The Role of Natural Language Processing in Automating Cybersecurity Compliance Audits
Keywords:
Natural Language Processing, Cybersecurity, Compliance Audits, Policy Validation, Automation, Cyber ThreatsAbstract
The increasing complexity of cybersecurity infrastructures and the growing regulatory requirements in the digital space have made compliance audits a time-consuming and resource-intensive task. To address these challenges, Natural Language Processing (NLP) techniques have emerged as promising solutions for automating key aspects of cybersecurity compliance checks, including policy validation and audit reporting. This paper explores the application of NLP in cybersecurity audit automation, focusing on how NLP algorithms can efficiently process large volumes of policy documents, identify non-compliance risks, and generate actionable insights for security professionals. By analyzing case studies and recent research in the field, we discuss the accuracy, efficiency, and limitations of current NLP tools used in cybersecurity compliance. Furthermore, we examine the integration of NLP systems into broader cybersecurity frameworks and provide future research directions for enhancing their effectiveness.
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References
Vangoor, Vinay Kumar Reddy, et al. "Zero Trust Architecture: Implementing Microsegmentation in Enterprise Networks." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 512-538.
Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.
Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.
Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.
Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.
Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.
Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.
Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.
Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.
Godbole, Aditi, Jabin Geevarghese George, and Smita Shandilya. "Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications." arXiv preprint arXiv:2409.18454 (2024).
P. Katari, V. Rama Raju Alluri, A. K. P. Venkata, L. Gudala, and S. Ganesh Reddy, “Quantum-Resistant Cryptography: Practical Implementations for Post-Quantum Security”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 283–307, Dec. 2020
Karunakaran, Arun Rasika. "A Predictive AI-Driven Model for Impact of Demographic Factors in Demand Transfer for Retail Sustainability." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 476-515.
Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
Namperumal, Gunaseelan, Akila Selvaraj, and Deepak Venkatachalam. "Machine Learning Models Trained on Synthetic Transaction Data: Enhancing Anti-Money Laundering (AML) Efforts in the Financial Services Industry." Journal of Artificial Intelligence Research 2.2 (2022): 183-218.
Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Debasish Paul. "AI-Driven Synthetic Data Generation for Financial Product Development: Accelerating Innovation in Banking and Fintech through Realistic Data Simulation." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 261-303.
Pradeep Manivannan, Priya Ranjan Parida, and Chandan Jnana Murthy, “Strategic Implementation and Metrics of Personalization in E-Commerce Platforms: An In-Depth Analysis”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, pp. 59–96, Aug. 2021
Yellepeddi, Sai Manoj, et al. "Federated Learning for Collaborative Threat Intelligence Sharing: A Practical Approach." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 146-167.
Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning," in Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 160-167.
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