LLMs in AppSec Workflows: Risks, Benefits, and Guardrails
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P109Keywords:
LLMs, Application Security, AppSec, Secure Development, AI Security, Prompt Injection, Vulnerability Detection, Guardrails, Threat Modeling, DevSecOps, Risk Mitigation, Code Generation, Model Hallucination, CI/CDAbstract
Large Language Models (LLMs) are changing application security by giving developers and security teams creative ways to find, understand, and fix issues with increased speed. Rapidly becoming a vital element in modern AppSec toolkits, LLMs enable auto-generation of safe code snippets, support threat modeling, and expose vulnerabilities in easily accessible language. Their growing usage does, however, raise serious questions about hallucinated responses, poor context management, data leaks, and vulnerability to manipulation by evil impulses. The two sides of the problem the major advantages LLMs provide to application security operations and the real risks they create if improperly controlled are investigated in this article. We investigate how companies may safely include LLMs into their SDLC, including best practices, architectural protections against risk, and regulatory concerns, thereby encouraging innovation.We provide a case study in which a company successfully employed LLMs to improve static code analysis while overcoming challenges like trust limits, model restrictions, and human supervision, therefore helping to contextualize the problem. Readers will ultimately have a sophisticated understanding of including LLMs in AppSec not as panaceas, but rather as powerful tools that, with careful application, may improve security procedures and reduce human effort
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