How to Build a Data Security Strategy for AI

If you are wondering how to build a data security strategy for AI, this week gave you a loud reminder of why. Proofpoint agreed to acquire Varonis for $6.3 billion, a deal that pairs human-focused threat protection with a company known for finding and locking down sensitive data.

The message behind the deal is simple. As companies feed more internal documents, emails, and customer records into AI tools, the question shifts from “who can log in?” to “what data can this system see, and where can it send it?” Buyers are paying serious money for answers. You do not need a multibillion-dollar budget to start, though. You need a clear process. Here is one you can run in a few weeks, whether you are a 50-person firm in Berlin or a 500-person company in Lahore serving European clients.

What You Need Before You Start

Prepare a short list of where your data lives: cloud storage (SharePoint, Google Drive, S3), databases, SaaS apps, and laptops. Identify who owns each system. You also need a rough list of the AI tools in use, such as Microsoft Copilot, ChatGPT Enterprise, internal chatbots, or custom models. Add a legal contact or a consultant who understands GDPR and, if relevant, the EU AI Act.

Block out time with three people: someone from IT or security, someone from legal or compliance, and a business leader who understands which data really matters. Strategy built by IT alone usually protects the wrong things. Useful references include the GDPR overview and the ENISA guidance on cybersecurity in Europe.

Step 1: Discover and Classify Your Data

You cannot protect what you cannot find. Run a discovery scan across your main repositories to locate personal data, financial records, contracts, source code, and trade secrets. Commercial tools in the data security posture management category do this automatically, which is exactly the space Varonis operates in. Smaller teams can start with built-in options such as Microsoft Purview or Google Cloud DLP.

Then classify. Keep it to four labels so people actually use them: Public, Internal, Confidential, and Restricted. Define each in a sentence with examples. “Restricted” might cover customer personal data, payment details, health records, and unreleased product plans. Apply labels automatically where you can and require them on new documents.

Expect surprises. Most first scans find old shared folders open to the whole company, files sitting in personal drives, and customer data copied into spreadsheets. Write down what you find. This list becomes your priority queue.

Step 2: Clean Up Access Before You Connect AI

AI assistants inherit the permissions of the people using them. If an intern can technically open the salary spreadsheet, an AI assistant working for that intern can summarize it. That is the single most common AI data leak, and it is entirely an access problem.

Before rolling out or expanding any AI tool, review permissions on your most sensitive repositories. Remove “everyone” and “anyone with the link” sharing on Confidential and Restricted data. Apply least privilege: people get the access their job needs, no more. Set up regular access reviews, quarterly for Restricted data, and ask data owners to approve or remove each user. Where possible, use groups and roles rather than individual grants so reviews stay manageable.

Step 3: Set Clear Rules for What AI Can See and Do

Write a short AI data policy, two pages at most. It should answer: which AI tools are approved, which data classes may be used with each, what is forbidden, and who to ask about exceptions. For example, public and internal data may be used with approved enterprise AI tools, while Restricted data may only be used with systems you host or have a signed data processing agreement for.

Check vendor terms carefully. Confirm whether your prompts and files are used to train models, where data is stored and processed, how long it is retained, and whether the vendor is a GDPR-compliant processor. Under GDPR you remain responsible for personal data even when a vendor processes it. For high-risk uses, consider whether an EU AI Act obligation applies and document your assessment.

Add technical guardrails to back up the policy: data loss prevention rules that block Restricted data from being pasted into unapproved tools, network controls on unsanctioned AI sites, and prompt logging for approved tools.

Step 4: Monitor, Test, and Prepare to Respond

Strategy without monitoring is a document. Turn on audit logs for your AI tools and data platforms and send them to a central place. Watch for unusual patterns: large downloads, mass file access, or sensitive documents suddenly appearing in AI queries.

Test your setup. Ask a staff account with normal permissions to query your AI assistant about salaries, board minutes, or customer lists. If it returns anything it should not, you have found a real gap before an attacker did. Repeat after major changes.

Finally, update your incident response plan. GDPR requires notifying the supervisory authority within 72 hours of becoming aware of a qualifying personal data breach. Decide in advance who investigates, who decides whether notification is required, and who talks to customers. Run a tabletop exercise once a year, with an AI-related scenario included.

Common Mistakes to Avoid

Buying a tool before fixing the basics. Software will not rescue you from messy permissions. Do the cleanup in Step 2 first, then let tools maintain it.

Writing a policy nobody can follow. If the approved tools are slow or limited, employees will use personal accounts. Make the safe option the easy option.

Forgetting third parties. Contractors, agencies, and SaaS vendors often hold copies of your data. Include them in your inventory and contracts.

Key Takeaways

• Find first: Discover and classify data before deciding what to protect.
• Access is the real risk: AI exposes whatever permissions already allow, so tighten them before rollout.
• Simple policy: Two pages, four data labels, clear tool approvals.
• Know your obligations: GDPR duties and the 72-hour breach window apply to AI-related incidents too.
• Test regularly: A simple permissions test on your AI assistant reveals gaps fast.

Need Expert Help?

If this feels like a lot to manage alone, TecniForge can handle the heavy lifting. Our team specializes in custom software development and AI integration, including secure data pipelines and access controls for AI projects. Get in touch with our experts.

Also read: The Proofpoint and Varonis Deal and AI Data Security: our earlier coverage on why this matters today.

Your challenge: pick one repository this week, run a permissions check, and remove one “everyone” share. It takes an hour and closes a real door.