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Governance-Aware Observability Pipeline (GAOP): Embedding Compliance Enforcement and Cryptographic Lineage into Telemetry Data Flows
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 50 |
| Year of Publication: 2025 |
| Authors: Priyanka Kulkarni |
10.5120/ijca2025925867
|
Priyanka Kulkarni . Governance-Aware Observability Pipeline (GAOP): Embedding Compliance Enforcement and Cryptographic Lineage into Telemetry Data Flows. International Journal of Computer Applications. 187, 50 ( Oct 2025), 49-58. DOI=10.5120/ijca2025925867
Abstract
Observability pipelines—systems that collect, process, and route telemetry from distributed applications—are increasingly central to the resilience of cloud-native infrastructures and compliance-intensive domains such as healthcare and finance. Yet these pipelines are fragile: telemetry often contains personally identifiable information (PII), clinical data, or financial identifiers. Misconfigurations, such as AWS CloudTrail log exposures or multi-tenant monitoring dashboard leaks, show how ungoverned telemetry creates regulatory violations and reputational harm. Existing governance solutions, including Apache Atlas, Marquez, and Pachyderm, address metadata or provenance in batch pipelines, while observability frameworks like OpenTelemetry and Fluent Bit emphasize scale and interoperability. None operationalize governance enforcement inline at event velocity. This paper introduces the Governance-Aware Observability Pipeline (GAOP), a framework embedding compliance directly into the telemetry data path. GAOP integrates: A policy enforcement engine translating legal clauses (GDPR, HIPAA, CCPA, PCI-DSS) into machine-verifiable rules. Cryptographic lineage mechanisms providing tamper-evident accountability at streaming throughput. Compliance mapping aligning regulatory obligations with telemetry lifecycle stages. Evaluation across three domains—cloud-native microservices, healthcare telemetry, and financial fraud detection—demonstrates governance coverage exceeding 95% with latency overhead under 12%. Comparative benchmarks against Atlas, Marquez, Pachyderm, and OpenTelemetry highlight GAOP’s novelty: inline enforcement, scalable cryptographic proofs, and domain adaptability. Beyond technical performance, GAOP addresses ethical and regulatory tensions: compliance theater, cross-jurisdictional contradictions, and the balance between diagnostic richness and privacy. By embedding governance as a first-class concern, GAOP reframes observability infrastructures as infrastructures of compliance, accountability, and trust.
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