Managing financial data well is not only about storing it securely. It is about knowing what data exists, where it lives, who has access to it, how long it needs to be retained, and how to demonstrate all of that to a regulator when asked. Financial institutions and the organizations that manage financial data on behalf of clients face regulatory expectations that have grown more specific and more demanding over time, and meeting those expectations requires more than a collection of security tools applied to a storage environment.
Financial data management solutions address this as a systematic practice rather than a collection of individual controls, and the difference between a systematic approach and an ad hoc one becomes most visible during regulatory examinations and in the aftermath of security incidents.
The Data Discovery Problem
One of the most common findings in financial services information security assessments is that organizations do not have an accurate picture of where their sensitive financial data actually lives. Data that was created for a specific purpose gets copied, emailed, downloaded to local devices, and stored in locations that were not designed to hold sensitive information and are not subject to the security controls applied to primary systems.
Data discovery, the process of systematically identifying where sensitive financial data exists across the organization’s systems and storage environments, is the foundation of any effective financial data management approach. Without discovery, the organization cannot apply consistent security controls to the data it is responsible for, because it does not know where all of that data is.
The scope of data discovery in a financial services context extends beyond the primary financial databases and core banking systems where the official record of financial data is maintained. It includes email archives, collaboration platforms, cloud storage environments, and shared drives where working copies of sensitive documents accumulate over time.
Data Classification and Governance
Once sensitive financial data is discovered, it needs to be classified in a way that determines how it should be handled, stored, and eventually disposed of. A classification framework that distinguishes between different categories of financial information- public data, internal use, confidential, and regulated- provides the basis for applying appropriate controls to each category rather than applying the same controls to all data regardless of sensitivity.
Classification that is applied automatically, based on the content of the document rather than requiring manual tagging by the person who creates or stores it, is more reliable at scale than manual classification because it does not depend on individual users making consistent classification decisions across thousands of documents created by hundreds of people.
Governance policies that specify how each classification category must be handled, which users can access it, how it must be stored and transmitted, and how long it must be retained before disposal, translate the classification framework into operational requirements that can be monitored and enforced.
Egnyte’s financial data management solutions bring data discovery, classification, and governance together within a platform designed for the content management and compliance requirements of financial services organizations. The full context for what financial data management solutions provide and how they address the security and compliance requirements of the financial sector is covered in this guide.
Retention, Disposal and Regulatory Evidence
Regulatory frameworks for financial services specify retention periods for different categories of financial records, and those periods vary by record type, jurisdiction, and the regulatory authority whose requirements apply. An organization that retains records longer than required carries unnecessary storage costs and risk exposure from data that no longer needs to exist. An organization that disposes of records before the required retention period has elapsed is in regulatory breach.
Automated retention management, where disposal schedules are applied based on document classification and creation date, reduces the manual effort of compliance with retention requirements and produces a more consistent outcome than a process that depends on individual users deciding when records are no longer needed.
The audit trail that demonstrates records were retained for the required period and disposed of in accordance with the applicable policy is itself a compliance deliverable that needs to be maintained alongside the records it documents.
Sandra Larson is a writer with the personal blog at ElizabethanAuthor and an academic coach for students. Her main sphere of professional interest is the connection between AI and modern study techniques. Sandra believes that digital tools are a way to a better future in the education system.
