Encryption-Aware Data Integrity and Quality Controls in SAP SuccessFactors Integrations Using Machine Learning and Cryptographic Hash Chains for Tamper Detection
DOI:
https://doi.org/10.15680/IJCTECE.2021.0406014Keywords:
HR system integrations, data integrity assurance, encryption aware validation, cryptographic hash chains, tamper detection, machine learning anomaly detection, encrypted data pipelines, data quality governance, secure enterprise integrations, integration telemetry analysis, privacy preserving security controls, chain of custody verification, cloud HR security architecture, auditability and compliance, integrity aware encryption, workforce data protectionAbstract
Enterprise human resource platforms increasingly depend on encrypted, multi hop integration pipelines to exchange highly sensitive workforce data across payroll, identity, benefits, and analytics systems. While encryption effectively protects confidentiality, it also obscures visibility into data integrity and quality, creating conditions where tampering, silent corruption, or transformation errors can propagate without detection. This study argues that confidentiality centric security models are insufficient for enterprise HR integrations and that integrity assurance must be designed explicitly for encrypted data flows. The paper introduces an encryption aware integrity and quality control framework for SAP SuccessFactors integrations that combines cryptographic hash chains with machine learning based anomaly detection to identify tampering and degradation without exposing plaintext content. The framework establishes a verifiable chain of custody across integration hops by binding successive payload hashes while simultaneously analyzing encrypted flow telemetry to detect behavioral deviations that indicate manipulation or systemic failure. Empirical evaluation using representative HR integration scenarios demonstrates that the combined approach detects both deterministic integrity violations and subtle quality degradation patterns that would bypass conventional reconciliation or checksum based controls. Findings indicate that encryption compatible integrity mechanisms significantly enhance trust, auditability, and operational resilience in HR data pipelines while preserving privacy and regulatory compliance. The study contributes a practical architectural model for enterprises seeking to strengthen data trust in cloud based HR ecosystems and offers a foundation for future research on integrity assurance in encrypted enterprise information systems.
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