Context and why it matters for SMEs and mid-market firms in the Baltics and EU
Artificial intelligence is now part of everyday work. Knowledge workers use language models such as ChatGPT, Google Gemini and Microsoft Copilot to draft emails, summarise documents and speed up research. Developers use code assistants. Analysts automate reporting. These are legitimate, valuable use cases that compress cycle times and reduce costs.
The risk appears when internal material is copied into a prompt without thinking. Public AI models run on external servers. If the prompt includes trade secrets, pricing strategies, source code or customer identifiers, the business may experience a data leak before anyone notices. The outcome can be more than embarrassment. It can breach contracts, harm trust and trigger regulatory scrutiny.
In Estonia and across the Baltics, firms often serve EU customers with strict privacy obligations. Boards want the gains of AI, but they expect the same rigour that applies to email, cloud storage and financial systems. The principle is simple. Use AI consciously, securely and purposefully, with a clear understanding of why you use it, how it works, and which data it touches.
What it is and what it is not
AI security for business is a defined set of policies, controls and behaviours that protects information when using artificial intelligence. It covers data classification, identity and access management, safe model selection, monitoring, Data Loss Prevention, and staff awareness. It ensures people know which AI tools are approved, which data types are allowed, and how to check outputs before they drive action.
It is not a single product. It is not a ban on innovation. It is an operating model for safe enablement. Your aim is to route the right task to the right AI based on the sensitivity of the data and the criticality of the decision.
There are many types of AI. Language models handle text. Multimodal models process text and files. Embedded assistants sit inside productivity suites. Vendor chatbots appear within support portals. Each pathway moves data differently. Businesses must understand what data they upload into these systems, and where that data flows.
Sensitive data, trade secrets and internal documents should never be shared with public AI models. Public tools involve transmission to external infrastructure that you do not control. Even if a provider offers opt-out settings, the safest posture is to treat public AI as an external party and restrict prompts to non-confidential content.
It is not only about business secrets. Poor use of AI can expose customers’ personal data, for example names, addresses, transaction details or ID numbers. That creates reputational harm and potential non-compliance with data protection policies. The right approach balances speed and safety so teams work faster without compromising privacy.
Typical weak points or failure modes
- Uploading confidential files to public AI tools.
- Combining customer identifiers with free text prompts.
- Using unmanaged personal accounts rather than enterprise identities.
- Assuming AI outputs are correct without review.
- No logging of prompts and responses.
- Lack of AI-aware DLP at the gateway or endpoint.
- Unclear ownership of AI policy, exceptions and incidents.
Tooling and process integration
You do not need to buy everything at once. Start with policy and awareness, then add controls where risk is highest. Useful categories include identity and access, AI gateways with DLP, endpoint agents that recognise sensitive content before it reaches the browser, cloud access security that identifies AI usage, configuration management for embedded assistants, and logging that routes events to your SIEM. Integrate these with incident response so alerts create tickets, owners are notified, and lessons learned feed back into policy.
For sensitive workloads, deploy on-premise or private AI. This means running models within your own environment or using a managed service with strict data isolation. Keep prompts and outputs within your control, and ensure encryption in transit and at rest with keys you manage. For teams that only need public content summarisation, a managed enterprise account with tenant controls may be sufficient.
Regional and regulatory considerations
In the EU, privacy expectations are high and customers expect evidence of control. Firms in Estonia and the wider Baltics often operate across borders, which means consistent policies are essential. Align AI usage with your existing data protection framework and security management system. Keep records that show what data is processed, which tools are used, where data is stored, and who has access. When in doubt, prefer private or on-premise AI for sensitive workloads and avoid exporting personal data to services outside your approved jurisdictions.
How Cybertex Security can help
We help organisations get the benefits of AI without losing control of their data. A focused Security Assessment identifies where AI is already in use, which data is at risk, and which controls will produce the highest impact. We set practical policies that staff can follow, design the data sharing rules, and recommend a phased control set that fits your size and sector. Where you need technical measures, we support Security Technology Selection and Implementation so you deploy AI-aware DLP and logging that integrates with your processes.
Ready to enable AI safely and confidently? Contact us via Contact.
