AI-Assisted Controls Change Management for Cybersecurity in the Cloud
Keywords:Regulatory Compliance, Cybersecurity, Cloud, NLP
AbstractWebscale services dealing with sensitive content are increasingly being deployed in public and hybrid cloud environments. At the same time, the impact of security breaches have also increased manifold averaging at USD 3.86M per data breach. To tackle such increasing risks, regulations and security frameworks are defined that an organization must comply with. Most of these frameworks are published in natural language text that run into hundreds of pages resulting into thousands of requirements and controls. When these frameworks undergo revisions, understanding the changes, and interpreting their impact consumes huge amount of time, effort and resources. In this paper, we propose a change management system that supports SMEs with AI-assisted automation of this extremely manual and time consuming activity. Specifically, we introduce the concept of live crosswalks – a framework that models complex relationships among security and compliance documents along with associated operations to manage the change. It uses natural language processing (NLP) and algorithmic techniques to transform the current document-driven, highly manual process into a data-driven interactive intelligent system. We present the overall design and demonstrate its efficacy over several hundreds of diversified controls through experimental evaluation.
How to Cite
Tupsamudre, H., Kumar, A., Agarwal, V., Gupta, N., & Mondal, S. (2022). AI-Assisted Controls Change Management for Cybersecurity in the Cloud. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12629-12635. https://doi.org/10.1609/aaai.v36i11.21537
IAAI Technical Track on Emerging Applications of AI