Behavioral Anomaly-Driven Access Governance: Unifying Identity Management and Secure File Transfer Workflows in Hybrid Enterprise Cloud Architectures

Abstract

Introduction: Data breaches cost organizations an average of $4.45 million per incident globally, with healthcare breaches averaging over $10 million. Most of these losses are caused not by external hackers but by insider threats and compromised credentials, because organizations have historically been able to verify who logs in but cannot effectively monitor what authenticated users do with sensitive data once they are inside a system.

Objectives: This study proposes a behavioral anomaly detection layer that integrates with IAM-governed Managed File Transfer pipelines to monitor file transfer sessions continuously and revoke access in real time when suspicious activity is detected. The framework incorporates Natural Language Processing techniques to parse and classify audit log text, building on recent advances in IAM-MFT integration [9] to address the post-authentication monitoring gap.

Methods: A three-layer framework combines Google Cloud IAM for identity governance, IBM Sterling File Gateway for AES-256 encrypted file transfer, and an LSTM-based behavioral module augmented by an NLP audit log parser. The NLP component applies named entity recognition and sequence classification to raw transfer log text to generate structured anomaly features and human-readable incident narratives. Testing covered 2.4 million simulated events across four attack scenarios.

Results: The behavioral layer achieved 94.7% anomaly detection accuracy with a 3.2% false positive rate and mean session revocation latency of 212 milliseconds. The NLP log parser reduced analyst triage time by generating structured incident summaries from unstructured log text. Combined with existing IAM-MFT controls, the full architecture reduced estimated breach-related financial exposure by 31% and demonstrated full compliance with HIPAA, GDPR, and ISO 27001.

Conclusions: Combining continuous behavioral monitoring with NLP-driven audit log analysis closes the post-authentication gap in IAM-governed MFT environments, providing healthcare, financial, government, and law enforcement organizations with a deployable, explainable, and regulation-ready data protection architecture.