Core Insights - The article highlights the alarming rise of AI-driven cyberattacks, with a report from Anthropic indicating that AI has automated 90% of the hacking process, requiring minimal human intervention [1][3] - The evolution of black and gray market activities is marked by a significant shift towards AI agents, which enhances the efficiency and effectiveness of cybercriminal operations [4][5] Group 1: AI in Cybersecurity - Anthropic's report reveals that AI's capabilities in executing complex attacks have reached unprecedented levels, marking a turning point in cybersecurity [1][3] - The use of AI agents allows for autonomous operations with minimal human oversight, fundamentally changing the nature of digital warfare [4][5] Group 2: Evolution of Black and Gray Markets - The black market has transitioned from mechanical scripts to intelligent agents capable of generating realistic content, significantly lowering the barriers to entry for cybercriminals [5][6] - AI has enabled the mass production of high-quality fake accounts, which can pass Turing tests, thus complicating traditional risk control measures [5][6] Group 3: Defense Mechanisms - To counter the sophisticated AI-driven attacks, defense strategies must evolve to incorporate principles from the physical world and community behavior [9][10] - The "anti-fraud three laws" proposed by industry experts emphasize the importance of diversity, information consistency, and community detection in identifying fraudulent activities [9][10] Group 4: Challenges in AI Models - The introduction of "uncertainty labels" in AI models aims to address the issue of misjudgment caused by ambiguous samples, significantly improving accuracy rates [11][12] - Continuous feedback mechanisms are essential for enhancing the model's ability to recognize ambiguous cases, thereby reducing error rates [13] Group 5: New Paradigms in Risk Control - The traditional "machine review + human review" model is becoming obsolete, leading to the emergence of a new paradigm centered around AI-driven agents [16][17] - This new approach integrates AI machine review, agent-based review, and expert decision-making to enhance the assessment of complex risks [17][18]