Research Summary: AspisAI: A Canonical, Machine-Interpretable Governance Framework for Automated Multi-Standard Compliance Monitoring

Organizations in regulated and critical-infrastructure sectors face challenges in managing diverse cybersecurity and privacy compliance obligations due to reliance on manual, spreadsheet-based methods. These methods are costly, inconsistent, and lack traceability. AspisAI proposes a machine-interpretable governance framework designed to automate the monitoring of multi-standard compliance by translating requirements into a canonical control model and evaluating evidence.

Why it matters

The ability to efficiently and consistently manage compliance with multiple, evolving cybersecurity and privacy standards is critical for maintaining operational integrity and avoiding significant financial and reputational penalties. This development offers a potential pathway to reduce the cost and complexity of regulatory adherence, thereby enhancing an organization's risk posture and freeing up resources for strategic initiatives.

Key insights

• Organizations in regulated and critical-infrastructure sectors must comply with multiple, heterogeneous cybersecurity and privacy standards concurrently (e.g., ISO/IEC 27001, NIST CSF 2.0, Cyber Essentials, GDPR).
• Current compliance management practices predominantly involve manual mappings, spreadsheet tracking, and periodic audits.
• Manual compliance methods are characterized by high costs, inconsistency across different standards, and weak traceability.
• AspisAI is introduced as a standard-agnostic, machine-interpretable governance framework designed to automate compliance monitoring.
• The framework translates selected requirements from various standards into a canonical, machine-interpretable control model.
• AspisAI evaluates submitted evidence against condition-based decision rules to assess compliance.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.10881