Posts tagged with security

Slack EngineeringMar 7, 2025

How we built enterprise search to be secure and private

Why it matters: This article details how to build secure, privacy-preserving enterprise search and AI features. It offers a blueprint for integrating external data without compromising user data, leveraging RAG, federated search, and strict access controls. Essential for engineers building secure data platforms.

Slack's enterprise search and AI uphold strict security and privacy by keeping customer data within Slack's trust boundary, utilizing an AWS escrow VPC for LLMs.
The system employs Retrieval Augmented Generation (RAG) instead of training Large Language Models (LLMs) on customer data, ensuring data privacy and preventing retention.
Enterprise search operates on a federated, real-time model, never storing external source data in Slack's databases, but rather fetching it via partner APIs.
Access to external content is strictly permissioned based on the user's existing Access Control Lists (ACLs) and requires explicit user/admin consent, adhering to the principle of least privilege.
External data and permissions are always up-to-date with the source system, ensuring accuracy and compliance.
Search Answer summaries generated by the AI are ephemeral, shown to the user and immediately discarded, further enhancing privacy.

#security #data #dist

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Instagram EngineeringJan 25, 2020

Five things I learned about working on content quality at Instagram

Why it matters: Managing content quality at scale requires balancing real-time signals with static analysis. This approach shows how to operationalize quality metrics and use multi-stage ML pipelines to protect users while maintaining high-performance recommendation systems.

Combined manual labeling with classifier scores to create calibrated metrics for statistically significant A/B testing results.
Developed 'read-path' models that utilize real-time engagement signals like comments and likes to improve detection precision.
Maintained 'write-path' filters at the sourcing level to handle low-prevalence violations and ensure a baseline of benign content.
Implemented a multi-stage pipeline that balances high-precision sourcing filters with fine-tuned ranking models.
Established continuous model performance tracking to identify edge cases and maintain user safety standards.

#mlp #data #security

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