Explainable Machine Intelligence Frameworks for Cloud Security Posture Management in Regulated Enterprise Environments

Abstract

Explainable Machine Intelligence (EMI) is emerging as an important approach for improving Cloud Security Posture Management (CSPM) in regulated enterprise environments where security decisions must be transparent, auditable, and aligned with organizational and regulatory requirements. Modern cloud infrastructures contain dynamic workloads, distributed identities, complex configurations, containerized applications, application programming interfaces, and continuously changing security controls. Traditional CSPM approaches primarily depend on predefined rules, configuration checks, and compliance mappings, which can generate large volumes of alerts without adequately explaining their context, significance, or remediation priorities. Machine intelligence can enhance CSPM through automated anomaly detection, risk identification, configuration analysis, behavioral monitoring, and predictive security assessment. However, opaque machine-learning models can create challenges for auditors, security teams, and regulated organizations when decisions cannot be adequately interpreted or justified. This paper proposes a conceptual explainable machine intelligence framework that integrates machine-learning analytics, cloud telemetry, security posture assessment, regulatory control mapping, explainability mechanisms, and human oversight. The framework emphasizes interpretable risk reasoning, traceable evidence, confidence information, policy alignment, and auditable remediation recommendations. It also considers challenges involving data privacy, model drift, adversarial manipulation, false positives, explainability limitations, and accountability. The proposed framework provides a foundation for developing transparent, adaptive, and governance-oriented CSPM architectures capable of supporting security operations while preserving regulatory accountability and human decision-making.