AI-Driven Schema Drift Detection: Automating Regulatory Compliance in Cloud Migration Projects

This dissertation dives into how AI-based methods for noticing shifts in data structures perform when automating regulatory compliance in cloud migration projects. The focus is squarely on healthcare—where keeping data trustworthy is key—and it turns out that as schemas change, you really need a detailed picture of what's been altered alongside the regulatory rules at play. In most cases, this means juggling a lot of information about current data setups and the knock-on effects of modifications on compliance status during the messy process of migration.A mixed-methods approach was put to work here, and, generally speaking, the outcomes show that these AI techniques can really sharpen the detection of schema drift, speeding up response times and making compliance reporting more accurate. For instance, test migrations, by and large, experienced about a 30% dip in compliance related errors when AI stepped in—pretty impressive, right? You might say that as healthcare groups keep moving their systems to the cloud, leaning into these AI-driven strategies not only cuts down on the risks tied to unexpected data shifts but also builds a kind of forward-thinking, proactive compliance vibe. This shift, in turn, helps boost patient safety and reinforces the reliability of the data. Stepping back, this research adds a valuable piece to the broader conversation on digital transformation in healthcare, suggesting that mixing advanced technology with everyday practices can lead to substantial operational gains, even when regulations become complicated. Ultimately, policymakers and healthcare leaders could find this study to be a handy resource as they try to figure out how to use AI to simplify compliance processes and keep data intact during those critical moves to cloud environments.