Leveraging AI in Threat Modeling for Enhanced Application Security
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P107Keywords:
Application Security, Threat Modeling, Artificial Intelligence, Machine Learning, Cybersecurity, Risk Assessment, Security Automation, DevSecOps IntegrationAbstract
Application security is fundamental to more contemporary software development, with threat modeling more essential for detecting & mitigating their possible more vulnerabilities prior to exploitation. Historically, threat modeling has mostly depended on their manual procedures and the proficiency of their security experts to anticipate their hazards & develop safe systems. Nevertheless, as applications increase in their complexity and cyber dangers evolve, these manual methods often fail to keep up. This is the juncture at which Artificial Intelligence (AI) begins to transform the environment. AI is becoming a more formidable friend in cybersecurity, providing capabilities to automate & improve their threat detection, pattern identification & also decision-making processes. In the context of threat modeling, AI has significant advantages: it can swiftly evaluate their extensive codebases, simulate possible attack vectors & learn from extensive datasets known by their vulnerabilities and exploits to anticipate unrecognized dangers. This article investigates the integration of AI into the threat modeling lifecycle, analyzing tools, approaches & case studies that illustrate its effects. Actual world examples and more experiments demonstrate enhanced accuracy in threat detection, less human error & also more expedited security analysis timeframes. We examine the approaches used, including NLP for analyzing design documentation, ML for detecting anomalies & also graph-based models for delineating attack surfaces. Although AI-enhanced threat modeling is still developing, its capacity to transform more application security is indisputable. As these technologies advance, they are poised to enhance human knowledge & revolutionize threat modeling from a periodic checklist into a continuous, adaptive process that responds in the actual time. The use of AI into security protocols is expected to enhance the efficiency and efficacy of safeguarding more contemporary applications against latest threats
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