Internal RAG Data Hygiene Screener

Connecting an internal Large Language Model to your corporate data repositories is highly dangerous without pristine access controls.

This setup is known as Retrieval-Augmented Generation. It acts as an incredibly efficient search engine.

If your underlying permissions are messy, the AI will instantly surface sensitive HR reviews and legal documents to unauthorized employees. This transforms an IT problem into a massive GDPR and EU AI Act liability.

You cannot launch an internal Copilot without first auditing the hygiene of the data it connects to.

The Flashlight in a Hoarder's Attic Analogy

Searching for a specific misfiled document manually takes hours. Most employees will never find it.

Deploying an internal AI agent is like handing everyone a high-powered flashlight. The AI finds everything instantly.

If your permissions are broken, the AI will hand a confidential executive salary file to a junior intern simply because they asked a clever question.

3D illustration of a digital lock protecting corporate files from an AI scanning laser

Audit Your Data Environment

Evaluate your actual Identity and Access Management reality before granting system access to an internal LLM.

Generate your Deployment Readiness Report locally. Use this to pause unapproved AI rollouts until fundamental data hygiene is secured.

Privacy By Design: This executes entirely in your browser. We never see your responses.

1. Access Control Baseline (IAM)

How are file permissions currently structured across your connected repositories? Example: SharePoint or Google Workspace.

Default Open (Critical Risk)

Files are generally visible to the whole company unless actively locked down. Permission creep is rampant.

Group Based

Permissions are managed via broad departmental groups. Files are occasionally saved in the wrong group folder.

Strict Least Privilege (Defensible)

Access is rigorously controlled. Employees only possess access to the specific files required for their daily duties.

Data Security Note: Your selections stay right here on your screen.

2. Data Segregation

What data is the AI actually allowed to read and index?

Global Index (Critical Risk)

The AI reads everything on the network. It relies purely on existing user permissions to filter outputs.

Excluded Folders

The AI index attempts to block specific high-risk folders. Example: Blocking the HR drive.

Whitelisted Only (Defensible)

The AI only indexes pre-approved, sanitized data repositories. All other corporate drives are physically disconnected from the model.

Privacy Note: We do not transmit, sync, or store your responses.

3. Audit and Retrieval Logging

When a user prompts the AI, how is the data retrieval logged?

No Traceability (Critical Risk)

We only see the prompt text. We cannot determine which specific source files the LLM accessed to generate the answer.

Basic System Logs

We can theoretically trace retrieval through deep server logs, but there is no accessible dashboard for security audits.

Full Provenance Logging (Defensible)

We explicitly log the user, the exact prompt, and the unique document IDs the model retrieved to form the response.

Data Sovereignty Lock: Your selections evaluate locally. We never see them.

Disclaimer: This diagnostic evaluates data handling risks associated with Retrieval-Augmented Generation. It does not replace a formal Data Protection Impact Assessment under GDPR Article 35. Consult licensed EU regulatory counsel regarding internal data processing compliance.

Internal RAG Data Hygiene Screener

Audit Your Data Environment

1. Access Control Baseline (IAM)

2. Data Segregation

3. Audit and Retrieval Logging

Readiness Report Output

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