Regulated systems need more automation, not less

Caution is crucial in regulated industries, yet excessive caution that keeps organizations reliant on lengthy, manually intensive testing can inadvertently introduce risks they aim to avoid. Modern systems are intricately connected, incorporating legacy infrastructure, cloud services, third-party providers, and significant amounts of sensitive data.

A modification in one component of the environment can produce unexpected effects elsewhere, while overlooking defects in payment workflows, claims processes, or customer onboarding could rapidly transform into operational, financial, or regulatory issues. Consequently, testing must serve a purpose beyond serving as a final quality assurance.

It must instill confidence among organizations to continuously evolve complex systems without undermining the safeguards in place. However, as manual regression cycles grow longer and more challenging to scale, releases stagnate, coverage remains uneven, and teams become increasingly reluctant to undertake necessary changes. While automation is sometimes viewed as a threat to oversight, proper implementation can actually furnish the uniformity, traceability, and evidence that regulated entities require.

"Some may believe that test automation diminishes oversight or complicates compliance," explains Akshay Deole, head of testing at BBD, a custom software solutions provider. "In truth, the opposite is the case." As systems evolve into greater complexity, manually verifying that new changes have not disrupted functioning components becomes increasingly arduous.

Testing teams contend with a growing number of integrations, dependencies, and business rules, typically under tight deadlines to complete regression cycles. Moreover, repetitive testing can introduce inconsistencies, such as differing interpretations of scenarios, uneven evidence collection, or dropping of lower-priority tests due to time constraints.

In environments where compliance is paramount, these discrepancies are critical. Automated regression testing alters the scenario by continuously validating workflows where failures could exert the most significant impact. Examples include payment processing, identity verification, claims validation, customer onboarding, access management, and telecoms billing.

However, this does not imply automating every conceivable test. Over-automating typically yields an expensive, difficult-to-maintain framework that teams struggle to manage. Deole emphasizes that the most effective strategies commence with processes carrying the highest business and regulatory risk. "Complexity does not warrant avoiding automation," asserts Deole.

"It is the reason automation becomes essential." Speed alone is insufficient. Regulated organizations must also possess the capability to demonstrate precisely what was tested, which requirement it pertained to, the resultant outcome, and whether the appropriate controls were adhered to. Incorporating traceability into an automation framework ensures that each execution generates its own evidence.

Test assets are version-controlled, results are documented, and reporting becomes an ongoing aspect of delivery rather than a rushed attempt to reconstruct the record post-execution. This approach offers a more consistent audit trail compared to a collection of manually maintained spreadsheets, screenshots, and unconnected testing artifacts.

It also grants engineering, testing, compliance, and business teams a clearer, unified view of whether the system behaves as anticipated. Automation does not eliminate governance from the process. Critical systems may still necessitate formal sign-offs, segregation of duties, and tightly controlled production releases. What undergoes a change is the caliber of validation preceding those decisions.

Integrating automated tests into delivery pipelines empowers teams to detect defects earlier, when they are generally more manageable and cost-effective to rectify. It also liberates testing professionals from repetitive execution, enabling them to concentrate on aspects where human judgment holds greater significance: exploratory testing, atypical scenarios, emergent risks, and user experience.

Many automation endeavors falter before the implementation of the first test because organizations commence with a tool rather than a clear understanding of what they aim to safeguard. Others attempt to automate excessive amounts too rapidly, disregard test data and environment consistency, or accept unreliable tests until teams lose confidence in the outcomes.

A preferable initiation point involves identifying a limited number of high-risk, high-value workflows and constructing robust regression suites around them. Subsequently, traceable reporting and approval controls can be instituted before testing is gradually incorporated into delivery pipelines and subsequently broadened to additional areas.

Reliable automation is more valuable than sophisticated automation. It should fortify the organization's existing controls and render risks more apparent, rather than introducing another layer of complexity that only specialists can comprehend. The objective is not to release software at a faster pace, irrespective of the cost. Rather, it is to make change safer and more predictable.

For regulated entities, this differentiation holds substantial significance. Automation is no longer merely a means to diminish testing effort. As systems become increasingly interconnected and the ramifications of failure intensify, it is emerging as a critical instrument for preserving control.