Standard : Logging is embedded in design and operations

Purpose and Strategic Importance

This standard ensures that logging is a first-class concern throughout system design, development, and operations. By capturing consistent, structured, and actionable log data at every layer—from infrastructure and middleware to application code and user interactions—teams gain the visibility needed to detect, diagnose, and resolve issues rapidly. Embedding logging practices up front reduces firefighting, improves system health, and supports compliance and auditability.

Strategic Impact

Adhering to this standard delivers:

  • Faster Detection & Diagnosis: Consistent, high-quality logs enable automated alerting, root-cause analysis, and rapid recovery.
  • Improved Reliability: Observability powered by comprehensive logging uncovers hidden failure modes and drives proactive remediation.
  • Regulatory Compliance & Security: Detailed audit trails support forensic investigations, data governance, and compliance with standards (e.g., GDPR, PCI).
  • Data-Driven Improvements: Aggregated log metrics inform capacity planning, performance tuning, and feature prioritization.

Risks of Not Having This Standard

  • Blind Spots in Production: Critical failures go unnoticed or are diagnosed too late.
  • Inconsistent Formats: Heterogeneous log schemas impede automation and tooling integration.
  • Slow MTTR: Lack of structured logs prolongs investigation and root-cause analysis.
  • Audit & Compliance Gaps: Incomplete or missing logs can lead to failed audits and compliance violations.
  • Operational Debt: Ad-hoc logging practices become brittle and costly to maintain as systems evolve.

CMMI Maturity Model

  • Level 1 – Initial: Logs are sporadic, free-form, and maintained in silos; teams rely on ad-hoc prints or manual traces.
  • Level 2 – Managed: Basic logging libraries are adopted; logs are centralized but lack consistency or structure.
  • Level 3 – Defined: A standardised schema (e.g. JSON, key/value) and centralised logging platform are mandated; developers follow agreed conventions.
  • Level 4 – Quantitatively Managed: Log volume, coverage, and latency metrics are tracked; automated pipelines parse, index, and alert on key patterns.
  • Level 5 – Optimising: Logs feed machine-learning and anomaly-detection systems; continuous feedback loops refine logging strategy and pre-empt incidents.

Key Measures

  • Structured Coverage: Percentage of services emitting logs conforming to the standard schema.
  • MTTD (Mean Time to Detect): Average time from incident onset to detection via log-driven alerts.
  • Log Latency: Time between event occurrence and log availability in central index.
  • Audit Completeness: Proportion of security- or compliance-relevant events recorded and retained per policy.
  • Alert Precision: Ratio of actionable log-based alerts vs. total alerts generated.