It’s important to highlight that AI is the simulation of human intelligence processes. Computer systems analyze huge datasets, collect insights on specific patterns, and then use this information to provide you with necessary results.
From design to content writing, from code generation to customer service - the AI-based solution always needs data input. And this is one of the main risks of AI technology.
Most big tech companies have databases with confidential information, including PII (Personally Identifiable Information) and IP (Intellectual Property). This can include data about their clients, technology information, contacts of providers and partners, etc. To make AI work effectively and integrate it into your processes, you need to share this information. As a result, this may lead to data leakage, as AI development companies get your confidential data stored on their servers.
Another reason to improve risk management while using AI is that employees can delegate their work to artificial intelligence. In this case, the situation gets out of your control. Business owners don’t know what information is shared and face commercial threats that can harm their reputation and processes in the company.
So, how to get benefits and avoid the risks of AI?
- Define what data can be shared with AI
Start with a data policy before anyone starts using AI tools. Classify information into what’s safe to share (public docs, generic code), what needs care (internal processes, anonymised data) and what must never leave your systems (customer personal data, credentials, source code under NDA, financials). Clear rules prevent most leaks, which usually come from well-meaning employees providing the wrong thing to an agent.
- Train employees to use AI safely and effectively
Tools don’t create value on their own. People do. Run short practical sessions on writing good prompts, recognising when AI output is wrong, and following your data policy. Teams that are trained get much better results and make far fewer risky mistakes than teams left to figure it out alone.
- Choose the right AI services
Not all AI tools treat your data the same way. Prefer business or enterprise plans that don’t use your data for training, offer data-retention controls, and support compliance needs such as GDPR, SOC 2 and data residency. Check where data is processed, read the terms, and keep a short approved list instead of letting every team pick its own tools.
- Set up an internal AI gateway for all models
If your scale allows it, route every AI request in your organnization through one internal gateway instead of letting each app call models directly. A gateway gives you one place to:
- enforce data rules automatically, for example masking personal data before it reaches a model
- log requests for audit
- control costs and usage limits per team
- switch between providers without changing your applications
It turns AI from scattered individual use into managed company infrastructure.
- Keep a human in the loop and review AI output
Treat AI output like work from a fast but junior colleague: useful, but it needs checking. Check licences and originality to avoid intellectual-property issues. For any customer-facing or business-critical decision, make sure a person reviews and signs off.
