Are your SharePoint agents suddenly surfacing answers that feel too honest—or worse, too exposed? It’s probably not “AI being spooky.”
It’s your permissions, scope, and DLP. In this episode, we unpack why SharePoint agents leak data, why it’s almost never “hallucination,” and how to fix it with:

• Tight knowledge source scoping
• Permission and inheritance hardening in SharePoint
• Sensitivity labels + Purview DLP that actually block agents
• Approval gates for agents, licensing boundaries, and data policies
• A baseline policy pack you can roll out as an IT admin today

If you’re an M365 admin, SharePoint architect, security engineer, or Copilot / agents owner, this is your practical playbook for stopping AI-driven data leaks before they start. 🔍 Episode Summary Your SharePoint agent didn’t “leak” data because AI is haunted.
It leaked because you overscoped the agent and left permissions inheritance and DLP in a half-configured state. In this episode, you’ll learn:

• How SharePoint agents actually see data (Graph + ACLs + labels + DLP)
• Why grounding does NOT equal security
• The difference between retrieval filters and permissions boundaries
• How to scope knowledge like a lawyer writes contracts
• How to break inheritance the right way and pair it with sensitivity labels
• How to build DLP patterns that bite, not just log
• How to use PayG / licensing and approval workflows as hard guardrails
• How to monitor, audit, and safely rollback when something goes wrong
• A baseline agent governance pack you can deploy today

This isn’t a hype episode. It’s an IT pro fix for a very real risk. 🧬 Segment 1 – How SharePoint Agents Actually See Your Data We start by demystifying how agents “see” SharePoint:

• Agents don’t read your intentions; they read Microsoft Graph
• Graph is the bloodstream – if ACLs allow access, agents can see it
• An agent = user persona + retrieval filters
  • Persona = the identity and its permissions
  • Retrieval = which libraries/folders/URLs you pointed at

Key idea: Permissions gate first. Retrieval filters only decide where to look, not what’s allowed. We cover:

• Why grounding filters relevance but doesn’t shrink legal access
• How permissions inheritance becomes silent escalation
• How an overscoped agent “accidentally” pulls HR or Legal content from adjacent libraries
• Why “it’s just one site root” is the fastest way to disaster

You’ll walk away with a mental model:

• Gate → Find → Enforce
  • Permissions (ACLs) gate access
  • Retrieval filters help find content
  • Labels + DLP enforce what’s allowed to be processed

Once you understand that stack, the “leak” stops being mystical. 📚 Segment 2 – Control Plane 1: Scope Knowledge Sources Like a Lawyer Next, we fix the first big mistake: overscoping. We walk through how to design knowledge sources so they cannot wander: Core Scoping Rules

• Library-level sources only
  • No site roots
  • No hub-level “everything under here” shortcuts
• Shallow folder depth, avoid recursive “grab the world” patterns
• Metadata filters only
  • Only ingest items where Status = Approved, Version = Published, Department = X, etc.
• Exclude drafts, archives, and “Working” trees
• No crawling arbitrary internal/external URLs “for context”

The Real-World Pattern

• Many small, narrow agents → safer and more predictable
• One giant “encyclopedic” agent → high blast radius

We also cover:

• Why you should disable general AI knowledge for regulated agents
• How to use an explicit fallback answer: “I’m not authoritative for that. Here’s what I can answer.”
• How to test scope using edge-case queries (in-domain vs out-of-domain)

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