Network Security Risk Mitigation Model in Generative AI Code Generation: In-depth Analysis of the Application of the ANN-ISM Hybrid Method

Network Security Risk Mitigation Model in Generative AI Code Generation: In-depth Analysis of ANN-ISM Hybrid Method Application

Introduction

Generative Artificial Intelligence (Generative AI) is reshaping the software development process at an unprecedented speed, especially in automatic code generation, greatly enhancing development efficiency. Modern Integrated Development Environments (IDEs) and AI tools can rapidly synthesize code snippets and implement complex functionalities based on user input, significantly accelerating software iteration. However, this increase in efficiency is accompanied by significant cybersecurity risks. When code generation relies on Large Language Models (LLMs), systems are not only prone to introducing traditional programming errors but can also be exposed to more insidious, novel attack vectors brought about by the AI models themselves, such as code injection attacks, insecure template usage, backdoor implantation, and adversarial perturbations against the AI models themselves.

Traditional cybersecurity defense systems are mostly designed for traditional software development paradigms and are difficult to effectively cope with the code security challenges brought by the Generative AI era. Therefore, there is an urgent need to develop a new security framework capable of understanding, predicting, and structuring these complex risks. This paper will focus on a framework called the "Artificial Neural Network (ANN)-Explainable Structure Modeling (ISM) framework," deeply exploring how it utilizes the predictive capabilities of ANN and the structural analysis capabilities of ISM to build a forward-looking, multi-layered code security defense system for enterprises.