Since the beginning of 2026, regulatory enforcement related to data security and personal information protection in the banking sector has intensified significantly. According to public reports based on data from Enterprise Early Warning Platform, as of May 26, the People's Bank of China (PBOC), the National Financial Regulatory Administration (NFRA), the State Administration of Foreign Exchange (SAFE), and their local branches have issued 56 penalties to banks involving data security and personal information protection violations, with total fines exceeding RMB 70 million.
The message behind these enforcement actions is clear: data security in the banking industry has evolved from a matter of policy development into a matter of regulatory accountability.
Based on extensive experience from multiple banking data security governance projects, RIKING has identified five common challenges that financial institutions typically encounter when implementing data classification and grading programs.
Banking data assets are often distributed across business applications, databases, files, APIs, reports, inventories, and historical repositories. Some data remains in legacy systems, some is embedded in temporary reports, some flows through interfaces and integrations, while other datasets are maintained independently by different departments.
As a result, organizations often struggle to gain a comprehensive and accurate view of their data assets.
The same set of customer transaction data may be assigned different classification levels by headquarters, branches, business units, IT teams, and data governance departments.
The challenge is not the absence of standards, but rather the inconsistent interpretation of those standards when applied to specific data fields, tables, files, and business scenarios.
Data classification and grading involves multiple stakeholders, including business teams, technology departments, compliance teams, security teams, and operations personnel.
Without a unified workflow and governance mechanism, organizations often face inconsistent standards, unclear responsibilities, repeated reviews, and prolonged implementation cycles.
Many institutions successfully establish classification inventories but fail to operationalize the results.
When classification outcomes are not integrated with security controls such as access management, data masking, encryption, auditing, and outbound data protection, the classification exercise remains a static compliance record rather than a practical governance tool.
Data classification and grading is not a one-time project.
As systems evolve, new data fields are introduced, APIs change, and business processes are adjusted, classification results must be continuously updated. Otherwise, the classification inventory created during the project quickly becomes an outdated snapshot.
To address the challenges associated with banking data classification and grading initiatives, RIKING provides an integrated solution covering policy consulting, data asset discovery, rule management, intelligent identification, workflow management, result validation, and security control integration.
Based on each institution’s existing data foundation, system architecture, and governance maturity, RIKING delivers flexible service models that encompass consulting and planning, platform implementation, and full lifecycle governance, helping financial institutions establish a practical, scalable, and sustainable data security governance framework.
The solution supports the onboarding and management of various data sources, including business systems, databases, data tables, fields, files, APIs, and reports.
Through metadata synchronization, data import capabilities, and interface management, organizations can establish a centralized data asset catalog that serves as the foundation for classification and grading activities.
Regulatory requirements, industry standards, and internal policies are transformed into executable and reusable classification rules.
This approach minimizes inconsistencies in classification decisions across departments and personnel, ensuring a unified assessment methodology.
Leveraging Natural Language Processing (NLP), data labeling frameworks, data characteristic analysis, and classification models, the platform assists organizations in identifying critical data assets such as Personal Financial Information (PFI), sensitive data, and Important Data.
This significantly improves both classification efficiency and result consistency.
The solution supports task management, process monitoring, result review, and reporting throughout the classification lifecycle.
Classification evidence, review comments, and adjustment records are retained to support internal audits, independent reviews, and regulatory inspections.
Classification results are integrated with key security capabilities, including data masking, database encryption, operations management controls, risk monitoring, and compliance assessments.
As a result, data classification levels become a core basis for access control, masking policies, encryption requirements, audit strategies, and outbound data governance.
Through ongoing rule management, periodic reviews, classification updates, and reporting mechanisms, the solution supports continuous maintenance of classification results in response to system upgrades, new data fields, API changes, and business adjustments.
Financial data classification and grading should not be viewed merely as a one-time inventory exercise or a compliance register.
Instead, it should serve as a foundational capability that enables financial institutions to implement personal information protection, identify important data, and enforce security controls throughout the entire data lifecycle.
As regulatory expectations continue to rise and data assets grow in volume and complexity, the value of classification and grading extends far beyond regulatory compliance. It helps institutions gain visibility into data assets, identify data-related risks, align security controls with risk levels, and establish a sustainable governance framework.
Leveraging proven methodologies and platform-driven capabilities, RIKING is committed to helping financial institutions build practical, verifiable, and sustainable data classification and grading systems, transforming classification from a compliance requirement into the foundation of modern data security governance.
To learn more about our solutions and best practices, please contact RIKING to schedule a consultation and solution discussion.
