Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. The team at Immuta has built a platform that aims to tackle that problem in a flexible and maintainable fashion so that data teams can easily integrate authorization, data masking, and privacy enhancing technologies into their data infrastructure. In this episode Steve Touw and Stephen Bailey share what they have built at Immuta, how it is implemented, and how it streamlines the workflow for everyone involved in working with sensitive data. If you are starting down the path of implementing a data governance strategy then this episode will provide a great overview of what is involved.
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- Your host is Tobias Macey and today I’m interviewing Steve Touw and Stephen Bailey about Immuta and how they work to automate data governance
- How did you get involved in the area of data management?
- Can you start by describing what you have built at Immuta and your motivation for starting the company?
- What is data governance?
- How much of data governance can be solved with technology and how much is a matter of process and communication?
- What does the current landscape of data governance solutions look like?
- What are the motivating factors that would lead someone to choose Immuta as a component of their data governance strategy?
- How does Immuta integrate with the broader ecosystem of data tools and platforms?
- What other workflows or activities are necessary outside of Immuta to ensure a comprehensive governance/compliance strategy?
- What are some of the common blind spots when it comes to data governance?
- How is the Immuta platform architected?
- How have the design and goals of the system evolved since you first started building it?
- What is involved in adopting Immuta for an existing data platform?
- Once an organization has integrated Immuta, what are the workflows for the different stakeholders of the data?
- What are the biggest challenges in automated discovery/identification of sensitive data?
- How does the evolution of what qualifies as sensitive complicate those efforts?
- How do you approach the challenge of providing a unified interface for access control and auditing across different systems (e.g. BigQuery, Snowflake, RedShift, etc.)?
- What are the complexities that creep into data masking?
- What are some alternatives for obfuscating and managing access to sensitive information?
- How do you handle managing access control/masking/tagging for derived data sets?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned while building Immuta?
- When is Immuta the wrong choice?
- What do you have planned for the future of the platform and business?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Data Governance
- Data Catalog
- Snowflake DB
- ABAC == Attribute Based Access Control
- RBAC == Role Based Access Control
- Paul Ohm: Broken Promises of Privacy
- PET == Privacy Enhancing Technologies
- K Anonymization
- Differential Privacy
- LDAP == Lightweight Directory Access Protocol
- Active Directory
- COVID Alliance