Products & Services

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    Multi-Dimensional Data Loss Prevention (MDLP)
    MDLP comprehensively covers five major channels: network, endpoints, email, applications, and mobile devices. Regardless of where data assets are stored, used, or transmitted, MDLP can secure the corresponding channels, providing full-spectrum protection for your data assets.
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    Advanced Secure Email Gateway (ASEG)
    Offer comprehensive email security, including anti-spam, anti-malware, malicious link detection, secure email access checks, email DLP, OCR, QR code security checks, email tagging, logging, reverse lookup, and approval features. Analyze and protect outgoing email content to prevent data leaks.
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    Unified Content Security Server (UCSS)
    Centralize the management of all devices with unified security policies. Visualize data risk events and threat reports, supporting large volumes of events/logs, and assist in data security governance, seamlessly integrating with enterprise operations.
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    Advanced Secure Web Gateway (ASWG)
    Detect, prevent, and eliminate various web threats and attacks, effectively safeguarding against advanced persistent threats (APTs). Identify and protect sensitive outbound content to prevent data leaks via web access channels.
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    Mobile Access Gateway (MAG)
    Achieve comprehensive security for enterprise mobile applications in BYOD environments through mobile device monitoring, enterprise mobile app management, data storage security, and network transmission security on mobile devices.
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    Data Security Scanner (DSS)
    Conduct compliance checks on data security for endpoints, databases, shared storage, and cloud storage, identifying risks associated with non-compliant data storage.
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    Unified Content WebService Inspector (UCWI)
    Monitor the content of data transmitted within applications via APIs to detect viruses, sensitive data, or inappropriate content, preventing the transmission of unauthorized data. Apply predefined policies to anonymize or tag data as needed.
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    Extended Detection and Response (XDR)
    Gather threat intelligence from endpoints, networks, mobile devices, cloud environments, email, and applications. Conduct correlation analysis to swiftly and accurately detect and block threats.
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    Data Classification Template
    Provide services to business systems via APIs, using Attribute-Based Access Control (ABAC) to map access control for data and personnel classification. Encrypt, anonymize, or otherwise process data based on predefined policies to ensure protection according to data classification.
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    Cloud Access Security Broker (CASB)
    Enhance cloud application security in terms of visibility, compliance, data security, and protection. Integrate with ASWG product modules to ensure real-time visibility of all application traffic.
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    GatorCloud NG (SkyGuard SASE2.0)
    Built on an identity-driven zero-trust architecture, Gator Cloud NG leverages cloud-native networking and security capabilities to reduce costs and complexity in device procurement, maintenance, and operations.
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    Insider Threat Management (ITM)
    Analyze large volumes of logs using AI algorithms to detect abnormal user behavior, enabling real-time risk control and precise policy management. Dynamically adjust data security strategies for timely protection.
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    Data Security Automated Governance (DSAG)
    Leverage automated workflows to streamline data resource discovery, asset management, and identification template definition, providing a comprehensive, actionable data security governance solution.
  • UCS
  • Cloud Data Security
  • ITM
  • DSAG

Solutions

  • Banking
    Data security solution
  • Internet
    Data security solution
  • Insurance
    Data security solution
  • Automobile
    Data security solution
  • Smart Manufacturing
    Data security solution
  • Securities
    Data security solution
  • Healthcare
    Data security solution
  • Industry Background
    Digital transformation is having a profound impact on the banking industry. With the widespread adoption of financial technology, big data, and cloud computing, more and more banking operations are migrating to the cloud. Driven by the popularity of industry clouds, public clouds, and mobile apps, IoT smart devices are widely used. Data distribution has become pervasive, data storage has become decentralized, and network boundaries are gradually blurring. This fast-flowing data brings about not only opportunities to rapid development of financial business, but also challenges to data security.
    Challenges
    While the rapid growth enabled by digital transformation gives banks unprecedented vitality and development speed, they are also facing external compliance pressures and internal threats.
    Solutions
    In the past, banking security systems relied primarily on IT infrastructure, with a focus on isolation methods for protection. However, during digital transformation, the massive flow and sharing of data have made physical or logical isolation—whether through virtual desktops or encryption—insufficient to protect data security. In fact, such methods can even hinder business development. Banks need to adopt more flexible, risk-based approaches to building their data security systems.
