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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 13 Issue 2, February 2024 | Rating: 5.7 / 10
Implementing AI - Driven Strategies in DevSecOps for Enhanced Cloud Security
Sarthak Srivastava [5] | Manish Singh [3]
Abstract: In the constantly shifting digital technology arena, the protection of cloud environments has become critical for the preservation of sensitive information and the continuous operation of applications and services. With the growing adoption of cloud computing by organizations, embedding security protocols is essential within the software development life cycle. DevSecOps, an approach that blends security processes smoothly with DevOps practices, serves as a bridge connecting development, operations, and security teams. However, this integration presents unique challenges due to the fluid nature of cloud environments and the increasing complexity of cyber threats, necessitating a forward-thinking and flexible security strategy. Artificial Intelligence (AI) has risen as a significant ally in DevSecOps, introducing sophisticated methods to bolster cloud security. This overview highlights the crucial role of AI-enhanced techniques in refining DevSecOps practices to ensure robust and durable cloud security safeguards. Exploring areas such as threat identification, vulnerability evaluation, and automatic incident management, AI's role in enhancing conventional security strategies becomes apparent. Employing machine learning algorithms and predictive analytics provides organizations the tools to spot irregularities, pinpoint potential hazards, and quickly react to new threats. This discussion illuminates AI's smooth incorporation into the DevSecOps ecosystem, marking it as an essential element of Continuous Integration/Continuous Deployment (CI/CD) processes. Automating security assessments throughout the development cycle enables proactive vulnerability management, embedding strong security practices into the core of applications. Furthermore, AI-enabled communication technologies promote improved cooperation among development and security teams. The immediate exchange of threat insights, along with the adaptive learning from previous incidents, lays the foundation for a nimble and efficient security framework. Through case studies and practical examples, we observe how companies have effectively integrated AI into their DevSecOps strategies, leading to notable enhancements in security stances, faster incident response, and greater defense against cyber threats. As we explore the advantages, considerations, and upcoming directions, this analysis seeks to emphasize the importance of incorporating AI-driven techniques in DevSecOps to achieve secure cloud environments. The path to a secure digital future is paved with the convergence of artificial intelligence and security practices, enabling organizations to remain competitive in a dynamically changing threat landscape.
Keywords: Cloud Security, AI in DevSecOps, Machine Learning Algorithms, Continuous Integration/Deployment (CI/CD), AI-Driven Communication Tools
Edition: Volume 13 Issue 2, February 2024,
Pages: 1281 - 1285