Connect AI Workflows to Data Safely: LINK Framework



How to Connect an AI Workflow to One Approved Data Source

A practical beginner-friendly guide to connecting AI workflows to approved information using the LINK framework.

After mapping an AI workflow, it may be tempting to connect every useful folder, database, and application.

Do not begin there.

Start with one approved data source, one clearly defined purpose, and the minimum access required for a supervised trial.

The goal is not simply to make information available to AI. It is to create a connection whose ownership, permissions, contents, limitations, and use can be reviewed.

The LINK Framework

L — Locate the Source
I — Isolate Access
N — Name Inputs and Outputs
K — Keep Evidence

L — Locate One Authoritative, Approved Source

Information being available does not mean it is approved for AI use.

Before connecting a source, document:

  • Source name and location
  • Accountable owner
  • Authorization to use the information
  • Data origin and collection method
  • Included and excluded records
  • Update frequency
  • Known quality limitations
  • Applicable organizational policies

A shared folder containing several versions of a document is not automatically authoritative. A database may contain obsolete, incomplete, or restricted records.

Ask the owner to identify the current source of truth and document approval.

The NIST AI Risk Management Framework Playbook provides guidance on data provenance, relevance, quality, and risk management.

I — Isolate and Minimize Access

When the workflow only needs to retrieve information, begin with read-only access.

Read-only permissions can prevent direct modification of source records, but they do not guarantee that retrieved information is accurate, safe, or appropriate to share.

Recommended Access Boundaries

  • Use credentials dedicated to the workflow.
  • Restrict access to an approved repository or collection.
  • Apply authorized filters and date ranges.
  • Retrieve only necessary fields.
  • Exclude unnecessary sensitive information.
  • Limit records, requests, and execution time.
  • Restrict onward sharing with unrelated tools.
  • Require approval before consequential downstream actions.
Important Security Rule

Access authorization must be enforced by the connected system. Do not rely on the language model to determine whether a user is permitted to access a record.

See OpenAI's MCP server guidance for additional information about authorization boundaries.

N — Name the Permitted Inputs and Outputs

Avoid vague instructions such as "search everything relevant."

Instead, define exactly what the workflow may request and return.

Example: Structured Retrieval

Permitted Input Permitted Output
Product identifier Document title
Approved date range Approved summary field
Document category Source URL or record ID
Authorized query filters Last-updated date
Request identifier Retrieval timestamp and missing-information status

Structured fields make results easier to validate and reduce unnecessary information flow.

The output should clearly distinguish:

  • Retrieved fact: Information found in the approved source
  • AI-generated summary: Model interpretation of source information
  • Missing information: Required information not found
  • Conflicting record: Sources containing inconsistent details
  • Reviewer-required item: Information needing human judgment

A generated summary must never silently become an authoritative source record.

For more guidance, see OpenAI's agent safety documentation.

K — Keep Evidence of Every Retrieval

Retrieval logging helps teams understand what information influenced an AI-generated response.

For each retrieval, capture:

  • Source queried
  • Requesting account or workflow
  • Retrieval timestamp
  • Filters applied
  • Requested fields
  • Number of returned records
  • Blocked or omitted information
  • Generated output reference
  • Reviewer and decision
  • Subsequent action

Logs should follow organizational privacy, access-control, and retention policies. Avoid storing unnecessary sensitive content in logs.

Example Retrieval Log

Field Example Value
Source Approved Policy Library
Access Read-only
Filter Published policies only
Returned records 3
Output Draft policy answer with citations
Reviewer Policy Manager
Status Pending Review

These values are illustrative, not actual retrieval records.

Example: Connect One Internal Policy Library

Suppose an AI assistant helps employees locate answers to internal policy questions.

Weak Connection Plan

"Let the AI assistant search our entire shared drive."

This approach could expose drafts, archived policies, personal folders, and unrelated records.

Better: Bounded Connection Plan
  • Source: Approved Operations policy library
  • Owner: Operations policy manager
  • Access: Read-only
  • Collection: Published policies only
  • Fields: Title, text, effective date, owner, source link
  • Excluded: Drafts, archives, personnel files
  • Output: Draft answer with policy references
  • Stop conditions: Missing or conflicting active policy
  • Approval: Policy manager for consequential guidance
  • Log: Query, records, citations, reviewer decision

The narrower design can be tested without exposing the entire shared drive.

Copy-and-Paste LINK Prompt for ChatGPT

Use the following prompt to design a controlled connection before configuring the integration.

LINK Framework Prompt

Act as an AI workflow security and data governance reviewer. Design a bounded connection between an AI workflow and one approved data source. Use the LINK framework: L — Locate the source I — Isolate access N — Name inputs and outputs K — Keep evidence Workflow step: [DESCRIBE STEP] Proposed source: [NAME SOURCE] Source owner: [ADD OWNER] Approved records: [DEFINE SCOPE] Required fields: [LIST FIELDS] Excluded information: [LIST EXCLUSIONS] Reviewer: [ADD ROLE] Please: 1. Identify source ownership and required authorization. 2. Document provenance, update frequency, and known limitations. 3. Recommend minimum read-only access. 4. Define approved records, filters, and fields. 5. Identify sensitive information to exclude. 6. Define structured input and output schemas. 7. Recommend record, request, and retry limits. 8. Define freshness checks and stop conditions. 9. Require human approval for consequential actions. 10. Create a retrieval-log template. 11. Identify security and data-quality risks. 12. Provide a supervised test plan. Do not assume access, ownership, or authority that has not been provided. Mark missing details as "Needs confirmation." Present the result as a connection specification, risk checklist, and approval plan.

Five Common Mistakes to Avoid

1. Connecting a Source Without a Named Owner

Every approved source needs an accountable person who can authorize access and clarify quality or policy questions.

2. Granting Broad Access for Convenience

Restrict retrieval to the records and fields required for the documented purpose.

3. Assuming Read-Only Means Trustworthy

Read-only information may still be inaccurate, outdated, sensitive, or contain malicious instructions.

4. Ignoring Data Freshness

Record when information was updated and define when it is too old to use without confirmation.

5. Skipping Retrieval Logs

Without sufficient evidence, it becomes difficult to determine which records influenced an unexpected output.

Approved Data Source Connection Checklist

☐ One authoritative source identified.

☐ Accountable source owner confirmed.

☐ Authorization documented.

☐ Intended workflow purpose defined.

☐ Read-only access evaluated.

☐ Permitted collections and fields specified.

☐ Sensitive and unrelated information excluded.

☐ Input and output schemas documented.

☐ Freshness and quality checks defined.

☐ Request, record, and retry limits established.

☐ Stop conditions documented.

☐ Retrieval logging configured.

☐ Human reviewer assigned.

☐ Supervised test completed before expansion.

Keep Downstream Decisions Human-Reviewed

Retrieved information can inform an answer without authorizing an action.

Require accountable human review for consequential recommendations or actions involving customers, employees, finances, legal obligations, healthcare, security, or record changes.

Whenever practical, begin with a test source or sanitized records.

Do not enter confidential, personal, employee, client, financial, or medical information into an AI system that has not been approved for that information.

Final Thoughts

Your first AI data connection should be narrow enough to understand and verify.

Use LINK to locate one approved source, isolate access, name permitted inputs and outputs, and keep reviewable evidence.

Expand only after the connection performs reliably during normal, missing-data, conflicting-data, and failure scenarios.

Start narrow. Verify access. Keep evidence. Expand responsibly.

Free Resource: Approved Data Source Checklist + LINK Prompt Sheet

Use this companion resource to evaluate source ownership, permissions, data boundaries, retrieval evidence, and human review before connecting your AI workflow.

Download: [ADD FREE RESOURCE DOWNLOAD URL]

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Sanjeev from Dallas | AI Tools & Hacks

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