垂类数据
Search documents
斗象科技谢忱:十年蝶变 从白帽平台到AI安全云平台
Shang Hai Zheng Quan Bao· 2025-10-09 18:39
Core Insights - The importance of "security" as a foundational element in the AI era is increasingly highlighted, with companies facing challenges related to loss of control over the physical world and the opacity of reasoning processes [2][3] Company Development - The company, founded by Xie Chen in 2014, originated from a technical community focused on cybersecurity, evolving from a platform for vulnerability crowdsourcing to a comprehensive security service provider [3][4] - The "Vulnerability Box" platform, which connects enterprises with white hat hackers, represents a shift from traditional security models that rely on internal teams to a crowdsourced approach [3][4] Business Model and Growth - The platform has successfully gamified the engagement of white hat hackers through various incentive systems, resulting in over 150,000 users and thousands of enterprise clients [4][5] - The company has established itself as a leader in the cybersecurity sector, recognized as an "excellent technical support unit" by the National Information Security Vulnerability Database [4][5] AI Integration and Market Position - The company is focusing on leveraging vertical data as a competitive advantage in the AI era, emphasizing the need for rich security data to build effective AI models [5][6] - The integration of AI into its services has led to significant business growth, with a 55.2% increase in smart manufacturing and enterprise-level business in 2024 [6][7] Industry Leadership and Future Plans - The company aims to establish itself as a leader in AI security, actively participating in industry standards and collaborations, including the establishment of a "Trusted + AI" security laboratory [7] - Recent funding rounds, including over 1 billion yuan in strategic investments, are aimed at enhancing AI security technology and preparing for future capital market activities, including an IPO [7]
人工智能+行动 来了
小熊跑的快· 2025-08-26 14:19
Core Viewpoint - The article emphasizes the implementation of the "Artificial Intelligence +" initiative across six key areas to foster new productive forces and support China's modernization efforts, with specific goals set for 2027, 2030, and 2035 [1]. Group 1: Six Key Areas of "Artificial Intelligence +" - "Artificial Intelligence + Science and Technology": Accelerate scientific discovery processes and drive technological innovation [2]. - "Artificial Intelligence + Industry Development": Cultivate new models and business formats, promoting intelligent development across industrial and agricultural sectors [2]. - "Artificial Intelligence + Consumer Quality": Expand new service consumption scenarios and foster new product consumption formats, promoting intelligent terminal connectivity [2]. - "Artificial Intelligence + Welfare": Create intelligent work methods and more effective learning approaches to enhance quality of life [2]. - "Artificial Intelligence + Governance Capability": Establish a new paradigm of human-machine coexistence in social governance and create a multi-governance framework for safety [2]. - "Artificial Intelligence + Global Cooperation": Promote inclusive sharing of artificial intelligence and build a global governance system for AI [3]. Group 2: Fundamental Support Capabilities - Enhancing foundational model capabilities, innovating data supply, strengthening intelligent computing power coordination, and optimizing the application development environment are essential [4]. - Promoting a thriving open-source ecosystem, strengthening talent development, ensuring policy and regulatory support, and improving safety capabilities are also critical [4]. Group 3: Participation and Opportunities - The "Artificial Intelligence +" initiative allows for broad participation, with opportunities in ecosystems that seamlessly integrate business processes and quickly cultivate user habits to form network effects [5]. - The value of vertical data is significant, especially when high-quality user data continuously feeds back to improve model capabilities, creating a snowball effect [5]. - Talent development is crucial for the success of the initiative [6].