AI Training and Data Protection: The 7-Step Operational Guide

Article 4 of the AI Act has required since February 2025 an "AI literacy" obligation: any deployer of an AI system must ensure that their staff has a sufficient level of competence to use these tools responsibly. Training a team on AI while protecting data rests on three pillars: classify data before authorizing tools, deploy real-time technical protection, and document usage for audit.

Prerequisites and Tools Needed

Before launching an AI training program, three elements must be in place.

Inventory of Existing AI Tools

I observe in the field that 70% of companies discover the extent of Shadow AI during an initial audit. Before training, you need to know what's already being used:

• What AI tools are accessible from the network?
• Which employees use them, and for what use cases?
• What data flows through them (text, files, code)?

Data Classification

Article 9 of GDPR distinguishes ordinary personal data from special categories (health, political opinions, union membership). This classification determines which tools are authorized:

List of Use Cases by Department

Each department has different needs. A developer wants to generate code. A salesperson wants to summarize calls. An HR wants to sort resumes. Mapping these use cases allows defining adapted rules rather than a one-size-fits-all policy.

Steps 1-3: Building the Foundation

Step 1: Map Existing Shadow AI

Shadow AI is the unauthorized use of AI tools by employees. CNIL recommendations advocate for a processing inventory before any compliance effort.

Concrete actions:

• Analyze DNS logs to identify contacted AI domains (openai.com, anthropic.com, mistral.ai...)
• Survey teams (anonymously) about their current usage
• Identify "power users" who can become ambassadors

Estimated duration: 1-2 weeks

Step 2: Define a Usage Policy by Risk Level

An effective policy doesn't prohibit everything. It defines what's authorized, under what conditions. ANSSI recommends a risk-level approach.

Recommended tools/data matrix:

Estimated duration: 1 week (management approval required)

Step 3: Choose Approved Tools

ANSSI defines three criteria for evaluating a solution:

1. Sovereignty: Where is data stored? Under which jurisdiction?
2. Transparency: Is the operation documented? Is data used for training?
3. Reversibility: Can data be exported or deleted?

Questions to ask vendors:

• Are prompts used to train the model?
• Where are the servers hosted?
• What is the data retention policy?

Estimated duration: 2-3 weeks (comparison + contract negotiation)

Steps 4-6: Operational Deployment

Step 4: Deploy Technical Protection

Article 32 of GDPR requires "appropriate technical measures". Training alone is not enough: human errors happen.

Deployment options:

A browser extension deployed via MDM (Microsoft Intune, Google Workspace) offers the best protection/friction ratio. Observed deployment time: 15 minutes for 500 workstations.

Estimated duration: 1 day (deployment) + 1 week (rule adjustment)

Step 5: Enable Just-in-Time Training

AI Act Art. 4 doesn't impose a training hour requirement. It requires a "sufficient level of AI literacy" appropriate to context. Just-in-Time training meets this requirement.

Principle: When a user is about to send sensitive data to an AI tool, a contextual alert appears. It explains:

• The specific risk (e.g., "This text contains a social security number")
• The recommended action (e.g., "Use approved tool X" or "Anonymize before sending")
• Link to internal policy

Advantages:

• Training at the moment it's useful (3x memorization according to cognitive studies)
• Automatic documentation (proof of awareness for audit)
• No dedicated training time to schedule

Estimated duration: Configuration in 2-4 hours

Step 6: Create an Internal Support Channel

Training doesn't stop at deployment. Questions arise. Edge cases appear.

Recommended structure:

• Living FAQ: Shared document updated based on questions received
• AI referent per department: A trained point of contact who knows business use cases
• Dedicated Slack/Teams channel: For quick questions and sharing best practices

Estimated duration: 1 week (identifying referents + creating initial FAQ)

Step 7+: Continuous Optimization

Step 7: Analyze Usage Metrics

What's not measured doesn't improve. Usage data reveals necessary adjustments.

Metrics to track:

Step 8: Adjust Policies Quarterly

AI tools evolve rapidly. New services appear every month. A quarterly review allows adjusting:

• Authorized tools (new more secure competitor?)
• Detection rules (new sensitive data patterns)
• Approved use cases (new business requests)

Step 9: Prepare for Audit

The accountability principle (Art. 5.2 GDPR) requires demonstrating compliance. Prepare:

• AI processing registry: Who uses what, for what data
• Training evidence: Alerts viewed and acknowledged, awareness sessions
• Documented policy: Dated version signed by management

Common Mistakes and How to Avoid Them

Mistake 1: Blocking Without Explaining

Symptom: High bypass rate, team frustration.

Solution: Each block must include an explanation and alternative. "This tool is not authorized for customer data. Use [approved tool] instead."

Mistake 2: Training Only Once

Symptom: Initial compliance that erodes in 3-6 months.

Solution: Continuous training via contextual alerts + quarterly reminders during policy reviews.

Mistake 3: Ignoring Existing Shadow AI

Symptom: False sense of compliance, latent risk.

Solution: Mandatory initial audit. Amnesty on past usage if declared, to encourage transparency.

Mistake 4: Uniform Policy for All Departments

Symptom: Policy too restrictive for some, too lax for others.

Solution: Rules by risk profile (developers ≠ HR ≠ management).

Expected Results and KPIs to Track

A well-executed AI training program produces measurable results. Here are the indicators to follow and realistic 3-month objectives.

Key Takeaways

Training a team on AI while protecting data is not a one-time project. It's a continuous process based on:

1. A clear policy: Classify data, define authorized tools by risk level
2. Technical protection: Don't rely solely on human training
3. Contextual training: Just-in-Time rather than forgotten theoretical sessions

AI Act Art. 4 doesn't require hours of training. It requires "sufficient AI literacy". An alert at the right moment, documented, meets this requirement while preserving productivity.

References

• AI Act - Regulation (EU) 2024/1689 - European Artificial Intelligence Regulation
• GDPR - Regulation (EU) 2016/679 - General Data Protection Regulation
• CNIL - Artificial Intelligence - CNIL recommendations on AI
• ANSSI - French National Agency for Information Systems Security
• OWASP Top 10 for LLM Applications - Security framework for LLM applications