Guide to data privacy compliance for financial institutions

Understanding data privacy laws for financial institutions

Financial institutions are tasked with navigating an increasingly complex web of data privacy laws designed to protect consumer information. As our dependence on data in software development continues to balloon, so has the data privacy legislation dictating how that data must be handled. Key regulations include the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the U.S., and other specific financial regulations like the Payment Services Directive (PSD2). These regulations mandate strict guidelines on data handling, processing, and storage to protect consumer data against misuse and breaches. Along with these there are industry standards, as well, to ensure safe data handling, such as the PCI Security Standards.

The goal of all of these laws and standards is simple: minimizing the risk of exposure or leakage of sensitive consumer data. In the world of software development and generative AI for financial institutions, this translates to: preventing sensitive consumer data from leaking into unsecure development environments or generative AI tooling like LLMs.

In this guide, we’ll explore best practices, challenges, and solutions for working with sensitive consumer data to achieve financial compliance in application and AI software development in the financial services industry.

Best practices in data handling for financial sectors

Across the organization, financial institutions must adopt rigorous data security measures to comply with regulatory demands and safeguard sensitive information, in particular as regards financial sector data used in software development and testing.

In the below table, we'll define the key practices to adopt.

All of the above practices can be implemented to advance the security and compliance of financial sector data used in software development and testing.

Data minimization can be achieved by way of database subsetting, to shrink massive production databases down to the minimum amount of data required by engineers to develop and test their code. This practice can often reduce PB-scale databases down to referentially-intact GB datasets, which are also much more manageable for developer environments.

Encryption can be used to safeguard real-world data for use in lower environments. It represents just one of the many approaches to data de-identification discussed below.

Access controls can and should be a feature of the test data management systems used by engineering teams, to ensure that only the appropriate individuals can view unprotected production data prior to its de-identification for use in testing.

Conducting audits should be done at the higher organization level, but also within your data de-identification workflows, as well. Test data management platforms that provide audit trails for actions taken to protect data are critical in this regard.

Key to all of these best practices, when it comes to secure test data, is data de-identification. Techniques such as data masking and synthesis allow for the compliant use of production data without exposing actual sensitive information. When done well, data de-identification can maintain the utility of the data for development and testing purposes while ensuring compliance with data protection regulations.

By integrating these best practices, financial institutions can enhance their data handling protocols to ensure robust security and regulatory compliance, effectively reducing the risks associated with data breaches and unauthorized access.

Common compliance challenges and how to overcome them

Financial institutions often face several challenges in maintaining data privacy compliance:

• Integration with legacy systems: Many financial services still rely on outdated systems that are not designed for modern data security standards. As their engineering teams move to cloud-forward services and infrastructure, they should seek out test data management solutions that can bridge the gap by integrating natively with both their legacy systems and their cloud-based technology.
• Complex regulatory environment: Navigating multiple regulations can be daunting. Institutions should invest in training and development to keep their teams informed of the latest requirements, while also implementing techstack solutions that aim for the broadest coverage in terms of compliance with the most stringent legislation.
• Balancing security with innovation: As financial institutions innovate, they must look out for novel risks presented by new technologies, especially in the generative AI space. Approaches to counteract these risks must be implemented hand-in-hand with innovation. In the LLM and RAG space, these approaches can include secure data redaction of unstructured free-text data for use in model training, and RAG system performance monitoring to validate the efficacy and security of your retrieval augmented generation (RAG) applications.
• Secure data management: In the past, secure data management was often synonymous with locking down access to data and leaving engineering teams to create their own datasets from scratch. Thanks to modern test data platforms, this no longer needs to be the case. Leveraging synthetic data and data de-identification techniques allows institutions to securely share data for various use cases, including software testing, LLM model training, and more, both internally and with external partners. By creating non-sensitive replicas of real datasets, these practices ensure that data can be used freely without exposing actual sensitive information, thus maintaining compliance with data privacy laws while facilitating collaboration and innovation.

While there are challenges in achieving data privacy compliance for financial sector engineering teams, modern solutions exist that provide robust strategies for equipping developers with the realistic data they need, while also handling sensitive data securely. By effectively addressing the challenges with versatile technology built for today’s data ecosystems, teams can safeguard their operations and customer data against potential threats, without slowing down their engineering velocity.

How Tonic's data transformation platforms ensures compliance

Tonic.ai offers a suite of platforms built for engineering teams to transform their production data for safe and effective use in testing and development. These enterprise-ready solutions provide data redaction, masking, and synthesis of structured, semi-structured, and unstructured free-text data to fuel lower environments and AI model training with secure, realistic data that complies with the financial sector’s regulations. What’s more, they integrate natively with legacy technology like Oracle and IBM Db2, as well as cloud-forward solutions like Snowflake and Databricks, helping financial institutions to bridge the gap on their journey toward digital transformation.

Here is a quick overview of each product and how it supports compliance:

• Tonic Structural: Built for structured and semi-structured data, Structural is a test data management platform that creates high-fidelity test data that mimics actual production data without containing sensitive information. It also includes an industry-leading subsetter for data minimization.
• Tonic Ephemeral: Pairing well with Structural, this platform allows for the efficient creation of temporary, on-demand data environments for developers that help reduce costs and minimize data exposure risks.
• Tonic Textual: For institutions dealing with unstructured data, Tonic Textual ensures that sensitive information is redacted, synthesized and transformed into formats suitable for machine learning and AI development, aligning with the most recent developments in data protection laws and recommendations.
• Tonic Validate: This platform offers real-time performance monitoring for retrieval augmented generation (RAG) systems, ensuring that they operate within regulatory frameworks specific to AI.

Synthetic data generation is an effective strategy for compliance, enabling the creation of realistic yet non-sensitive datasets based on actual production data. This approach supports complex testing and development environments without compromising data privacy. By using synthetic data, financial institutions can ensure that data handling practices in their software development workflows comply with stringent regulatory standards, enhancing their overall data security posture and minimizing the risk of data breaches.

Case studies: Financial institutions leveraging Tonic

A steadily growing number of financial institutions leverage Tonic.ai's solutions to improve developer productivity and enhance their compliance and security measures. Examples include a major US bank that relies on Tonic Structural to create a secure environment for application development, significantly reducing the risk of data breaches. Enterprise fintech company and global payments platform Flywire accelerated the time it takes them to spin up test data from weeks down to under 30 minutes, all while ensuring regulatory compliance. And credit provider Paytient, meanwhile, achieved a 3.7x ROI with Tonic Structural by saving its developers hundreds of hours they once spent on test data management, allowing them to focus on their product and get to market faster.

Future of data privacy in the financial industry

The future of data privacy in finance is likely to see increased regulation and higher standards for data protection. Financial institutions must continue to evolve their data handling practices and leverage industry-leading data platforms like those offered by Tonic.ai to achieve compliance and protect their customers' data. These platforms not only enhance your overall data security posture, they are built to accelerate your engineering velocity. By implementing best practices in test data management and data preparation for AI, financial institutions can navigate the complexities of data privacy with confidence and enable their developers to efficiently pave the path toward digital transformation.