What are the application risks and preventive measures of domestic large models in network security?

When domestic large models are applied in cybersecurity scenarios, they mainly face risks such as data security, algorithmic vulnerabilities, and malicious exploitation. These risks need to be prevented through a combination of technical protection, management norms, and compliance review measures. **Application Risks** Data security risks: If training data contains undesen sitized sensitive information (such as user privacy and corporate secrets), it may lead to data leakage or abuse. Algorithmic security risks: Models are vulnerable to adversarial attacks (such as inputting interfering data to cause misjudgments) or may produce biased security decisions due to algorithmic bias. Malicious exploitation risks: Being used to generate realistic phishing content, malicious code, or false information, increasing the concealment of cyberattacks. **Preventive Measures** Data governance: Implement desensitization processing for training data, establish a data classification and grading mechanism, and clarify data usage boundaries. Algorithmic protection: Introduce adversarial training to enhance model robustness, and regularly conduct algorithmic security audits and vulnerability scans. Access control: Strictly restrict model calling permissions and set up manual review procedures for high-risk operations (such as code generation). Compliance supervision: Follow regulations such as the "Interim Measures for the Management of Generative Artificial Intelligence Services" to ensure that model applications comply with data security and cybersecurity requirements. When enterprises deploy large models, they can give priority to building a "data-algorithm-access" three-layer protection system, combined with real-time monitoring tools to dynamically identify and respond to security risks, thereby improving the reliability of large models in cybersecurity applications.