Real-Time & Intelligence Data Platform on AWS

Lifecycle-governed storage to protect, retain, and reuse AI data as a long-term enterprise asset
Design Intent

This design assumes a centralised data ownership model where AI data is governed as a reusable enterprise asset rather than a disposable by-product. Control is enforced through lifecycle-aware policies and recovery discipline, ensuring data durability and traceability across its lifecycle. Consumption is structured around controlled reuse for retraining and audit rather than unrestricted access. AWS provides the execution context, anchored through Amazon S3 and AWS Backup to maintain storage consistency and protection governance.

Design
Design Walkthrough
  • Centralising primary storage establishes a single control boundary for all AI data, preventing fragmentation and ensuring consistent protection policies across workloads (Amazon S3, Amazon EBS, Amazon FSx).
  • Introducing lifecycle-aware tiering separates hot and cold data paths, preventing unnecessary storage costs while maintaining availability aligned to access patterns (S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA).
  • Enforcing policy-driven backups ensures recovery readiness is standardised rather than team-dependent, reducing risk of data loss across environments (AWS Backup, Backup Vaults).
  • Segregating archival storage from active tiers enables long-term retention without impacting operational performance, while preserving durability for audit and compliance (S3 Glacier tiers).
  • Enabling controlled rehydration ensures archived data remains usable for retraining and audits, preventing data from becoming inaccessible or operationally irrelevant (Rehydration, model reuse flows).
  • Embedding governance and cost controls across all layers ensures security, compliance, and financial discipline are continuously enforced, preventing uncontrolled data growth and policy drift (AWS IAM, AWS KMS, AWS Cost Explorer, Chavans MirAI).
Operational Outcomes
Enables
  • Consistent protection and recovery of AI data across its lifecycle
  • Cost-aligned storage management without loss of data durability
  • Controlled reuse of historical data for model improvement and audit
  • Continuous governance over access, encryption, and compliance
Good fit when
  • AI data volumes are growing without clear retention discipline
  • Backup and recovery processes are inconsistent across teams
  • Long-term data retention is required for audit or regulatory needs
  • Storage costs are increasing due to unmanaged lifecycle policies
This reference architecture reflects patterns we see when enterprises attempt to standardise platforms while still allowing teams to move at different speeds.

A practical way to understand whether our approach fits your operating reality.

© 2026 Chavan. All rights reserved
© 2026 Chavan. All rights reserved