By Chris Faraglia, Solutions Architect at TestRail
The pandemic revealed how vital digital transformation is to remain relevant in an increasingly competitive technology landscape. Now, almost five years later, the pressure is still on. As software development teams race toward faster release cycles and continuous improvement, the allure of leveraging AI in software development becomes stronger.
A recent software testing and quality industry survey found that 65% of respondents use AI in their software testing processes. However, AI’s rapid integration into development and QA processes brings heightened risks, especially for teams in regulated industries like healthcare, finance, and transportation.
With recent cyberattacks disrupting operations across these sectors, ensuring AI-assisted development doesn’t impact quality, security, and scalability has never been more critical. AI offers exciting possibilities, but untested or improperly implemented AI-generated code for industries subject to heavily scrutinized compliance standards can introduce vulnerabilities that may result in costly security breaches, service outages, or hefty regulatory penalties. A proactive approach to software testing, tailored for regulated environments, is key to striking the right balance between innovation and compliance.
Here are some ways QA managers can align their processes to maintain quality and security while integrating AI responsibly:
1. Transparency through Detailed Quality Records
Regulated industries require teams to document their testing efforts meticulously to meet compliance mandates. Analysts and engineers will likely encounter and address challenges such as data privacy restrictions, detailed audit histories, disaster recovery planning, high traceability standards, and government oversight when analyzing software quality and testing requirements in regulated environments. Keeping clear and complete quality records throughout the development lifecycle ensures that internal stakeholders and external regulators have complete visibility into software performance and security.
Comprehensive documentation also allows teams to trace the source of errors, track the impact of AI-generated code, and demonstrate due diligence in the event of an audit or security inquiry. A central testing management platform helps orchestrate these records, offering unified oversight across every test and iteration.
2. Innovative Testing Strategies to Protect Sensitive Data
Introducing AI into the development process presents new challenges for safeguarding sensitive information. Although AI is widely adopted, its complexity, data privacy and security concerns, and uncertainty about its benefits impede its broader implementation. Rigorous testing strategies—such as automated tests that simulate various use cases and penetration testing to expose vulnerabilities—are essential. For teams in regulated industries, these strategies must meet specific security standards to protect personal and financial data, prevent unauthorized access, and ensure disaster recovery plans meet operations requirements and recovery time objectives (RTO).
In addition, QA teams must ensure that their testing environments mirror real-world scenarios without compromising the confidentiality of sensitive data, such as medical records or bank account numbers, where production data is highly sensitive. This may require data de-identification and data masking techniques to prevent the disclosure of personally identifiable information during data alteration. Testing platforms that integrate with DevOps pipelines are imperative in maintaining secure workflows, ensuring that data stays protected throughout development and testing cycles.
3. Strategic Processes to Balance Speed, Quality, and Compliance
AI can expedite development, but relying too heavily on it without appropriate human oversight risks introducing defects that can delay timelines and increase security vulnerabilities. Dev and QA managers must foster a culture prioritizing quality over shortcuts, even when facing tight deadlines. A strategic QA approach helps align teams around quality goals, ensuring that development lifecycles move forward efficiently without compromising safety or compliance.
Managers should adopt processes that blend human expertise with automation to successfully implement AI. For example, AI can support code reviews by identifying patterns and suggesting optimizations, but human testers must still verify outcomes to ensure accuracy and adherence to regulations. This is indispensable for companies that must adhere to strict regulatory standards or legal requirements.
The Future of AI-Driven Testing Strategies in Regulated Industries
Regulated industries are constantly evolving, and AI can help teams operate more effectively and efficiently if leveraged appropriately. QA managers can integrate AI responsibly into their workflows by proactively establishing transparent documentation, implementing robust security measures, and balancing speed with quality. The goal is to prevent costly errors and security breaches and create a sustainable, scalable approach to software quality that drives business success.
With these tactics and tools like centralized test management platforms, QA teams can remain agile, compliant, and prepared to tackle the challenges of tomorrow’s development landscape. Despite AI’s complexities, the QA community has an optimistic outlook on its future use to help propel testing and software development forward to become an enabler—not a risk—within regulated industries.
Guest article written by: 
Chris Faraglia is currently a Solution Architect and testing advocate for TestRail. Chris has 15+ years of enterprise software development, integration and testing experience spanning domains of nuclear power generation and healthcare IT. His specific areas of interests include but are not limited to test management/quality assurance within regulated industries, test data management, and automation integrations.