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SUS04

SUS04-BP01 - Implement a data classification policy

Implementation Guidance

“Implement a data classification policy” creates control points that keep operations aligned with business policy, risk, and compliance obligations. Treat ownership, exception handling, and review cadence as first-class operational mechanisms.

For the question “How do you take advantage of data access and usage patterns to support your sustainability goals?”, define measurable outcomes, assign owners, and review execution regularly. Integrate this practice into delivery and operations processes so improvements persist as workloads and requirements evolve.

Key Steps

  1. Establish policy and control model:

    • Define policies and standards that govern “Implement a data classification policy”
    • Map control ownership and review cadence across teams
    • Set exception handling and approval workflows
  2. Implement controls in delivery and operations:

    • Embed checks into deployment pipelines and operational processes
    • Use audit evidence and tracking to prove control effectiveness
    • Escalate policy violations through predefined response paths
  3. Review, audit, and improve:

    • Measure compliance drift and operational outcomes regularly
    • Resolve control gaps with prioritized remediation actions
    • Update governance artifacts as architecture and risk change

Risk / Impact

Level of risk if not implemented: High

Impact: If this best practice is missing, teams are more likely to experience preventable incidents, delayed recovery, and inconsistent change outcomes. Control gaps and weak visibility can increase customer impact during high-pressure events.

Benefits of implementation:

  • Reduced operational risk through repeatable controls
  • Faster detection and response during incidents
  • Stronger auditability and decision traceability

AWS Services to Consider

Amazon S3

Delivers durable object storage with lifecycle controls for efficient data management.

AWS Glue

Automates data cataloging and ETL workflows for efficient data processing.

Amazon Athena

Queries data in S3 with serverless SQL for analytics and reporting.

Amazon EMR

Runs scalable big data processing frameworks for batch and streaming workloads.