Have you ever wondered, what happens to the information you enter into ChatGPT or other AI toolsArtificial intelligence has become a part of our everyday lives – it helps us at work, suggests ideas, and can even write texts or analyze data for us. However, behind this convenience lie questions we rarely consider: is it safe to use AIAre our conversations with the model private and the data fully protected?
Sam Altman, one of the creators of ChatGPT, openly admitted that he doesn't treat a conversation with AI like a conversation with a lawyer or therapist. This warning shows that even industry leaders emphasize the lack of complete confidentiality in interactions with AI models. Many users, captivated by the technology's capabilities, are unaware that artificial intelligence analyzes and stores huge amounts of data, and some of them can be used for further model training.
In this article we will take a closer look AI security and what the privacy issue really looks like. We'll discuss the most important threats to users, we will explain, what are the risks associated with data transfer?, and we will also advise you, how to use AI safelyto protect your information. This will be a practical guide for anyone who wants to use this technology consciously – reliably and to the point.
Privacy and data security in the context of AI
When talking about artificial intelligence, the terms are often used interchangeably. privacy and data securityAlthough closely related, they represent different aspects of information protection. Privacy refers to the user's right to decide what data about themselves they share and for what purpose it can be used. Data security these are technical and organizational measures intended to protect this data against unauthorized access, loss or leakage.
In the case of AI tools, both of these issues become particularly important. Models such as ChatGPT and other AI-based systems they process huge amounts of text data, often coming from public internet sources, but also from user-entered content. Although AI providers declare they use security and anonymization, it is worth remembering that information sent to the model can be used to further improve it.
This raises questions about the risk of using AI in the context of sensitive data – for example, financial, medical, or legal information. Unlike a conversation with a lawyer or doctor, interaction with an AI model is not covered by professional secrecy. This means that when providing the tool with specific data, the user should consciously assess whether they are prepared for this information to be processed to some extent or stored in system logs.
Discussion about privacy and artificial intelligence is becoming increasingly important as technology develops. AI is entering new areas of life – from education, through business, to medicine. Therefore, it is necessary to distinguish between what data we want reveal to AI models and how effectively they are protected by service providers.
Main risks and pitfalls when using AI
While artificial intelligence tools offer enormous potential, their use poses significant risks to privacy, security, and information integrity. Understanding these risks is crucial to use consciously from technology and avoid reckless actions.
Conversations with AI as entrusting confidential information
One of the most common user mistakes is treating AI like a trusted advisor—a lawyer, accountant, or psychologist. Many people include AI in models like ChatGPT sensitive personal data, information about problems at the company, and even contract numbers and salary amounts. Meanwhile, Sam Altman, director of OpenAI, clearly emphasized that talking to AI is not covered by professional secrecy.
If a user provides confidential data in chat, it may be recorded in system logs or used to improve model quality. Although providers claim to anonymize and filter data, there is still a risk of accidental disclosure. An example is an incident in 2023 when, due to a bug in the ChatGPT system, some users had temporary access to other users' chat histories.
Leaks and improper processing of personal data
AI models are trained on massive datasets, which may also contain personal information. There is a risk of so-called model inversion attack – an attack that involves "recreating" portions of training data based on the model's performance. This means that under certain conditions, the AI could inadvertently reveal the information it was trained on.
In 2024, researchers from Google and OpenAI highlighted the possibility of AI models extracting information from seemingly forgotten training data. Such security flaws raise questions about compliance with GDPR regulations, which require full control over personal data.
Incorrect or biased answers
Artificial intelligence is not infallible. Models can generate answers incorrect, outdated or biased – especially in political, social, and legal topics. This stems from the fact that AI relies on patterns in data, not on "understanding" the topic.
For example, a chatbot provided a user with incorrect medical information, suggesting incorrect medication dosages. Such cases have already been reported and have prompted warnings from experts not to rely on AI as a source of reliable and definitive advice on health or legal matters.
Manipulations and attacks on models
AI systems can be vulnerable to various types of attacks, including:
• adversarial attacks – entering specially prepared data that deliberately “cheat” the model, causing it to malfunction,
• data poisoning – “poisoning” training data so that the model generates erroneous or harmful content,
• prompt injection – manipulating commands in such a way that the AI starts to perform undesirable actions, e.g. revealing confidential information about its activities.
In 2023, researchers demonstrated that it is possible to create "malicious prompts" that cause chatbots to provide unsafe responses and even provide portions of their internal training data.