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  • Industry Background
    With the rapid development and widespread application of global Internet technology, data security issues have increasingly become a global concern. Driven by intelligence and the Internet of Things, the value and security of data have been given unprecedented attention. Internet companies face many challenges in data security supervision and protection, including hacker attacks, data leaks and theft, and the secure transmission of sensitive data across borders. At the same time, the development of globalization and the Internet has made data flow more convenient and rapid, but it has also brought more security risks and challenges. Enterprises and institutions need to strengthen their investment and management in data security, establish a comprehensive security management system, and actively adopt emerging technologies to improve their protection capabilities.
    Challenges
    Increasingly stringent data regulations and harsher penalties.
    Complex security scenarios.
    Disorganized storage of business and corporate data on personal devices, making it difficult to manage.
    Growing internal risks.
    Solutions
    Internet companies should plan their data security governance using the DSG model, balancing business needs and risks, while prioritizing and classifying data for protection. By Leveraging the CARTA model, businesses can build adaptive security measures, continuously monitoring the data lifecycle. Identity verification and trust assessments allow for agile updates. Combining UCS and ITM technologies enables the creation of an automated internal threat protection system that monitors user behavior, predicts potential risks, and delivers real-time alerts. This approach ensures a visual, automated, and proactive defense system, securing data integrity.
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  • Industry Background
    The global insurance industry, amid business and technological development, is confronted with challenges in data security regulation and protection. As digital transformation progresses, insurance institutions amass a vast amount of sensitive data, making the security of this data a critical issue. Regulatory bodies worldwide are enhancing data security regulatory policies, strengthening the protection of financial consumers' information, and promoting the construction of industry data security management systems, requiring insurance institutions to strictly adhere to high standards of data processing and privacy protection. The insurance industry, when utilizing new technologies such as cloud computing and artificial intelligence, faces the risks of cyber attacks and data breaches, necessitating the establishment of robust technical protection measures and risk monitoring mechanisms. At the same time, insurance institutions must also address issues such as unclear data ownership and increasing compliance risks, which demand that the industry ensures the security and legal rights and interests of personal and organizational data while promoting the rational development and utilization of data.
    Challenges
    From the perspective of preventing data breaches, the insurance industry faces two major characteristics:
    The vast volume and variety of data.
    The widespread distribution of sensitive information.
    Solutions
    Insurance companies need to implement a comprehensive, AI-driven data security solution that addresses both internal and external threats. As threats and attack methods continuously evolve, traditional security systems, which focus on network infrastructure, are no longer sufficient to address these modern challenges. A successful internal threat protection system must be adaptive and continuously refined. The goal is to establish a security architecture that can perform ongoing behavioral analysis of both users and systems, including reviews, policy execution, scoring, continuous profiling, analysis, and validation.
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  • Industry Background
    The global automotive manufacturing and new energy industry is facing dual challenges of technological innovation and environmental protection, with rapid development in business and technology, especially in the fields of new energy vehicles and intelligent connected vehicles. As digital transformation deepens, the automotive and new energy industry has accumulated a large amount of personal information, intellectual property, trade secrets, as well as vehicle operation and environmental data. These data are crucial for improving service quality, optimizing product design and user experience, and maintaining corporate competitiveness. At the same time, global market data security regulations are becoming increasingly stringent, and companies must ensure the legal and proper handling of data, and take effective measures to protect data from leaks and misuse. In this environment, companies need to focus not only on technological innovation and market competition but also on the challenges of data security and privacy protection.
    Challenges
    Data operations in the automotive industry involve multiple parties, including manufacturers, software providers, dealers, repair services, ride-hailing companies, and insurers. While traditional cars rely on engines and transmissions, smart cars add an additional layer of dependence on information systems and chips. With technological advancements, new challenges arise—not just in manufacturing, but also in sales, operations, and the overall security of data.
    Solutions
    Automotive companies should build proactive, internally-focused data protection systems to defend against evolving threats. This is a long-term project that should start with the most critical data, from the most familiar locations, using the simplest methods. Unlike traditional security products, an internal threat protection system is a continuous process. The ultimate goal is to create an adaptive security architecture that performs ongoing analysis of both users and systems, including audits, policy enforcement, scoring, continuous profiling, analysis, and verification. The key concept here is “continuous,” based on Gartner’s CARTA model.
