AI监管下，业绩穿透审计和资质溯源怎么应对？

Core Insights - The article emphasizes the importance of companies adapting to increasingly stringent AI regulations, particularly in the context of performance audits and qualification tracing as critical for survival and development [1] Group 1: Challenges and Strategies - Companies face challenges related to performance audits and qualification tracing due to stricter AI regulations [1] - A system that can identify risks such as false trade contracts and non-compliant contract splitting is essential, along with a dynamic early warning mechanism to mitigate potential risks [3] - Companies are encouraged to establish a data-driven audit system to ensure that every transaction and decision withstands scrutiny under AI [3] Group 2: Implementation Models - Companies can adopt a model that utilizes blockchain technology to verify key documents like qualification certificates and performance proofs, ensuring their authenticity through AI comparison with historical data and industry standards [5] - On a technical level, companies should introduce AI auditing tools for automatic data collection and intelligent risk warnings, while utilizing blockchain to create a trustworthy tracing system [7] - On a regulatory level, companies need to establish AI usage guidelines, define data collection boundaries, and create internal ethical review mechanisms to prevent algorithmic bias [7]