Compliance monitoring · Production

Meta scales Privacy Aware Infrastructure (PAI) to embed privacy controls across GenAI product development

The problem

GenAI products introduce novel data types, dramatically increased data volumes, shifting privacy and compliance requirements, and faster development cycles that strain existing privacy infrastructure.

Workflow diagram · grounded in source

User interaction generates data

trigger

“To maintain the privacy requirements for the data under consideration — for example, for user-interaction data from the scenario above with our AI glasses — we need a complete map of its movement”

Automated scanning and tagging at ingestion

integration

“Automated data detection through advanced scanning and tagging identifies relevant data at the point of ingestion”

Cross-stack lineage collection

integration

“PAI collects these lineage signals crossing all stacks, including web probes, logger, batch-processing lineage, RPC lineage, and training manifests. Together they form an end-to-end graph for interaction data”

Policy Zone enforcement

validation

“We use lineage to guide the placement of Policy Zones, protecting interaction data. We start the training job for a model using a data asset in this zone only if all training-data assets are permitted for this purpose; otherwise, we reme…”

Continuous edge verification

feedback_loop

“our verifiers watch these edges over time, so that any new or changed data-processing jobs are identified early during feature development”

Reported outcome

PAI supports thousands of microservices and product teams across Meta's ecosystem, providing auditable real-time insight into every data flow and enabling Meta to launch GenAI products like AI glasses at global scale with verifiable privacy guarantees.

Reported metrics

Microservices and product teams supportedthousands of microservices and product teams

Data and code assets spannedmillions of data and code assets

Reported stack

PrivacyLibPolicy Zoneslarge language model (LLM)

Source

https://engineering.fb.com/2025/10/23/security/scaling-privacy-infrastructure-for-genai-product-innovation/

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

PAI supports thousands of microservices and product teams across Meta's ecosystem, providing auditable real-time insight into every data flow and enabling Meta to launch GenAI products like AI glasses at global scale…

What tools did this team use?

PrivacyLib, Policy Zones, large language model (LLM).

What results were reported?

Microservices and product teams supported: thousands of microservices and product teams; Data and code assets spanned: millions of data and code assets (source-reported, not independently verified).

How is this compliance monitoring AI workflow structured?

User interaction generates data → Automated scanning and tagging at ingestion → Cross-stack lineage collection → Policy Zone enforcement → Continuous edge verification.