AuditEase: Compliance & Cybersecurity Remediation Platform for ISO 27001, CIS Benchmarks, and RBI Guidelines

Abstract

Organizations increasingly operate under stringent security frameworks and sectoral regulations. While ISO 27001 and CIS Benchmarks define best practices for information security and system hardening, financial institutions in India must additionally adhere to Reserve Bank of India (RBI) cy- bersecurity guidelines. In practice, compliance programs remain heavily manual, costly, and error-prone. We present AuditEase, an automated compliance and remediation platform that ingests evidence from logs, configurations, and policy documents; maps evidence to control clauses across ISO 27001, CIS Benchmarks, and RBI guidelines; computes risk scores; and auto-generates remediation playbooks and audit-ready reports. Theplatformalsoemploysmachinelearningtopredictcontrol- level risk and prioritize remediation. We benchmark four can- didate models—Random Forest, Gradient Boosting Machines, Support Vector Machines (SVM), and a Multi-Layer Perceptron neural network—under constraints typical of enterprise com- pliance (5–50 labeled systems, 121 features, 10–30% missing values). Random Forest achieves the best trade-off between accuracy (85.2% ± 3.1), stability, robustness to missing data, training time, and interpretability, and is therefore selected asthe prediction engine. Our modular system—implemented with Python, FastAPI, Node.js/React, and MongoDB, and deployable via Vercel/Render—targets continuous compliance by design.We describe the system architecture, evidence and rules model, the ML-based prediction engine and its comparative evaluation, scoring methodology, and remediation workflow, and we discuss an evaluation protocol including accuracy, coverage, and time- to-audit metrics. AuditEase demonstrates how rule-driven and ML-assisted automation can reduce audit time, raise coverage, and improve readiness for external certification and regulatory review.

Introduction

Cybersecurity compliance is essential for enterprise reliability, yet most organizations still depend on slow, manual, and error-prone audit processes. Standards such as ISO 27001, CIS Benchmarks, and RBI cybersecurity guidelines impose overlapping management, technical, and regulatory requirements. Manually mapping controls, collecting evidence, and tracking remediation often takes months, suffers from inconsistencies, and creates compliance drift between audits.

AuditEase is proposed as an automated, unified compliance platform that integrates ISO 27001, CIS Benchmarks, and RBI guidelines into a single rule-driven and ML-assisted framework. The system automates evidence ingestion, clause mapping, compliance scoring, ML-based risk prediction, and remediation workflows to enable continuous, near real-time audit readiness.

Architecturally, AuditEase uses modular microservices for ingestion, rule evaluation, scoring, ML prediction, and ticket-based remediation, supported by FastAPI, React, and MongoDB. Evidence from logs, configurations, and policy documents is normalized and linked to controls, enabling transparent scoring and domain-level heatmaps.

The platform also incorporates machine learning to predict high-risk controls and assets. Four models—Random Forest, Gradient Boosting, SVM, and a neural network—were benchmarked under small-data constraints. Random Forest performed best, achieving ~85% accuracy, low variance, strong robustness to missing and heterogeneous data, and good interpretability, making it suitable for compliance environments.

Pilot studies show substantial improvements: 85–95% control coverage (vs. 60–75% manually), major reduction in audit time (from months to weeks), and structured remediation playbooks integrated with ticketing tools. Economic modeling indicates significant ROI through reduced audit effort and earlier risk remediation.

The paper also addresses security measures (hashing, RBAC, encrypted storage), threat modeling, and governance alignment with ISO and RBI expectations. Limitations include dependence on machine-readable evidence and limited labeled datasets, while future work aims to incorporate anomaly-based ML, expand frameworks (e.g., PCI DSS, HIPAA), and add blockchain-based evidence transparency.

Overall, AuditEase advances compliance automation by unifying multiple frameworks, coupling rule-based checks with predictive ML, and delivering actionable remediation workflows, thereby significantly reducing audit friction and improving organizational security posture.

Conclusion

AuditEasedemonstratesthatrule-drivenandmachinelearn- ing–assisted automation can unify management, technical,and regulatory controls to deliver continuous compliance. By explicitly modeling controls, evidence, and mappings; com- bining deterministic scoring with a carefully selected Random Forest–based risk prediction model; and coupling findings with remediation playbooks, the platform reduces audit time while improving readiness for ISO 27001, CIS Benchmarks, and RBI oversight. The architecture, methodology, and ML modelcomparisonpresentedhereprovideapracticalblueprint for organizations seeking to modernize their cybersecurity compliance posture.

References

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Copyright

Copyright © 2025 Rohit Bhatt, Deon Menezes, Krish Hadkar, Miten Bhandari, Prof. Yogita Borse, Prof. Midhya Mathew. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.