Why Compliance Has Become an Engineering Problem

Written by Amar Joshi

Managing regulatory compliance has never been a linear process. As global regulations evolve in response to technological advancements, global economic shifts, and increasing awareness of societal issues, so too has the approach to governing compliance. One consistent aspect, however, is the rising cost of addressing problems when they arise. According to the public policy research organization Cato Institute, the average U.S. firm spends between 1.3% and 3.3% of its total wage bill on regulatory compliance.

Although compliance complications require ongoing legal counsel, there is growing evidence to suggest that these concerns should not be viewed solely as legal matters. Increasingly, compliance issues involve complex engineering challenges that demand comprehensive, systems-based solutions. For example, when government regulators investigate a company’s data privacy and security practices, a seemingly simple request to preserve chat messages can trigger an operational crisis involving multiple siloed departments and risking multi-million-dollar sanctions if not executed flawlessly. The expanding use of information, coupled with the complexity of global data regulations, has rendered traditional manual compliance processes obsolete.

As a result, many corporate legal departments face a disconnect between

recognizing the severity of compliance problems and identifying how to resolve them

among the interconnected stakeholders, often referred to as a “crisis of scale.” In an era of conflicting global regulations and vast data volumes generated by short-lived applications, treating compliance as a software problem has become essential for achieving defensible, scalable, and cost-effective results. When integrated effectively, engineering-driven approaches can help address today’s most pressing compliance obstacles.

Identifying reasons for today’s compliance crisis of scale

The sheer volume of data captured and stored today is an inherent compliance red flag. While data is often preserved for potential legal proceedings, simply possessing it introduces many types of risks. Consider the ongoing dispute between Apple Inc. and officials in the United Kingdom (UK) over whether the government can enforce “backdoor” access to encrypted customer data in cloud services. With a hearing expected in early 2026, the financial implications remain unclear. Apple has reportedly removed its highest level of data security for UK customers in response to the proceedings, meaning that not all data will be fully encrypted.

Further complicating the ability to maintain compliant practices is the inconsistency among regulating bodies, particularly in relation to data protection. For example, the UK’s General Data Protection Regulation (GDPR) requires opt-in consent for data processing, while the landmark California Consumer Privacy Act (CCPA) in the United States emphasizes a consumer’s right to opt out of data sales. As additional U.S. states enact their unique data privacy laws, compliance management will become more complex.

Moving beyond manual compliance

Legal departments have traditionally operated in silos. This rigid mindset is ill-suited to the rapid pace of 21st-century communication expectations and data collection patterns. Similarly, traditional manual compliance processes have not kept pace with the volatile nature of regulatory changes. Manual strategies are prone to human error, time-consuming, and expensive to maintain. Meanwhile, the amount of data created is expected to double approximately every three years, producing petabytes of information that legal departments are required to manage. As data volumes accelerate beyond the capacity of legal oversight, relying on manual oversight has become not just inefficient, but a guaranteed point of failure. This growing imbalance further supports the need to initiate engineering-first compliance solutions and incorporate core engineering principles that invoke suchelements as ethics, functionality, sustainability, and systems into compliance protocols.

By fostering an environment in which engineers and legal teams closely collaborate,methods become more proactive rather than reactive, leading to improved productivity. Among the essential core concepts to implement are automation, scalability, andsystemic design, which can replace time-consuming tasks such as manualdocumentation and review. A truly scalable solution often takes the form of acentralized, automated data preservation platform. In a manual environment, legal holdinstructions are sent to dozens of system administrators, creating a high risk of humanerror.

An engineered approach, by contrast, is a centralized, application programming interface (API)-driven engineered system. It replaces fallible human communication with a single, auditable set of automated signals that propagate across the entire corporate infrastructure, ensuring defensibility at enterprise scale and preventing costly over-preservation of data, which can violate privacyregulations like GDPR.Automation also reduces the likelihood of human error by streamlining the collection of evidence and enabling efficient workflows. These systems enhance productivity and accuracy by providing the necessary data to make the right decisions at the right time. They also strengthen internal and external audit readiness by collecting, organizing, and presenting compliance data in clear, actionable formats.

Additionally, automated systems are designed to scale with evolving regulations and operational growth. Companies can create reusable templates for smaller cases that can expand as necessary. An example of effective system design is a centralizeddata and communication platform that connects seamlessly to multiple APIs, supporting smoother integration with new tools and software.

Reframing compliance with engineering-based outcomes

Purely manual processes are a form of operational risk. Consistency and responsiveness hold less value in a marketplace driven by rapid innovation and shifting jurisdictional laws. While companies invest significant resources in modeling potential risks, many lack sufficient preparation for responding efficiently to compliance failures. Cross-departmental collaboration reduces this risk. It’s vital to measure cost-effectiveness not only in annual savings but also in avoided penalties and reduced exposure to compliance violations.

The cost of a single compliance failure, whether through regulatory fines or adverse litigation outcomes, can easily run into the tens or hundreds of millions of dollars. System consistency reduces the chances of costly one-off mistakes. Additionally, engineering-based compliance supports scalability through sublinear processes that consume fewer resources as they grow.

Aligning compliance engineering with digital transformation

Although many companies continue to view their legal departments as cost centers, it is essential to view the development of engineering solutions as a long-term investment. Artificial intelligence (AI) and machine learning (ML) are playing an increasingly vital role in this transformation through technology-assisted review (TAR). Commonly referred to as “predictive coding,” AI- and ML-powered TAR enables faster, more accurate data review by training software to recognize specific data patterns. Especially valuable during processes connected to eDiscovery, TAR has been proven to reduce time spent on administrative obligations, costs, and staff effort during compliance reviews and internal investigations.

TAR can also tag and rate documents for further review and highlight relevant information. Among the limitations of TAR are its capability to review text-rich documents only, which excludes spreadsheets.

Once implemented, engineering systems must be robust enough to adapt to new tools and regulations without having to start from ground zero. Ultimately, leadership determines the success of any engineering system. It’s incumbent upon an organization’s executives to champion an engineering-first vision and integrate engineering teams within legal departments, empowering them to innovate and adapt to the latest technologies and regulations. Implementation is most effective when organizations encourage experimentation and learning without penalizing trial and error, recognizing that there is no single blueprint for success.

Looking ahead, the principles of engineering will be strengthened by AI and ML advancements. The next generation of compliance systems will automate responses and proactively identify potential risks in communications and data patterns before they escalate. For leaders in banking and finance, embracing an engineering-first mindset is no longer simply a best practice. It is the only sustainable strategy for navigating the complex regulatory environment of the future.

About the Author:

Amar Joshi is a technology lead and a senior software engineer specializing in the field of legal engineering at a leading global technology company. He leads the design and implementation of complex, large-scale technology solutions essential for eDiscovery, litigation support, and regulatory compliance. His work has been central to navigating high-stakes government investigations and has saved millions of dollars in operational costs. Joshi earned his master’s degree in computer science from New York University and has been a leader in the legal techspace for more than a decade. Connect with him on LinkedIn.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of the author’s employer.