生成式人工智能与网络安全：经典之作

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The integration of generative AI into enterprises presents both opportunities and risks, necessitating a careful balance between innovation and security [13] - Organizations must navigate a complex regulatory landscape that varies by geography and client base, impacting how generative AI can be utilized [7][8] - A secure generative AI reference architecture is essential for managing risks associated with various models, emphasizing the need for end-to-end risk management [8] Summary by Sections Data Risks - Data risks in generative AI stem from inaccurate information, potential exposure of confidential data, and ethical concerns related to training data [3][14] - Organizations must prioritize data and identity security, infrastructure monitoring, and application delivery to mitigate these risks [4] Regulatory Environment - The regulatory landscape for generative AI is diverse, with positions ranging from prohibition to laissez-faire, requiring enterprises to adapt to legal frameworks [7] - Existing and upcoming regulations will significantly influence the use of AI, necessitating compliance and ethical considerations [8] Security Framework - Establishing a security framework with governance is crucial for the secure adoption of generative AI, including team education and the formation of governance bodies [19] - Different cloud service providers offer varying security scopes for generative AI systems, highlighting the importance of understanding shared responsibilities [15][16] Applications and Infrastructure - Generative AI applications require robust infrastructure, including data collection, preparation, and transformation, with major providers like Google Cloud Platform and Amazon Web Services offering tailored solutions [6][16] - The report emphasizes the need for continuous monitoring of performance, user experience, and outcome quality in generative AI applications [6]