Beyond the Perimeter: Practical Patterns for Fine‑Grained Data Access
Data Engineering Podcast
00:00
AI for translating regulations into enforceable policies
Matt predicts AI will help transform legal/regulatory documents into machine-readable policies (Rego/CedAR) and improve catalogs.
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Summary
In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.
Announcements
Parting Question
In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Matt Topper about the challenges of managing identity and access controls in the context of data systems
- Introduction
- How did you get involved in the area of data management?
- The data ecosystem is a uniquely challenging space for creating and enforcing technical controls for identity and access control. What are the key considerations for designing a strategy for addressing those challenges?
- For data acess the off-the-shelf options are typically on either extreme of too coarse or too granular in their capabilities. What do you see as the major factors that contribute to that situation?
- Data governance policies are often used as the primary means of identifying what data can be accesssed by whom, but translating that into enforceable constraints is often left as a secondary exercise. How can we as an industry make that a more manageable and sustainable practice?
- How can the audit trails that are generated by data systems be used to inform the technical controls for identity and access?
- How can the foundational technologies of our data platforms be improved to make identity and authz a more composable primitive?
- How does the introduction of streaming/real-time data ingest and delivery complicate the challenges of security controls?
- What are the most interesting, innovative, or unexpected ways that you have seen data teams address ICAM?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on ICAM?
- What are the aspects of ICAM in data systems that you are paying close attention to?
- What are your predictions for the industry adoption or enforcement of those controls?
Parting Question
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
- UberEther
- JWT == JSON Web Token
- OPA == Open Policy Agent
- Rego
- PingIdentity
- Okta
- Microsoft Entra
- SAML == Security Assertion Markup Language
- OAuth
- OIDC == OpenID Connect
- IDP == Identity Provider
- Kubernetes
- Istio
- Amazon CEDAR policy language
- AWS IAM
- PII == Personally Identifiable Information
- CISO == Chief Information Security Officer
- OpenTDF
- OpenFGA
- Google Zanzibar
- Risk Management Framework
- Model Context Protocol
- Google Data Project
- TPM == Trusted Platform Module
- PKI == Public Key Infrastructure
- Passskeys
- DuckLake
- Accumulo
- JDBC
- OpenBao
- Hashicorp Vault
- LDAP
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