The 1960s ushered in an era of generational change in America when the indecipherable vocabulary of the youth resulted in the rise of the “communication gap” with adults. The period also heralded the start of great technological change.

The beginning of the decade saw the first robot put to use in a factory, and the first commercial video game, “Pong,” was released for holiday shoppers in the mid-1960s. At the end of the decade, in 1969, the first message was sent between two computers: Programmers attempted to send the word “login,” … but the computer crashed after the “o” was typed.

In some ways, this inauspicious yet exhilarating start of the technology era mirrors the relationship – and a new communication gap – between two key stakeholders that have been driving the response to new data privacy laws and the policies to support these technological developments ever since: lawyers and engineers.

The Precision of Language and Power of Alignment

According to a leading technology consulting group, by the end of 2024, modern privacy regulations will cover 75 percent of the world’s population. As of mid-2022, there were more than 100 privacy laws worldwide, with many things in common, but no two were exactly alike. As new privacy laws are enacted, privacy policies and data practices are evolving to accommodate these additional or updated regulations. Since the EU enacted its General Data Protection Regulation (GDPR), the average word count of privacy policies has increased by 25 percent. In fact, the privacy policy for one of the most popular global apps has grown over the past 15 years from 3,000 to 12,000 words – nearly three times as long as the U.S. Constitution.

Engineers are the epicenter of building new services and programs leveraging data and machine learning on the ever-expanding nucleus of innovation. The lawyers lead and innovate in developing privacy laws, policies, and data processing guidelines. In the world we live in today, this represents the inflection point of the current communication gap. Lawyers and engineers may see the exact words, but they interpret them differently because they speak different languages and use different semantics.

On many levels, these two groups of professionals share some similar traits. Both are analytical and think rationally and critically. But ask an engineer what 1+1 equals, and they’ll immediately tell you “2.” Ask the same of a lawyer, and first, they’ll want to know why you’re asking, then they’ll consider the risk of responding, and finally, the options for responding. Engineers typically engage in rules-based, deterministic, and probabilistic but predictable thinking. They thrive with structure and process and see most things as a problem to be solved. On the other hand, lawyers navigate a minefield of nuance and ambiguity before they end up with a solution – or any number of possible solutions – or it depends.

Unfortunately, for both lawyers and engineers, data privacy is not a binary concept. Privacy laws and policies are like a candy box full of ambiguities for lawyers, with words like “promptly,” “reasonable,” and “adequate performance.” How, then, can an engineer, accustomed to building systems that require precision, create a structure to solve the problem the lawyer sets up? The task of operationalizing privacy starts to seem insurmountable. The solution begins with mutual understanding and breaking away the layers of abstractions in each direction.

To help bridge the communication gap, we’ve provided a brief guide of 12 common terms in privacy to demonstrate how lawyers and engineers think.

Crossing the Bridge to Understanding and Better Product Building

As the guide above shows, bridging the communication gap will take work on both sides. In some cases, the same language produced somewhat similar definitions, while in other cases, the definitions seemed to come from two different dictionaries; and in a couple of cases, there was no understanding of the terminology at all.

The lesson here: To build understanding - and a better product or program - lawyers and engineers need to come together early in the process. If they can’t speak the same language, at least they can cross the communication barrier to reach an understanding of key terms and concepts before the hard work begins.

The solution: The best way to bridge the gap is to navigate the different levels of abstraction. On one side, engineers need to raise their level of abstraction, such as talking about what a product, or component of a product that is part of a logical group, does with regard to personal data processing. Engineers can ask themselves, “What business or user value does it provide? What product does it map to? What are the legal implications of the legal documents like MSA and DPA we sign with our customers and vendors?” On the other side, lawyers need to lower their level of abstraction, moving beyond the legal aspects and understanding that a product is built on infrastructure, code, and micro-services that may be shared or isolated. Lawyers build careers on asking questions, so ask the right questions to surface and assess the risks.

As a common example, let’s look at Data Analytics as a processing activity at an e-commerce SaaS business where both engineers and lawyers think and communicate with an altered or modified sense of abstraction on each side.

To help correctly identify all Data Analytics processing activities, the lawyer may ask the question with a slightly lower level of abstraction:

On the engineering side (even though grounding the thinking in core infrastructure, code, and services they build), they may raise the level of abstraction to answer the question by producing:

Similarly, on the topic of Purpose Limitation for the Data Analytics processing activity, the conversation may rather go as follows:

The end goal of an effective privacy program is a positive user experience, balanced with functionality and compliance for the company. Creating this positive experience requires lawyers and engineers to bridge the communication gap. Together, the two sides can be more effective than they can by themselves while delivering value to the business.

Closing The Communication Gap and Connecting The Dots — Relyance AI

Trust is paramount to business operations today. Trust and understanding among employees of an organization manifest in trust in the organization's product and business operations. Organizations often get this wrong as they approach privacy and data protection backward – leading to further breakdown in communication.

As we have seen in the concrete examples above, gaps in semantics and pushing the top down against the agile engineering product development workflows is the root cause of a lot of friction. We believe there is a better way.

At Relyance AI, we flip the traditional approach on its head. Instead of superimposing legal requirements onto engineering and machine-learning workflows, we start with the actual source of truth: the code, the data, the systems, the services, the infrastructure, the APIs, and the business processes. Then, we reverse-engineer the state of data topology and operations onto regulatory requirements, privacy and data policies, and contractual obligations with completely re-imagined workflows. At key steps, we lower and simultaneously raise the levels of abstraction to always generate and promote seamless cross-functional collaboration.

Book a live demo to see how we bridge the gap and connect the dots with the Relyance AI platform.