Trust but Verify. Implementing your data access infrastructure

Data discoverability

Data access enforcement

Assessing your strategy

  • How data assets are used across various departments or business verticals
  • How long it takes to get permission- if the review needs to be done by someone else in the organization it might take longer
  • What percentage of auto-grants are marked as false positives — meaning that someone who shouldn’t have gotten permission to view the data actually got it
  • What percentage of denied queries are restructured to avoid need for grant — minimizing access to sensitive info

Conclusions

  1. Before you start any AI or ML initiative make sure you’ve ironed out how people can go about accessing your datasets
  2. Define what you see as sensitive data and establish a clear set of rules for accessing it (bear in mind that there are plenty of opportunities to automate this)
  3. Non-sensitive internal data should be open and discoverable across the organization
  4. Document and audit everything
  5. A good building block to start with in terms of infrastructure is: OPA + git + Kubernetes

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