Where does AI get its data?

Artificial intelligence - from virtual assistants to recommendation engines on Netflix - has become a part of our daily lives. It creates text, generates graphics, and even helps programmers write code. But have you ever wondered where AI gets its “knowledge” from? How does it know which song to recommend or how to answer a complex question?

The answer is simple yet complex: data. AI is insatiable when it comes to information - it’s its fuel, without which it couldn’t function. In this article, we’ll take a behind-the-scenes look at where AI gets its data, how it learns from it, and the risks and regulations involved in the process.

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Main sources of data – What feeds AI?

To function effectively, AI models need massive and diverse datasets. Think of them as a library from which AI draws knowledge about the world. These datasets come from many sources and vary widely in nature. Key sources include:

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Public data. These are openly available sources, such as government databases (e.g., demographic statistics), scientific archives, weather data, or datasets shared by organizations for research purposes.

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Private data. Generated by all of us every day. This includes social media activity, e-commerce transaction history, data from mobile apps, or smart devices (IoT). These datasets enable personalization of services.

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Synthetic data. Computer-generated data that mimics the properties of real-world information. It is created to supplement existing datasets, test models in a safe environment, or avoid privacy issues.

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Sensor data. Real-time information collected by sensors, such as cameras in autonomous vehicles, medical devices monitoring patients, or machines in factories.

AI excels in situations where data is too complex or voluminous for manual analysis. AI-based systems can combine information from multiple sources - CRMs, ERP systems, social media, or market data - to deliver actionable insights and recommendations.

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How AI learns from data: The training process

Having data is just the beginning. For AI to use it, it must go through a process called “training.” Imagine teaching a child to recognize animals by showing thousands of pictures of dogs, cats, and birds. AI works similarly.

During training, the model is presented with large sets of input data (e.g., images) along with expected outcomes (labels, e.g., “dog”). Using advanced machine learning algorithms, the system identifies patterns - like features common to all dogs - and learns to recognize them. The more diverse and high-quality data it receives, the more accurate its responses will be.

This process enables automation of analytical tasks and efficient processing of vast amounts of information, forming the foundation of modern business solutions.

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Data under the microscope – Privacy, security, and risks

Since AI uses so much data, including private information, concerns about security naturally arise. This is one of the biggest challenges in modern technology.

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Are AI datasets anonymous?
Not always. They often contain information that could identify specific individuals. That’s why proper protection is critical. Uncontrolled use of public AI tools by employees can lead to what’s called Shadow AI, where confidential company data is fed into external chatbots, creating a high risk of leaks and loss of intellectual property control.

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How can data security be ensured?
Privacy protection is the cornerstone of user trust. Key techniques and practices include:

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• Anonymization and pseudonymization. Modifying data to make it impossible or extremely difficult to identify the individuals involved.
  
  
• Encryption. Encoding data so that it is unreadable to unauthorized parties.
  
  
• Data minimization. Collecting and processing only the information absolutely necessary for a specific purpose.
  
  
• Secure infrastructure. Building Data & AI solutions in controlled environments (e.g., private clouds) ensures sensitive data never leaves the organization.
  
  

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A responsible and transparent approach to data is essential for maintaining trust and acting ethically. Privacy protection is now a top priority in AI.

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Legal framework – Who regulates AI data?

The tech world is not a “wild west.” Data processing, especially in the context of AI, is strictly regulated. In the European Union, three key regulations apply:

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• GDPR. The General Data Protection Regulation sets rules for personal data processing and grants users rights over their information.
  
  
• AI Act. Regulates AI systems according to risk level and introduces specific requirements for safety, transparency, and compliance with fundamental rights.
  
  
• Data Act. Governs access to data generated by internet-connected devices and its reuse.
  
  

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Navigating these rules can be complex, which is why many companies consult AI experts to ensure their solutions are fully compliant.

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Data comes with responsibility

Data is the driving force of the AI revolution, opening doors to innovations we could only dream of until recently. But with great power comes great responsibility. Understanding where data comes from, how it’s processed, and the regulations governing it is crucial for anyone using or implementing AI solutions.

The real challenge lies not just in collecting data, but in using it wisely, safely, and ethically to create real value - for businesses and for people.