysis, threat prediction, and automated incident response by continuously learning from evolving attack patterns. Simultaneously, DevSecOps integrates security practices throughout the software development lifecycle, enabling automated vulnerability assessment, secure code analysis, compliance verification, and continuous monitoring without compromising development speed. The integration of these technologies strengthens enterprise resilience by reducing attack surfaces, improving visibility across distributed systems, and enabling proactive risk management. The research adopts a qualitative approach supported by an extensive review of current academic literature, industry frameworks, and cybersecurity best practices. The findings demonstrate that combining artificial intelligence, API security analytics, and DevSecOps automation significantly enhances organizational capability to withstand cyberattacks while maintaining business continuity. The study concludes that intelligent security automation represents a strategic foundation for developing adaptive, resilient, and future-ready enterprise architectures
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