Secure Data Backup Strategies for Machine Learning: Compliance and Risk Mitigation Regulatory requirements (GDPR, HIPAA, etc.)
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V1I1P104Keywords:
AI-driven data engineering, anomaly detection, CHIP claims, healthcare fraud detection, machine learning in insurance, predictive analytics, real-time monitoring, fraud prevention, healthcare data processing, data pipelines, supervised learning, unsupervised learning, explainable AI, blockchain for claims processing, federated learning, HIPAA compliance, claim validation automation, big data analytics, cybersecurity in healthcare, AI- driven risk assessmentAbstract
Data feeds innovation and decision-making; therefore, it is progressively the key tool for machine learning (ML), revolutionizing sectors including retail, finance, and healthcare. organizations find it more difficult to ensure data confidentiality, integrity, and compliance with strict criteria since ML systems depend more on large volumes of data for training, analysis, and predictive modeling than others. All of which ML-driven organizations cope with without strong backup plans run through data loss, regulatory non-compliance, & operational disruptions. Emphasizing their relevance in preserving high availability, ensuring compliance with laws including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), and so on, reducing risks associated with data corruption, breaches, and accidental loss, this paper investigates safe data backup strategies customized for ML environments. We thus go over industry-standard recommended techniques for automatically creating duplicity-based encrypted backup systems. We also stress the need for distributed storage systems, versioning approaches, and incremental backups in optimizing backup efficiency for machine learning activities. Apart from technical issues, the paper shows actual case studies from the healthcare and financial sectors proving how organizations have effectively placed safe backup systems in place to safeguard personal data and guarantee legal environment compliance. Scalability, cost efficiencies, and performance trade-offs for ML training pipelines requiring frequent, significant data backups especially draw our attention as important challenges. Organizations can enhance their ML systems against data loss, hackers, and regulatory difficulties by way of proactive data backups. Apart from protection of priceless data assets, good backup plans provide system resilience, operational continuity, and long-term sustainability. In a digital world expanding since ML use rises to guarantee that data is compliant, accessible, and safe, the demand for a well-organized, safe backup solution becomes critical
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