Most backup strategies treat all data equally: everything gets backed up on the same schedule with the same retention period. This wastes storage on rarely accessed data while potentially under-protecting critical, frequently changing datasets.

The Problem

A flat backup policy means your test database gets the same backup frequency as your production customer database. Stale marketing archives consume the same backup storage as active financial records. IT teams lack a systematic way to differentiate backup priority based on actual business value and change frequency.

The AI Approach

An LLM can analyze data access logs, change frequency, business process dependencies, and regulatory requirements to recommend tiered backup policies that match protection levels to actual data importance.

Three-Step Build

Step 1 - Data profiling. Collect metadata about each data source: access frequency, change rate, size, owning team, regulatory classification, and recovery time requirements from business stakeholders.

Step 2 - Tier assignment. Send the profile data to an LLM with your backup infrastructure capabilities and cost constraints. The model assigns each data source to a tier: critical (hourly backup, multi-region), standard (daily backup), archive (weekly backup, cold storage).

Step 3 - Policy generation. Generate backup policies for each tier including schedule, retention period, and recovery testing frequency. Present to IT and business stakeholders for approval.

Where It Breaks

Access frequency does not always correlate with importance - some critical data (disaster recovery configurations, insurance policies) is rarely accessed but essential when needed. Data classification requires business context that technical metadata alone cannot provide.

The Production Path

Validate tier assignments with data owners before implementing. Start by optimizing the most obviously over-protected datasets to reduce costs and reinvest savings in better protection for critical data. Review tier assignments quarterly as business needs change.