How is Synthetic Data Used in Regulated Industries?
Regulated industries depend on reliable data to build, test, and improve the systems that support everyday operations. From healthcare patient portals and insurance claims systems to bank payment processing and loan applications, there is a wide range of conditions that developers need to test for. At the same time, the data that these systems use is often highly sensitive and can’t simply be copied freely into development, testing, or training environments.
This is why synthetic data has become an increasingly key component of regulated industries’ software testing. It enables teams to cover more edge cases, reduce exposure to sensitive information, and more. When used effectively, synthetic data provides regulated teams a safer and more scalable way to test the systems that real people rely on.
Why Regulated Industries Need Safer, More Scalable Data
Before we dive into the specifics of synthetic data, it’s important to understand how the landscape of regulated systems has evolved in recent years. Production data is valuable because it reflects real users, real workflows, and real exceptions. However, production data also carries the most risk, even with modern access controls. This presents a challenge:
- If test data is too restricted, testers may not have enough representative information to validate critical workflows.
- If data access is too broad, the organization opens itself to significant privacy, compliance, and security risks.
- If data is created manually, testing becomes too slow, narrow, or incomplete to establish confidence in the final product.
Finding a way to thread the needle between these scenarios is critical for valuable and efficient testing. If organizations fail to achieve that balance, they risk falling behind other vendors that can create a more refined and safer platform.
What is Synthetic Data?
Synthetic data is artificially generated information that is designed to resemble real data. This includes matching the structure, formats, relationships, and statistical patterns of production or real-world data, but doesn’t correspond to actual people or transactions. In software testing, synthetic data is typically used to support specific test scenarios, such as mimicking customer records, patient profiles, financial invoices, and even API payloads.
The value of synthetic data comes from the balance we mentioned above — it can be both realistic and controlled, plus it can be scaled faster than manual data creation. For example, a testing team may need data for users who:
- Qualify for a service
- Do not qualify for a service
- Have incomplete information
- Have conflicting information
- Trigger an exception condition
Synthetic data can be quickly generated to reflect each of those criteria, speeding up functional testing, regression testing, integration testing, and data-driven test automation. For instance, tools like Rapise (supported by Inflectra.ai) help teams generate realistic synthetic data and apply it to automated testing workflows. This results in expanded test coverage without adding unnecessary exposure risk to real production data.
Synthetic Data vs. Anonymized Data
When it comes to protecting sensitive data in testing, teams commonly use anonymization to mask identities or other potentially distinguishing factors. The main difference is that anonymized data still starts as real data, but is altered so the information can’t be linked back to a specific person. Anonymized data might involve removing names, generalizing dates, grouping locations, or adjusting other identifying attributes. Synthetic data is fully generated and artificial, based on patterns or rules learned from real data. This distinction is important because anonymized data can still carry risk if the methods used were weak, incomplete, or reversible.
Why are Regulated Systems Adopting Synthetic Data?
Software teams need a better way to test complex applications without increasing potential exposure to sensitive production data. This isn’t just a technical concern for regulated industries — it’s a major privacy, safety, trust, and regulatory readiness issue. Some of the specific reasons that adoption has grown in recent years include:
- Sensitive production data creates risk: As we discussed above, production data is useful because it reflects how real systems behave, but it also may include personal identifiers, protected health information, financial details, government records, etc. Anonymization can help, but synthetic data reduces risks further while still providing QA teams useful data to test with.
- Realistic data is hard to share and scale: Production data often requires approvals from privacy, security, legal, or compliance teams before it can be used, which significantly slows down testing. Not only can synthetic data be scaled much faster (because it’s generated by AI), but it can be shared more freely because of the mitigated privacy implications.
- Regulated teams need broader coverage: Even small software changes can have a major impact on things like eligibility, approvals, disclosures, and audit trails, so broad test coverage is essential for modern applications. While production may not include enough examples of edge cases (and manually created data is far too slow), synthetic data bolsters coverage with controlled variations.
How Synthetic Data Supports Software Testing in Regulated Industries
Now that we’ve discussed the why, we should dig deeper into the how. Instead of waiting for approved production extracts or manually building new test records, teams can quickly and easily generate artificial data that reflects their genuine data. Regulated systems are rarely simple, so this is a major advantage in flexibility and speed — without sacrificing quality or effectiveness.
Creating Realistic Test Scenarios
Synthetic data helps regulated teams build realistic test scenarios without using sensitive records like real patient data in development, QA, or staging environments. However, the value isn’t just that the data is fake. The true value is that the data is useful, matching the structure of your system, following expected business rules, and reflecting conditions that testers actually need to validate.
Testing Edge Cases That Rarely Appear
Certain defects only surface under unusual or unexpected circumstances, but regulated systems need to be more fortified than your average applications. These edge cases may exist in production data, but they’re usually difficult to find, access, or use freely (due to information sensitivity). With synthetic data, teams can generate specific data conditions on demand, from invalid inputs and incomplete records to expired statuses, unusual combinations, and high-risk scenarios. These edge cases often carry real consequences that can affect compliance, service quality, or user trust in regulated environments.
