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
- Data distribution is boundless The polymorphism and openness of financial services result in pervasive data and applications, making it difficult for traditional boundary models to protect data.
- The shift from quantitative to qualitative data is ongoing Substantial increase in data volume requires banks to sharpen their data mining and analysis capabilities.
- Business is getting technology-driven Business departments need to use data analysis to promote business development, which places higher demands on data sharing and utilization.
- IT forward shiftThe trend of technology-driven is introducing new threat to data security.
Solutions
- DSG systemEstablish and improve the DSG system from the top level. Manage financial industry data through categorization and classification, formulate data security strategies based on balancing business and risks, and implement risk management across the entire data life cycle.
- Human-centric insider threat protection systemBuild a new security architecture which takes insider threat and data as center, and leverage behavior analysis technology to predict insider users’ risky behavior and intention, and take a real-time risk control following the coordinated policy.
Typical Scenarios
Scenario 1: Continuously and dynamically monitor sensitive data
Banks need an automated solution to inspect dark data, label sensitive data, and perceive data asset distribution risks. Based on the banks categorization and classification standards, we can use data scanning capability to pinpoint data storage risks that resides in different data storage targets.
Scenario 2: Digital workspace data security system
Build a multi-dimensional DLP technology and leverage a unified management policy to realize a unified control over data security in a full-scenario workspace.
Scenario 3: Human-centric insider threat protection system
Build a proactive defense system, to visualize user’s behavior and determine user’s risk level, quickly locate risky user and high-risk threat event, predict any threat events that may occur.
Solution Values
- Solve the hard issue of implementation of data security governance Provide data security consulting service, helping banks implement data security governance and effectively take risk control over data in motion.
- Protect the data dynamically Provide protection for data in motion, achieve full visibility, and implement categorization and classification-based control.
- Secure banks’ digital transformation Secure the digital transformation journey of banks and compliant with the laws and regulations, enable a data-driven business development.
Conclusion
Data security solutions for the banking industry need to comprehensively consider technology development and business needs. By building an adaptive security architecture, data security governance system, and insider threat protection system, the challenges brought by digital transformation can be effectively addressed. SkyGuard's solutions combine advanced technology with professional consulting services to help banks achieve the dual goals of data security and business development.