Core Insights - The article discusses the transformation of data security vendors from "compliance tool providers" to "value-releasing enablers" in response to the increasing data interaction demands and security requirements in the telecom industry [1][2] Industry Pain Points - Operators face a threefold structural contradiction in data security: the imbalance between compliance and efficiency, the conflict between data protection and utilization, and the disconnect between traditional architectures and new threats [2] - Compliance with the Data Security Law and the low-latency requirements of 5G and edge computing create challenges for traditional static protection solutions [2] - Sensitive data, such as user communication records, poses a dilemma of being both a core asset and a key resource for data transactions, leading to the challenge of achieving "usable but invisible" data [2] - Traditional security systems struggle with high false positive rates and slow response times due to AI-driven automated attacks [2] Technological Innovation Directions - Innovative vendors are addressing industry pain points through three main technological paths, shifting from "passive defense" to "active immunity" [3] - AI-native security platforms are being developed to reconstruct threat response logic, enhancing detection rates and operational efficiency significantly [3] - Trusted data spaces are being created to solve circulation security issues, utilizing technologies like privacy computing and blockchain to ensure compliance and data protection [4] - Scenario-based defense solutions are being implemented to address specific business security blind spots [5] Competitive Landscape - The market is divided into three types of players: platform-level vendors, scenario-based service providers, and technology component suppliers [6][8] - Platform-level vendors, like Anheng Information, dominate the market with over 60% share, focusing on comprehensive security solutions for provincial operators [7] - Scenario-based service providers, such as Baowangda, leverage deep industry knowledge and technical expertise to address specific operational needs, capturing 25%-30% of the market [9] - Technology component suppliers focus on providing modular capabilities but have weaker industry adaptation [10] Implementation Challenges and Future Trends - Current challenges include data silos, high computing costs, and supply chain risks, which hinder the scalability of AI security platforms [11] - Future trends indicate a deep integration of AI with business operations, lightweight deployment models, and automated compliance upgrades [11] - The core competitiveness of vendors will shift towards a combination of AI-native capabilities, deep scenario adaptation, and broad ecosystem integration [12]