Data Discovery - Data Protection for the Financial Services

A guide to using data discovery in the financial services industry.

Data Discovery – Modern Data Protection for the Financial Services Industry

Data Discovery is something many organizations are already doing. That said, in speaking with many organizations in the financial services space, there is still much improvement to be made. In fact, the nature of existing solutions is exposing these organizations to risk and can be handle much more effectively with modern data discovery solutions.

3rd party data sources, newly combined data sources for analysis, data release for analysts and developers. Current tools are not effective at exposing the risk that is contained in these uses.

The key realization is most of the technology and processes to perform data discovery have been made obsolete by new applications, processes, and uses of data in the organization. This lack agility has created an increasingly dangerous inability to capture the full scope of risk in these data assets.

The Problem

Data discovery and classification technologies have been around for a long time (relatively speaking). Organizations have typically favoured technology that bolts on top of the database environments in use or followed IT suggested options that integrate with or remain consistent with other infrastructure in place.

Despite the significant cost involved, these systems cannot typically extend beyond the environments on which they depend. Vendor-specific solutions often rely on the data to be injected within the corporate data stores to be interrogated (potentially exposing risk before discovery occurs). Additionally, with the growth of SaaS solutions and other software products that are procured outside IT, the ability for these legacy discovery tools becomes limited in the formats, locations, sequences, and languages for review.

The great Achilles heel of these legacy solutions is the static configurations and use of column headers and database structures for meaning. This increases the potential of misidentified sensitive data that may have been incorrectly stored, mislabeled, or that a business process unknowingly exposed data in a comments field. Static configurations do not adjust automatically. In most cases, a breach or unintended release of data is the first call to update configurations.

Scope for Financial Services

As the work of financial services relies on tight security controls and the ability to interoperate with other financial institutions and government regulators, these two requirements are at odds with each other. In the best case, this leads to the protection and discovery of data that incurs great cost and time. At worst, controls are scaled back to allow analysis to be completed, potentially exposing risk to the financial institution and its partners and stakeholders.

Additionally, data discovery requirements have grown to include the need for data quality assessments and data trend analysis for verification of AI/ML algorithms and models.

The Solution

Leveraging light-weight enterprise-class machine learning and artificial intelligence is the key. The adaptability to continually learn data formats, languages, and trends is key to providing deeper insights into the data's contents. These technologies do not rely on static configurations, database structures, and schema. That is the great strength in that they do not operate with a preconfigured view of the data. Data is interrogated raw and unfiltered, providing deep insight and more complete analysis results. Additionally, modern applications like Apption's Datahunter can examine any data source type; files, databases, data lakes, and cloud data locations.

The exchange of data files between parties can be scanned and assessed for threats, quality, and AI/ML readiness even before injecting into the corporate data. Furthermore, on-going analysis of the corporate datastore ensures consistent quality is maintained, providing downstream benefits to data analysis and AI and ML programs.

Lastly, running these solutions in a public or private cloud and on-premise provides flexibility to new processes and requirements. The most significant benefit is not relying on existing technologies but being adaptable to new and unknown future technology additions and the accompanying data. Datasource and database agnostic capabilities ensure the continual adapting of the technology to new data formats and business requirements.

Conclusion

Modern Data Discovery incorporates much more than just discovery and classification. It includes data quality analysis and data trend analysis for operating consistent AI/ML and analytics programs.

Data Discovery today also requires an agile and automated capability to learn and interrogate data for the raw contents; extracting deep insights that expose all the risk and pinpointing solutions to ensure effective risk management and data security.

Check out solutions like Apption's Datahunter to illustrate how modern Data Discovery solves today's and tomorrow's data protection challenges.