A Cloud-Native Framework for the Petabyte-Scale Purge of Regulated Data: Achieving Compliance and Performance in the Financial Domain

Financial record management at global organizations falls under very stringent, absolutely unyielding retention mandates (for example, SEC Rule 17a-4). And thus arises the problem of compliant data deletion—wiping petabytes of data once its retention period has expired—from legacy systems that were never built with such a purpose in mind. Manual processes are often invoked thereby introducing compliance risk and massive performance bottlenecks. This paper proposes Regulated Data Purge Framework (RDPF), a revolutionary cloud-native multi-stage architecture on AWS meant to fill in for such old systems.The RDPF orchestrates a high-throughput polyglot purge pipeline between Amazon Aurora, DocumentDB (MongoDB), and Amazon S3. The main contribution can be described as a four-stage pipeline that enables compliant purging without any performance loss: identification, extraction, purging and non-repudiable certification. RDPF scalability benchmarking has been evidenced by an ability to identify 4 billion records for destruction within less than 4 hours at 1 billion records per hour. Fully automated regulatory compliance is built into the system through generation and immutable storage of the Attestation of Dispose (AoP) in AWS. This framework was developed using an AI driven BMAD method built on multiple Persona’s. Different viewpoints guided how needs were studied, systems designed, steps planned, and tests written before code. Each piece made by machines got checked carefully by the people behind the work. Only after approval did anything move forward. The process relied on structured feedback loops throughout. Human oversight stayed central at every phase. What emerged combined machine speed with human judgment. Every decision had clear reasoning attached. No output went unexamined. Authors took responsibility for what was produced