Myth Busting Legal Compliance Automation in Data Analytics

Let’s face it: compliance can be tricky when it comes to data science and analytics. We can all agree that it’s a necessity, but what we usually can’t necessarily agree on is how to meet and monitor its requirements, particularly when legal rules can be vague, at best. This leads to questions about legal definitions, user access levels and technical capabilities bouncing between data users, data owners, legal or compliance teams, IT departments and data governance teams in a frustratingly inefficient cycle. It’s enough to make any data scientist consider disregarding anonymization options entirely.

But data security and compliance regulations are here to stay. And the truth is, compliance automation is the key to enhancing the quality of compliance within analytics and data science environments, and producing self-executing decisions that allow you to scale compliance. Despite its efficiency and effectiveness, there are still misconceptions about legal compliance automation in data analytics. It’s time to bust those myths and show how implementing automated policies can enable your data analytics and BI teams fast, secure and fully compliant access to data, accelerating your data-driven outcomes. 

Myth #1: Compliance automation decisions for analytics and data science are made unilaterally 

No matter how advanced machine learning is, nailing down the true meaning of legal rules will always be elusive. Sometimes even judges can’t agree on how to interpret certain laws — how can we expect technology to do it?

That’s why legal compliance automation remains a challenge; a better approach is to frame it as supportive rather than substitutive. The latter is inherently problematic because it is opaque, meaning compliance with standards such as due process and rule of law is very difficult, if not impossible. Therefore, a substitutive approach opens the door for regulations to be misinterpreted, resulting in the organization being held liable for unmet standards or compromised data. 

However, we suggest that supportive compliance automation should aim to reduce human interaction while still ensuring that a human with relevant legal expertise is in charge and able to make executable, traceable and contestable decisions. A supportive approach should thus remove the fear of unilateral or incomplete policy decision making by integrating relevant interdisciplinary expertises, both legal and technical, in the policy creation process. 

This nuance can put all stakeholders at ease, paving the way for collaborative policy formation. 

Myth #2: Compliance automation is impossible in data science because of the inherently complex and interconnected nature of privacy and data protection frameworks

Maybe you’ve experienced this scenario: Based on a set of compliance regulations, the legal/compliance team makes a broad recommendation to anonymize a data set. But without clear rationale behind the recommendation, it’s nearly impossible to determine which anonymization techniques should be applied to satisfy the regulations. As a result, data prep comes to a halt as teams look for clarity and alignment, and the project timeline is significantly delayed.

This cycle can waste time, energy and resources, in addition to creating an overall sap on motivation. Instead, starting with a RACI modelization can help implement supportive automation collaboratively and efficiently. This method identifies four key roles that exist in organizations across sectors: responsible, accountable, consulted and informed. Approaching supportive automation using the RACI model enables teams to identify which expertise is needed for rule authoring and enforcement, and combines those expertises as early in the process as possible in order to avoid inefficiencies, vague expectations and insufficient outcomes. Clarifying tasks this way and rapidly merging compliance and technical input can be the difference between ultimately creating a rule that says “anonymize data when possible,” and one that says “apply k-Anonymization and purpose-based access for this particular set of projects.” It also creates the opportunity for policy decisions to be contested — and defended — to safeguard against unnecessary restrictions, without discussions entering a black hole of deferral, like the scenario above.

The white paper Demystifying Legal Automation details the frameworks to structure your team around different levels of the decision making process so that policy automation is fast, scalable, technically sound and legally valid.

Myth #3: Automating compliance and policy enforcement leaves my organization susceptible to unforeseen risks 

Every organization and situation is unique. How do you know that setting up frameworks to streamline your decision making process will actually support compliance and appropriate data access permissions when your automation policies are implemented?

A layered approach based on three principles of the Object Oriented Programming paradigm is the most comprehensive way to produce quality risk assessment and scale compliance as data science projects grow in number within:

  1. Abstraction: Identifies commonalities in compliance requirements across projects or families of projects, which reduces the scope of the legal/compliance team and technical assessment to focus on what’s necessary for each project.
  2. Encapsulation: Buckets the essential legal/compliance and technical requirements of each data science project for clearer categorization and universal understanding. 
  3. Inheritance: Establishes common denominators that will make it possible to simplify assessments, inform decision-making, and accelerate execution through early activation of appropriate controls.

These measures empower interdisciplinary teams to leverage compliance automation to clearly define executable requirements and prevent violations of requirements. This proactive approach can help anticipate and take steps to avoid risks before they become full-blown failures, as well as solve for compliance anti-patterns. Demystifying Legal Automation outlines scenarios in which abstraction, encapsulation and inheritance can help teams accelerate compliance in data science environments.

Legal automation compliance can get a bad rap, and it’s not hard to see why. But automated policy creation doesn’t have to be a burden. When a rule or set of rules has been developed by a team with relevant expertise, has the goal of satisfying legal and business requirements, can be applied across various projects and is immediately executable, you have an automated policy. This enables your team to scale compliance across data sets, focuses your legal and technical assessments to save time and improve quality and allows data to be accessed faster than ever. 

To learn more about legal automation compliance, download the Demystifying Legal Automation white paper here


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