Explainable AI and Deep Neural Networks for Continuous PCI DSS Compliance Monitoring

Publication Date : 18/12/2024

DOI: 10.5281/zenodo.14514153


Author(s) :

Sandeep Belidhe, Phani Monogya Katikireddi, Sandeep Kumar Dasa.


Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)



Abstract :

Ensuring constant PCI DSS compliance is essential but not easy when dealing with PCI DSS-sensitive payment card information. This paper looks into XAI and DNNs to examine their possibilities of implementing and improving PCI DSS compliance check automation. XAI makes a model explain itself, making it easy for compliance officers to address non-compliance when identified by the model. For their part, DNNs can sift through large amounts of security data to look for anomalies, determine the effectiveness of the access control measures, verify the implementation of encryption for data, and monitor the effectiveness of controls of vulnerabilities. Applying these high-level AI methodologies can allow organizations to gain better, even real-time, control over compliance, thus significantly reducing the probabilities of security infringements and enhancing data protection measures in general. Not only does it build up compliance capabilities, but it also provides scalable and preventive solutions in reaction to the emerging threats in the cyber security domain
Author : Sandeep Belidhe, Phani Monogya Katikireddi, Sandeep Kumar Dasa


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