arXiv:2607.06364v1 Announce Type: new Abstract: Mapping cloud security controls to technical metrics is currently a manual process. This paper proposes domain adaptation of Sentence Transformer models to automate it. We build a training corpus of 3,499 semantic pairs from five European security standards and a set of technical metrics, then expand it via back-translation and LLM-based paraphrasing to up to 13,996 samples across four scenarios. We fine-tune five architectures and evaluate their performance on two independent tasks: control-to-metric and cross-standard controls association. All
The proliferation of cloud security standards and the rapid advancements in AI, particularly large language models and transformer architectures, converge to make automated compliance a timely problem.
Automating compliance mapping can significantly reduce the operational burden and costs associated with regulatory adherence, accelerating cloud adoption and improving security posture for organizations.
This research transforms a traditionally manual and labor-intensive process in cloud security into an automated, AI-driven workflow, increasing efficiency and accuracy.
- · Cloud Security Providers
- · Organizations with complex regulatory requirements
- · AI/ML model developers
- · Cybersecurity sector
- · Manual security auditors
- · Consulting firms specializing in manual compliance mapping
Security compliance for cloud infrastructure becomes faster and less expensive.
Increased adoption of cloud services due to reduced compliance friction, especially in regulated industries.
The development of 'AI-native' compliance frameworks that are designed to be interpreted and enforced autonomously, further integrating AI into regulatory and governance structures.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL