AI data preservation has become a critical infrastructure requirement for organizations in Saudi Arabia, as the Kingdom accelerates its digital transformation agenda under Vision 2030. Owais Mohammed, Regional Lead and Sales Director at WD for the Middle East, Africa, Turkey, and Indian Subcontinent, stated that AI investments built without comprehensive data durability strategies carry serious business risk.
Mohammed made the remarks ahead of World Backup Day on March 31, noting that AI adoption is expanding rapidly across sectors including smart cities, finance, healthcare, and government services. Organizations across the Kingdom now deploy AI models for medical diagnosis, customer service, supply chain optimization, and financial modeling.
Training Data as a Strategic Asset
High-quality historical data enables AI models to learn, adapt, and remain relevant over time. Mohammed said that techniques such as retrieval-augmented generation (RAG) can improve accuracy and reduce hallucinations, but effective model improvement requires access to historical baselines, not just fresh data.
He cited fraud detection as a practical example. When new behavioral patterns emerge, models must be retrained. However, without historical reference data, that retraining can inadvertently compromise detection of previously known threat types. As a result, a 2025 dataset is not merely archival — it can serve as the foundation for future model improvements.
AI Data Preservation and Regulatory Compliance
Saudi Arabia’s data governance landscape is increasingly shaped by frameworks including the Personal Data Protection Law (PDPL), which emphasizes data sovereignty, privacy, and accountability. Mohammed stated that many frameworks require AI systems to be explainable, reproducible, and auditable.
Consequently, organizations must retain the data used to input, test, and validate models, and preserve versions over time to reconstruct how decisions were made. Without a comprehensive backup strategy, organizations may be forced to pause or shut down AI systems entirely, resulting in disruption and loss of business value.
Managing Model Drift Without Historical Data
Model drift — the gradual degradation of AI performance as real-world data diverges from training data — affects many production AI systems. Detecting and correcting drift depends on access to historical datasets for comparison and retraining. Mohammed stated that organizations lacking comprehensive historical data backups face a choice between accepting degrading performance or rebuilding models from scratch.
Furthermore, he outlined three governance scenarios that depend directly on data preservation. Bias remediation requires both the corrected training data and proof of what the original dataset contained. Model rollback after a faulty update requires restoring the exact data environment the previous version was built on. Explainability for decisions such as loan rejections requires access to the training data that shaped the model’s logic.
Infrastructure Requirements for AI Resilience
Mohammed said that AI backup infrastructure should support versioning, immutability, scale from terabytes to petabytes, and rapid accessibility for data scientists. He recommended a tiered storage approach: hot storage for active development, warm storage for recent training data archives, and cold storage for long-term historical preservation.
“Leading organizations in Saudi Arabia are not just those with the best models — they are the ones who had the foresight to protect their data. Tomorrow’s competitive advantage may already exist in the proprietary data being collected today.”
Owais Mohammed, Regional Lead & Sales Director, WD – Middle East, Africa, Turkey & Indian Subcontinent
Mohammed added that World Backup Day serves as a timely reminder that resilience, cybersecurity compliance, and future capability all begin with a single decision: prioritizing data protection today. For AI-driven organizations in Saudi Arabia, he stated, this principle has never been more important.
The remarks reflect a broader industry conversation about the economic cost of data loss in AI-dependent enterprises, particularly as Saudi Arabia positions itself as a regional hub for artificial intelligence investment and deployment.