Expanding Regression Testing
As we’ve covered in another article, regression testing helps confirm that new changes have not broken existing functionality. The same capabilities that improved edge case coverage also make regression testing easier because teams can run the same tests across many controlled data variations. This is useful for catching issues that might only appear for certain users or data combinations.
Supporting Data-Driven Testing
On a similar note, data-driven testing allows teams to run the same test logic across multiple sets of input data (such as different eligibility rules, payment methods, user roles, or claim types). Once again, synthetic data naturally supports this by controlling datasets for each scenario and using that data to drive automated testing. Instead of repetitive test creation for each variation, teams can define a reusable test and pair it with tailored synthetic data that covers the conditions they care about.
Accelerating Test Automation
Synthetic data becomes even more valuable when it’s paired with test automation. Automated tests can run quickly and repeatedly, but they need high-quality data to be useful. In other words, if the data is too limited, automated tests may only confirm the simplest workflows. AI-powered testing tools like Rapise can not only help teams generate synthetic data, but also apply that data across automated testing workflows for unmatched QA efficiency. The combination of faster test preparation, broader coverage, and less reliance on sensitive production data is perfect for regulated industries, especially for scalable validation without slowing delivery.
Which Industries Benefit Most from Synthetic Data Generation?
So far, we’ve talked about regulated industries broadly — but it’s helpful to break that down into specific fields to help understand where and how synthetic data is being used.
- Financial Services: Can validate account, payment, lending, and fraud workflows without relying on customer records. Synthetic data is especially useful for testing high-risk or uncommon scenarios like suspicious transactions, failed payments, loan exceptions, or unusual customer activity.
- Insurance: Complex data relationships across customers, policies, claims, coverage rules, adjusters, and supporting documentation mean that artificial records can help test workflows across many unique customer scenarios. This might include simple claims, multi-party claims, coverage disputes, policy changes, or renewal conditions.
- Aerospace & Defense: Failure can have serious operational or safety-related consequences, so synthetic data may be used to simulate environments, logistics workflows, or secure operational functions. Real aerospace data for these failures is otherwise difficult to gain access to, so generating new data helps.
- Government & Public Sector: These systems often manage citizen services, eligibility programs, permitting, benefits, and more. To avoid using this sensitive personal information, synthetic data enables testing to improve system quality with less privacy exposure.
- Energy & Utilities: From billing and service requests to emergency outages, field operations, and infrastructure workflows, this industry has a wide range of scenarios to QA. Synthetic data makes the simulation of variable demand, regional service conditions, and complex customer data easier to create and manage.
Limitations & Risks of Synthetic Data
Although we’ve focused on the benefits and value of synthetic data, there are some considerations to keep in mind. The primary risk of using synthetic data is overconfidence — meaning not vetting the generation rules, potential gaps, and human oversight.
- Synthetic data can miss important real-world outliers: By definition, synthetic data is designed to reflect the patterns of real data. However, if the real data doesn’t accurately represent your user base (e.g. missing uncommon claims, underrepresenting certain segments, etc.), the artificial data can inherit those flaws.
- Poorly generated data can create false confidence: At the same time, even if the foundational data is strong, the synthetic generation process also needs to reflect these data relationships. If the artificial data is too simple or too clean, tests may pass without proving much, leading to a false sense of confidence.
- Privacy risks still need to be assessed: While this does reduce the reliance on real production data, it doesn’t entirely eliminate risk. When synthetic data is generated from real datasets, the outputs can still reveal sensitive patterns or unintentionally preserve unique details that could identify real people or events.
- Synthetic data should complement (not replace) QA discipline: As we’ve spoken about in other forums, we do not believe that AI will replace people (at least not for the foreseeable future). Synthetic data should be part of a broader quality strategy and should not replace test planning, requirements analysis, exploratory testing, human judgment, etc.
How Rapise & Inflectra.ai Use Synthetic Data for Safe Automated Testing
Synthetic data represents a major way for regulated software teams to improve testing without relying too heavily on sensitive production records. It enables wider coverage and enhanced automation across complex systems at scale. Applications for financial services, aerospace, healthcare, and other fields can all benefit from synthetic data when used thoughtfully.
Rapise has Inflectra.ai embedded in its capabilities to seamlessly bring this approach into the testing workflow. You can generate synthetic test data and apply it directly to cutting-edge automated tests that self-heal for more resilient testing suites.
As with our other Inflectra software, Rapise is compliant with the following global regulations and certifications, so you can rest assured that your data is always protected and secure — including in strictly-regulated industries:
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Inflectra Global Regulations Compliance |
Inflectra ISO/IEC Certifications |
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GDPR (General Data Protection Regulation) HIPAA (Health Insurance Portability and Accountability Act) GAMP (Good Automated Manufacturing Practice) DORA (Digital Operational Resilience Act) NIST (National Institute of Standards and Technology) Center of Excellence FMEA (Failure Mode and Effects Analysis) FDA 21 CFR Part 11 Eudralex Volume 4 Part I & II DO-178C (Airborne Software) |
ISO 26262 ISO 13485 ISO 31000 ISO 20022 ISO 27001:2013 ISO 9001:2015 IEC 62304 (Cybersecurity for Industrial Automation and Control Systems) IEC 62443 (Medical Device Software) |
