永信至诚持续保障AI大模型的原生安全

Core Viewpoint - The emergence of AI models like DeepSeek has raised significant security risks, including sensitive data leaks, necessitating a focus on "native security" to ensure safety from the inception of AI systems [1][2] Group 1: Security Risks and Challenges - AI models face various risks such as network and data security, model security, content security, and supply chain security due to their complex digital asset types and user interactions [1] - The dynamic lifecycle of AI models, characterized by continuous data collection, training, inference, and retraining, introduces new risks that differ from traditional software development processes [1] Group 2: Native Security Concept - The "native security" concept emphasizes the need for security management from the internal architecture, data processing, algorithm training, and workflow of AI models to ensure safety is built from the ground up [1] Group 3: Risk Management Mechanism by Yongxin Zhicheng - Yongxin Zhicheng has developed a risk management mechanism based on the "digital wind tunnel" product system, aimed at achieving native security throughout the entire lifecycle of AI models [2] - This mechanism includes multi-dimensional risk assessments and validations at various stages of AI model development, deployment, and operation to enhance security capabilities [2] Group 4: Health Management Solutions - Yongxin Zhicheng proposes a series of health management solutions, including an asset ledger for AI models, lifecycle testing and evaluation, threat intelligence monitoring, and real-time protection for data privacy [2] - The focus is on ensuring compliance and effective filtering of input and output information to prevent leaks of commercial information, national secrets, personal privacy, and sensitive social values [2] Group 5: Intelligent Agents for Security - The strategy involves equipping each AI model with a native, independent security "intelligent agent" to quickly identify, filter, and effectively block potential security risks [2] - This approach aims to improve the accuracy and efficiency of risk management for AI models, enabling them to better empower business development [2]