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  • Industry Background
    Smart manufacturing, as a key direction for the transformation and upgrading of the global manufacturing industry, is driving the production methods towards intelligence and service orientation. Companies, in the context of globalization, need to enhance their core competitiveness through technological innovation and at the same time face challenges in data security supervision and protection, as well as compliance with data security in supply chain. Data, as the core asset of smart manufacturing, is directly related to the operational security and trade secrets of the company. Enterprises must establish and improve data security management systems, implement classified and graded protection of data, and at the same time strengthen the supervision of data security at all stages of the supply chain to ensure the security of data at all stages of collection, storage, use, processing, transmission, provision, and public disclosure. In the wave of smart manufacturing, companies must seize the opportunities of digital transformation and at the same time build a solid defense line for data security.
    Challenges
    Challenges in governing data security.
    The essential path to achieving comprehensive data security.
    Solutions
    For smart manufacturing companies, the data protection process should begin with the most critical data in familiar locations and using simple methods. Implementing an internal threat protection system is a continuous process that requires regular optimization. The ultimate goal is to develop an adaptive security architecture that continuously monitors and analyzes user and system behaviors, incorporating Gartner’s CARTA model for ongoing audits, policy enforcement, scoring, profiling, analysis, and verification.
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  • Industry Background
    The global securities industry is facing the dual challenges of technological innovation and data security regulation. With the application of emerging technologies such as big data, artificial intelligence, and blockchain, the securities business is transforming digitally, expanding its service boundaries, and enhancing decision-making efficiency and trading transparency. However, data security and information protection issues are becoming increasingly prominent. Regulatory authorities in various countries demand that securities firms strengthen data security management and establish robust customer information protection mechanisms to prevent data leaks and unauthorized access. At the same time, the securities industry must also address risks associated with cross-border data flows and information disclosure. There is a continuous need to enhance data and business security regulations, increase the costs of securities violations, protect investor rights, maintain the order of the capital market, and ensure data security and compliance in the context of globalization.
    Challenges
    Securities companies are undergoing digital transformation, with data at the core of this process. The quality of data determines the success of digital transformation, and any failure to secure this data could lead to market instability. For securities firms, data security governance is both a priority and a challenge.
    Solutions
    For securities firms, building a data security protection system should start with the most critical data, in the most familiar locations, using the simplest methods. Unlike traditional security products, implementing an internal threat protection system is a continuous process that cannot be completed all at once. It requires ongoing optimization and fine-tuning. Ultimately, the goal is to create an adaptive security architecture that continuously analyzes user and entity behavior, including audits, policy enforcement, scoring, continuous profiling, analysis, and verification.
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  • Industry Background
    The global healthcare industry is at a critical juncture of digital transformation, with continuous innovation in business models and the widespread application of technological advancements such as artificial intelligence, big data, and cloud computing in disease diagnosis, patient monitoring, and medical management. As the value of medical data becomes increasingly evident, the security and privacy protection of Personally Identifiable Information (PII) and Protected Health Information (PHI) have become a focal point of concern for the industry. Medical institutions must comply with global and regional regulatory requirements to protect the lifecycle security of personal privacy and health data assets and address various insider threats.
    Challenges
    The COVID-19 pandemic in 2020 accelerated the adoption of digital healthcare, with 5G, cloud computing, and AI seeing Widespread deployments in the industry. The growing integration of healthcare and IT has significantly expanded the scale and complexity of medical data. This has made medical data an indispensable resource for healthcare institutions and researchers, drawing considerable attention to data analysis, mining, and application.
    Solutions
    Healthcare organizations must develop a proactive, internal threat protection system to safeguard data—a process that requires ongoing refinement. Starting with the most critical data, in familiar locations, and using straightforward methods, healthcare companies can deploy internal threat protection systems. These systems must create an adaptive security architecture that continuously analyzes user and system behavior, including audits, policy enforcement, scoring, continuous profiling, analysis, and verification. The key concept here is “continuity” as outlined by Gartner’s CARTA model.
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