Cloud-based test automation: Managing data privacy risks in regulatory technology environments

Multicloud architectures are being adopted by organizations in high numbers. This creates the need for robust development operations-driven software and it becomes critical for organizations to follow efficient and reliable approaches when it comes to test automation orchestration within continuous integration and deployment workflows.

However, this can introduce significant data privacy and additional compliance risks. If such risks occur in real production data, they can be concerning. Additionally, complexity increases when managing cross-border test environments and organizations must also ensure compliance with stringent regulatory requirements.

Key privacy risks in cloud-based test automation

Challenges in data residency and sovereignty. With multicloud architectures, data is dispersed across multiple platforms and geographic locations, requiring organizations to navigate data residency and laws in sovereignty.

There are regulations that dictate data storage, location and processing, which can potentially conflict with cloud strategies. For example, data protection measures are mandated in the EU General Data Protection Regulation and any noncompliance can result in penalties and reputational damage for organizations.

In regulatory technology environments, it is important to adhere to specific financial or health care regulations, which further complicates data management.

Data sharing risks and security gaps. Multiple teams are involved in distributed test environments. In addition, third-party vendors also play a role. This increases the risk of unauthorized data access and sharing.

It becomes critical to enforce stringent security controls. Without such measures, sensitive data can be exposed which can lead to potential breaches.

As cloud resources are shared, this can increase risks. These can manifest as vulnerabilities in one tenant's environment, potentially impacting others.

Any security gaps in regulatory technology where institutions rely on automated compliance solutions could lead to regulatory violations.

Best practices for securing test data

Data masking and anonymization. Data masking techniques are helpful in replacing sensitive data with mock or fictional, yet realistic, values. This helps preserve data quality without compromising privacy of data.

Using this approach can ensure that real personal information will not be exposed even if there is any unauthorized access to data.

Synthetic test data generation. Teams can conduct comprehensive testing without exposing real sensitive information. Synthetic data can be generated mimicking real datasets.

Privacy risks associated with real data can be handled using this method. This ensures compliance with data protection standards and regulations.

Cross-border data flow management. It is crucial to establish clear controls and data policies for cross-border data flows. Legal implications of transferring data across regions must be assessed and organizations must implement measures to comply with applicable data protection laws.

Organizations can mitigate risks in compliance by utilizing data localization strategies. Data will remain within specific jurisdictions preventing any noncompliance. International compliance is often required in regulatory technology, making it more essential to ensure data transfers are secure and lawful.

Integrating security controls into the continuous integration and deployment process

Data encryption. An additional layer of security is added when test data is encrypted, ensuring unauthorized access to sensitive information. For organizations to prevent unauthorized decryption, advanced encryption standards and key management practices must be robust.

Access controls and identity management. Strict access controls must be put in place. This ensures only authorized personnel can access test data. By leveraging solutions such as identity and access management, data access becomes much restricted. Aspects such as user permissions are enforced and can help reduce risks of insider threats. Data access becomes much more limited.

Continuous monitoring and auditing. To enable real time detection of security anomalies, it is important to integrate continuous monitoring tools within organizations' pipelines. Test environments and processes need to be regularly audited. This helps identify and proactively remediate any security vulnerabilities thereby maintaining integrity of the testing process.

Privacy-by-design framework in development operations and quality assurance

Bridging the gap between privacy and quality assurance teams. It is important for leaders to encourage collaboration between privacy professionals and quality assurance teams. This fosters a culture where data protection becomes an integral aspect to the development process. Joint training sessions and regular collaboration meetings can improve and enhance alignment on objectives related to privacy.

Embedding privacy requirements into development life cycles. Incorporating privacy considerations from the initial stages of software development can help ensure a more seamless and proactive approach to data protection. This leads to more secure applications and adherence to compliance requirements, thereby promoting the privacy-by-design principle.

Utilizing privacy enhancing technologies. Privacy enhancing technologies such as homomorphic encryption allow data to be processed in a secure manner. Secure multiparty computation also promotes security. Using these technologies, organizations can enable secure testing. This ensures privacy is protected without compromising functionality and quality.

Conclusion

Organizations are continuing to embrace cloud-based approaches to test automation within multicloud environments. In regulatory technology, compliance plays a major role. It is important to adhere to regulatory requirements and maintaining strong data privacy practices is paramount.

When organizations acknowledge the challenges and proactively implement best practices such as the masking of data, synthetic data generation, encryption, fostering collaboration between quality assurance and security teams, they can seamlessly navigate the complexities of testing in distributed environments.

When practices such as privacy by design are embedded within development operations and quality assurance processes, compliance and regulatory requirements are met. This helps build trust with customers and stakeholders, eventually leading to an organization's success.

Harini Shankar is director, technology for the Financial Industry Regulatory Authority.