Secure SDLC

Secure SDLC refers to software development practices that integrate security controls throughout the entire software development lifecycle, from initial design through deployment and maintenance. Organizations implementing secure SDLC reduce vulnerabilities in AI systems by embedding threat modeling, secure coding standards, and security testing at each phase rather than addressing security as an afterthought. For AI governance, secure SDLC is critical because it ensures AI models and supporting infrastructure are built with security and compliance requirements embedded from the start, minimizing regulatory risk and protecting against adversarial attacks or data compromise.

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ResearchGlobal2026-08-28

llama.cpp Flaws and 56% AI Code Failure Rate Expose SDLC Control Gaps

Researchers identified ten vulnerabilities in llama.cpp, a widely used runtime for self-hosted AI models, including two high-severity server flaws. Separately, Veracode found that AI-generated code passed automated security checks only 56% of the time. Together, the findings expose material weaknesses in how enterprises govern self-hosted model infrastructure and AI-assisted software development.

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