Enhancing Model Security in DevOps Pipelines
A Comprehensive Approach to MLOps Security
Keywords:
MLOps, DevOps, model security, AI security, threat detectionAbstract
As organizations increasingly adopt machine learning (ML) in their operational workflows, ensuring the security of ML models within DevOps pipelines has become a critical concern. This paper examines the unique security challenges that arise in the context of MLOps, particularly focusing on vulnerabilities within DevOps pipelines. It discusses various techniques for securing ML models, protecting data integrity, and mitigating vulnerabilities in AI-driven systems. By integrating security practices into the MLOps lifecycle, organizations can enhance the robustness of their AI solutions. The paper also explores frameworks and methodologies that facilitate the implementation of security measures at every stage of the ML lifecycle, emphasizing the need for continuous monitoring and threat detection. Ultimately, the findings suggest that a comprehensive approach to MLOps security is essential for safeguarding sensitive data and ensuring the integrity of machine learning applications in dynamic environments.
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