Secure Software Development Lifecycle: Integrating AI for Continuous Security Assessment
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Abstract
The integration of security into the software development lifecycle (SDLC) is essential for reducing vulnerabilities. This paper presents a framework that incorporates AI and machine learning techniques to conduct continuous security assessments throughout the SDLC. By automating vulnerability scanning, code analysis, and threat modeling, our approach enables developers to identify and address security issues early in the development process. Case studies illustrate how this framework reduces the time and cost associated with security remediation, promoting a culture of security-first development and improving software resilience against cyber threats.
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