International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Integrating AI into CVE Identification for Enhancing the SDLC and TLM

Author(s) Abhiram reddy bommareddy
Country United States
Abstract The integration of Artificial Intelligence (AI) into Common Vulnerabilities and Exposures (CVE)
identification represents a significant advancement in cybersecurity practices, particularly within the
Software Development Life Cycle (SDLC) and Threat Lifecycle Management (TLM) frameworks. This
article explores the transformative potential of AI technologies, including machine learning, natural
language processing, and automated code analysis, in revolutionizing vulnerability management
processes. Through a comprehensive analysis of implementation frameworks, quantitative benefits, and
organizational challenges, this article demonstrates how AI-enhanced CVE identification can significantly
improve detection rates, reduce response times, and optimize resource allocation in security operations.
This article examines both technical and organizational considerations, from model accuracy and
integration complexity to adoption barriers and training requirements. This article also addresses
emerging challenges and future directions, providing valuable insights for organizations seeking to
strengthen their security posture through AI-enabled vulnerability management. This article contributes to
the growing body of knowledge on AI applications in cybersecurity and offers practical guidelines for
implementing AI-driven CVE identification systems within existing SDLC and TLM frameworks.
Keywords Keywords: Artificial Intelligence (AI) in Cybersecurity, Common Vulnerabilities and Exposures (CVE), Software Development Life Cycle (SDLC), Threat Lifecycle Management (TLM), Automated Vulnerability Detection.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-20
Cite This Integrating AI into CVE Identification for Enhancing the SDLC and TLM - Abhiram reddy bommareddy - IJFMR Volume 6, Issue 6, November-December 2024.
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com