Core Viewpoint - The rapid development of generative AI technology presents significant potential for applications in data analysis, intelligent interaction, and efficiency enhancement, while also raising serious security concerns [1] Group 1: Current AI Security Risks - Data privacy risks include insufficient transparency of training data, which may lead to copyright issues, and the potential for AI agents to access user data beyond their permissions [3][4] - The lowering of security attack thresholds allows individuals with minimal technical skills to execute attacks using AI models, complicating the defense against such threats [3][4] - The misuse of generative AI can lead to societal issues such as deepfakes, fake news, and the creation of tools for cyberattacks, which can disrupt social order [3][4] Group 2: Defensive Strategies - The core strategy for preventing data leakage is full lifecycle data protection, covering all stages from collection to destruction, specifically tailored for AI model training and deployment [5][6] - Key measures include scanning training data for sensitive information, conducting supply chain vulnerability assessments, and ongoing risk monitoring during AI agent operation [6][7] Group 3: Challenges and Blind Spots - Supply chain and ecological risks, as well as the rapid development of AI agents, pose significant challenges due to the involvement of multiple participants and the lack of mature governance [7][8] - The need for a credible authentication mechanism is critical to ensure the trustworthiness of AI agents, especially in collaborative environments [7][8] Group 4: Governance and Responsibility - Platform providers play a crucial role in governance, as they have the authority to scan and manage AI agents developed on their platforms, but broader regulatory oversight is also necessary [8][9] - Effective governance requires collaboration between platform providers and regulatory bodies to establish standards and monitoring mechanisms [8][9] Group 5: Future Trends in AI Security - Future AI security development may focus on embedding security capabilities into AI infrastructure, achieving "security by design" [16][18] - Breakthroughs in specific security technologies could help mitigate risks for small and medium enterprises, making AI applications safer [16][18] - Data governance will be essential at both enterprise and societal levels, emphasizing transparency and accountability in AI data usage [16][18] Group 6: Role of Industry Standards - Industry standards are vital for establishing a secure ecosystem, guiding technical practices, and promoting compliance and innovation [18][19] - The development of open standards and assessment tools can lower barriers for small enterprises, enhancing overall security levels across the ecosystem [18][19] - The company has actively participated in the formulation of over 80 domestic and international standards related to AI governance and security risk management [19]
专访蚂蚁集团大模型数据安全总监杨小芳：AI安全与创新发展不是对立的，而是互相成就