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Introduction

Automated software vulnerability patching using artificial intelligence (AI) is a rapidly evolving field in cybersecurity that aims to improve the efficiency and effectiveness of patch management processes. Vulnerabilities in software systems are a major concern for organizations as they can be exploited by malicious actors to gain unauthorized access to sensitive information or disrupt critical operations. Traditional patching processes often rely on manual identification and remediation of vulnerabilities, which can be time-consuming and error-prone.

AI technologies, such as machine learning and natural language processing, offer promising solutions to automate and optimize the patch management process. By leveraging AI algorithms to analyze security advisories, code repositories, and other sources of information, organizations can quickly identify vulnerabilities and generate patches to address them. This thesis explores the potential of AI in automating software vulnerability patching and evaluates its impact on the security posture of organizations.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Overview of Software Vulnerabilities
2.2 Patch Management Processes
2.3 Artificial Intelligence in Cybersecurity
2.4 Automated Patching Technologies
2.5 Challenges in Automated Patching
2.6 Best Practices in Vulnerability Management
2.7 Case Studies on AI-driven Patching
2.8 Adoption of AI in Patch Management
2.9 Ethical and Legal Implications
2.10 Future Trends in Automated Patching

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Experimental Setup
3.5 Evaluation Metrics
3.6 Ethical Considerations
3.7 Validity and Reliability
3.8 Research Limitations

Chapter Four: Discussion of Findings
4.1 Analysis of Patching Performance
4.2 Comparison of AI-driven vs. Manual Patching
4.3 Impact on Security Posture
4.4 User Feedback and Satisfaction
4.5 Integration with Existing Systems
4.6 Scalability and Robustness
4.7 Cost-effectiveness
4.8 Recommendations for Implementation

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview:

Automated software vulnerability patching using AI is a critical area of research in cybersecurity that aims to enhance the efficiency and effectiveness of patch management processes. This thesis explores the potential of AI technologies in automating the identification and remediation of software vulnerabilities, evaluating their impact on the security posture of organizations. The literature review provides an overview of software vulnerabilities, patch management processes, AI in cybersecurity, automated patching technologies, challenges, best practices, case studies, adoption, and future trends. The research methodology outlines the research design, data collection methods, analysis techniques, experimental setup, evaluation metrics, ethical considerations, and limitations. The discussion of findings analyzes the patching performance, comparison of AI-driven vs. manual patching, impact on security posture, user feedback, integration, scalability, cost-effectiveness, and recommendations. The conclusion summarizes the findings, contributions to knowledge, implications for practice, future research directions, and concludes the thesis on automated software vulnerability patching using AI.

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