AI-Assisted Recovery Readiness for SaaS Workloads
A Framework for Anomaly Detection and Risk-Based Validation
DOI:
https://doi.org/10.66838/sauc.6510Keywords:
Recovery Readiness, SaaS Resilience, Anomaly Detection, Risk-Based Validation, Zero Trust, Backup Verification, Machine Learning, Incident Response.Abstract
Software-as-a-Service (SaaS) platforms protect the availability of their own infrastructure, but customers remain responsible for the integrity and recoverability of the data and configuration they place in them. Mass deletion, ransomware-like encryption, malicious permission changes and silently failing backup jobs can all leave an organization unable to restore a workload within its recovery objectives, and many such conditions are discovered only when a restore is attempted. This paper proposes the AI-Assisted Recovery Readiness Framework (ARRF), which treats recovery readiness as a continuously measured property of each SaaS workload and not as a state assumed from the existence of a backup.....
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