How to secure AI use in the enterprise

Discovering what AI tools the organisation actually uses

The first step is to create a complete inventory of every AI service the organisation accesses or that employees use on their own initiative. It is common for the marketing department to pay for a text generation licence, developers to use coding assistants and administrative staff to upload contracts to a public chatbot for summarisation. Review invoices, question team leads and examine network traffic destined for known AI provider domains. The output must be a list that records the tool name, vendor, purpose of use, type of data processed and the process owner.

Distinguishing data that may enter AI from data that must not

Data classification determines which information is allowed to leave the controlled environment. Divide data into at least three levels: public (website materials, press releases), internal (processes, methodologies, code without secret keys) and confidential or regulated (personal data under GDPR, trade secrets, health data, payment cards). For each level define whether processing in a public cloud is permitted, whether only a private instance may be used or whether processing is prohibited entirely. A typical example: a Czech e‑shop company must not upload orders containing customer addresses and e‑mails to a public interface for trend analysis unless the data has been anonymised.

Establishing an approved tool list and access rules

Based on the inventory and data classification, create a whitelist of permitted services with assigned data levels. Block access to unsanctioned AI domains at the firewall or proxy. For approved tools enforce single sign‑on (SSO) and require multi‑factor authentication. Define who may request a new tool: the process owner submits the request, the security team assesses risk and IT deploys. In practice this means a corporate GPT instance in Azure is approved for internal code and marketing texts, while public ChatGPT is blocked for all accounts using a corporate e‑mail address.

Preparing people for safe AI use

Technical controls fail if employees do not understand why they exist. Deliver role‑specific training: show developers the risks of inserting secrets into prompts, explain to HR why uploading CVs to public models is forbidden, demonstrate to managers how to anonymise data correctly before analysis. Training must include concrete examples of bad and good prompts, rules for reporting incidents such as the accidental submission of a contract, and a test that verifies understanding. Repeat training at least once a year or after any major policy change.

Monitoring compliance and responding to incidents

Monitoring is not a one‑off action but a routine process. Regularly, monthly or quarterly, download access logs for approved AI services and compare them with expected usage. Look for anomalies: unusual data volumes, access outside normal hours, attempts to upload files with .pdf, .docx or .csv extensions to unknown domains. Conduct an internal audit at least once a year: verify that the whitelist matches reality, that the data classification remains valid and that incidents have been properly resolved. Document the audit results and update the policy accordingly.

What this means

Secure AI use in the enterprise is not about prohibition but about controlled enablement. When the organisation knows its tools, understands what data may enter them, provides approved alternatives, trains its people and watches what happens, it gains the benefits of AI without losing control of its data.