Introducing ACL Hydration: secure knowledge workflows for agentic AI

2026-04-06 · Source: Blog | DataRobot · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

DataRobot has launched ACL Hydration, a new feature within its Agent Workforce Platform designed to secure knowledge workflows for agentic AI. This unified framework ingests unstructured enterprise content while preserving and enforcing source-system access controls (ACLs) at query time. The system addresses the critical problem of unauthorized data exposure in RAG implementations, where agents might retrieve sensitive documents for unauthorized users. ACL Hydration provides enterprise data connectors for systems like SharePoint, Google Drive, Confluence, Jira, OneDrive, and Box, with more planned. Its core differentiator is capturing and persisting document-level ACL metadata alongside vectorized content, storing it in a centralized, decoupled cache that refreshes in near real-time. This ensures dynamic permission enforcement, allowing different users to receive distinct, permission-scoped answers from the same agent query, thereby mitigating security and compliance risks for GenAI rollouts.

Key takeaway

For AI Architects and CTOs deploying agentic AI, DataRobot's ACL Hydration directly addresses a major blocker for production rollouts: unauthorized data access. Your teams can now build agents that access comprehensive enterprise knowledge without compromising security, as the platform enforces source-system permissions end-to-end. This capability eliminates weeks of custom security engineering, accelerating agent project timelines and fostering user trust in GenAI systems.

Key insights

ACL Hydration secures agentic AI by enforcing source-system access controls on enterprise knowledge at query time.

Principles

• Decouple ACL cache from vector database.
• Continuously refresh ACLs from source systems.
• Enforce permissions dynamically at retrieval.

Method

Ingest content with ACL metadata, map external identities, store ACLs in a centralized cache, and enforce permissions dynamically at query time using an authorization layer.

In practice

• Connect to SharePoint, Google Drive, Confluence.
• Monitor and audit all ACL changes.
• Use DataRobot's built-in FAISS or external VDBs.

Topics

• ACL Hydration
• Agentic AI
• Enterprise Knowledge Workflows
• Access Control Lists
• RAG Security

Best for: CTO, AI Architect, VP of Engineering/Data, AI Engineer, MLOps Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Blog | DataRobot.