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Managing Data Regulation and Privacy Compliance

Today, democratization of data science across the enterprise means that companies are using more data in more ways than ever before, but it also presents new challenges — namely that businesses’ IT organizations are often not able to scale to meet the demands of increased access.

With the heightening challenge given the influx of new data regulations being drafted, not to mention upkeep of processes that ensure continued compliance with existing legislation, things start getting complex quickly in terms of governance.

But compliance cannot exist on the technical level alone; true compliance and respect for data regulations is a mindset that must be embedded throughout the organization. Besides mitigating legal risk, collaboration across all the teams that engage with or benefit from users’ data is critical for larger success.

Since most developers and analysts are not lawyers, the majority of teams involved in data usage will not have the expertise needed to understand whether a particular action is compliant or not. Through education, restrictive admin rights, and broken-down silos, teams can distribute expertise and still uphold compliance.

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Understanding the Key Questions for Data Compliance

Data compliance is all about asking questions and building time into project lifecycles to get the answers. While the nature of legislation suggests a lack of room for interpretation, understanding key facts about data before it is used is critical to ensuring compliance.

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Privacy-Compliant Data Projects

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Dataiku and Data Privacy

Dataiku is the centralized data platform that enables the creation of Enterprise AI projects while also providing the tools for data regulation compliance. Dataiku ensures more opportunities for business-impacting models by providing a solid foundation for data governance policies that allow teams to work faster, smarter, and more securely.

Enterprises using Dataiku can better prepare themselves for current — or future — data regulations with the capacity to:

  • Monitor datasets, or entire projects that contain personal data
  • Document each dataset’s purpose, consent, and retention policies
  • Maintain project- and dataset-level security (user rights, available connections, etc) and automatically restrict model creation within a project where the dataset contains personal data
  • Generate reports with all datasets and models that contain or use personal data
  • Create specific, access-controlled environments in which authorized users can work on anonymized data
  • Handle right-to-erasure requests with ease

Effectively Managing Enterprise-Wide Risk

The age of AI presents additional risks across the enterprise that require a tighter — yet more flexible — governance structure.

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Go Further

PwC: On GDPR and the Future of Regulations

We talked to Rémi Dusaud, Director of Data Privacy at PwC, about what the landscape is like now and what’s to come.

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Introducing MLOps

What it is, why it matters, and how to develop ML models in a way that is consistent with sound MLOps practices — all brought to you by Dataiku and O'Reilly.

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How to Do Responsible AI

Despite the hype, most companies still have not dedicated the time and effort to ensure models are deployed responsibly.

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Data Privacy Compliant Projects

This guidebook walks through the myths & realities of working with personal data, working with anonymized data, and pseudonymization.

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