Security Challenges and Solutions in Cloud-Based Machine Learning Systems for Big Data

The sensitivity and volume of data involved in cloud-based machine learning systems for big data make security a top priority.This paper provides an overview of the security issues that these kinds of systems confront and suggests ways to address them.We investigate the weaknesses in cloud-based machine learning settings related to data availability, integrity, secrecy, and privacy.We also go over the dangers that come from hostile assaults, insider threats, illegal access, and data breaches.We suggest a multi-layered strategy that includes safe data exchange protocols, anomaly detection systems, encryption methods, access control mechanisms, and reliable authentication processes to solve these issues.We also discuss the significance of adhering to legal requirements and implementing security best practices.Through the application of these technologies, enterprises may improve the security posture of their cloud-based machine learning systems, protecting confidential information and guaranteeing the reliability of their analytics procedures.