Observability as Digital Forensic Science

Abstract

Distributed systems generate extensive telemetry, yet many operational investigations remain inconclusive because available records cannot be reliably preserved, correlated, or reproduced. Prior research has already connected observability with forensic readiness through evidence-preservation requirements, adaptive cloud collection, and adaptive observability for security incidents in microservice systems. This paper addresses a narrower gap: the absence of an integrated evidence-quality model for evaluating telemetry after capture and governing its use across reliability, data-quality, audit, security, and compliance investigations. A targeted scoping search produced a 32-item working corpus. The framework was derived through an auditable two-cycle conceptual coding procedure that extracted investigative capabilities, failure modes, controls, and candidate measures, followed by boundary testing to reduce overlap among constructs. The resulting Forensic Observability Framework (FOF) comprises eight dimensions: provenance, temporal coherence, causal correlation, contextual completeness, integrity and preservation, controlled custody, reconstructability and reproducibility, and organizational learning. The framework distinguishes trace continuity from chain of custody and operational evidence from legally admissible evidence. It is illustrated through an anonymized enterprise data-reconciliation case in which a 42-cell evidence matrix exposed compensating defects and reduced a major metric discrepancy from approximately 96% to 1.4%. The contribution is a technology-neutral framework, a reproducible synthesis protocol, a seven-stage investigation lifecycle, and candidate indicators that complement rather than replace forensic-ready software and adaptive-observability approaches.