Overreliance on technology
Many users treat AI responses as entirely trustworthy, forgetting that they are generated based on the probability of specific words occurring, rather than a reliable analysis of the facts. Overreliance on AI for decision-making—especially in business, legal, or financial matters—can lead to costly mistakes.
A 2024 Gartner report predicts that by 2026, approximately 30% of data incidents in companies will be the result of employees misusing AI tools.
These risks show that Using AI requires a conscious approach and critical thinking.
What are the consequences of real threats (for companies)
Using artificial intelligence tools without understanding the risks can lead to serious consequences – for both individuals and companies. AI security have legal, financial and image dimensions.
Violation of personal data protection regulations
In the European Union, all entities processing personal data must comply with the rules GDPRProviding an AI model with information about employees, customers, or contractors without their consent can be considered a violation of the law. In 2023, the Italian data protection authority temporarily blocked ChatGPT, citing a lack of clear information about how user data was processed.
For companies, this means a real risk of financial penalties, which in the case of a serious violation can reach up to 20 million euros or 4% annual turnover – whichever amount is higher.
Loss of confidential information and company secrets
Feeding data related to business strategy, projects, or contracts into an AI tool can result in uncontrolled disclosure. Even if information is theoretically anonymized, there's always the risk of it being later used to improve the model or accidentally leaked.
In 2023, several major companies, including Samsung, implemented internal bans on using ChatGPT at work after employees unknowingly shared sensitive project data in AI chats.
Financial losses resulting from wrong decisions
Relying on artificial intelligence to make business or financial decisions without verifying information can lead to significant losses. AI models lack context awareness and are not responsible for the consequences of their responses. In the event of erroneous recommendations, the consequences are always borne by the user, not the tool provider.
For example, in the US, there have been cases where law firm employees cited fictitious rulings generated by AI, resulting in financial penalties and reputational damage.
Damage to reputation and loss of customer trust
Data leaks or irresponsible use of AI can lead to a loss of trust among customers and business partners. In the digital age, information about security incidents spreads rapidly, and the reputational consequences can be more severe than the financial penalty itself.
A 2024 Cisco study found that 76% of consumers declare distrust in companies that cannot clearly explain how they use data in the context of AITransparency therefore becomes crucial for building credibility.
Legal and ethical issues
The development of artificial intelligence often outpaces legal regulations. As a result, users and companies may find themselves in a so-called "gray zone" where not all activities are clearly regulated. EU AI Act, which will come into force in the coming years, will introduce specific requirements for high-risk systems, including in terms of transparency of operations, human oversight and assessment of the impact on fundamental rights.
Improper use of AI – even if it is not yet formally prohibited – may give rise to serious ethical dilemmas, especially in the context of content manipulation, generating disinformation or infringing other people's copyrights.
For companies, these consequences may be particularly expensive. Customer data leaks, GDPR violations, or incorrect decisions based on AI responses can lead not only to financial penalties, but also loss of trust customers and business partners. Therefore, every organization should consciously implement rules for using artificial intelligence tools and training employees in data protection.
The consequences of threats show that Conscious use of AI requires not only knowledge of the technology, but also the principles of data protection and legal responsibility.
Legal framework and regulations – what you need to know
The development of artificial intelligence requires appropriate legal regulations to protect users and ensure the transparency of AI systems. Several key legal acts and initiatives have emerged in recent years that are worth knowing about.
In the European Union, the basis for personal data protection remains GDPRIt regulates, among other things, the principles of collecting, processing, and storing personal data. If a user provides an AI model with personal information, the entity providing the tool becomes a data controller or processor and must comply with the GDPR requirements.
The second important document is EU AI Act, which will enter into force in stages from 2026. This is the world's first comprehensive regulation on artificial intelligence. It introduces risk categories for AI systems and imposes obligations on providers and users, particularly for systems deemed high-risk—for example, those used in recruitment, medicine, or education. The provisions include requirements for transparency, human oversight, and fundamental rights impact assessments.
Outside the EU, other countries are taking similar initiatives. The United States is relying on guidance from the White House and recommendations from the Federal Trade Commission, while China has regulations governing AI-generated content and the liability of providers for damage caused by their systems.
For users this means that Responsible use of AI requires knowledge of basic data protection principlesCompanies should develop internal policies for using AI, and individuals should be aware that regulations will increasingly govern how these tools are used.
Regulations are not intended to hinder the development of technology, but ensuring security, transparency and accountability in the use of artificial intelligence.
Best practices and methods for data protection
Consciously using artificial intelligence tools requires adherence to basic security and privacy principles. These principles can help reduce the risk of unwanted disclosure and avoid legal and reputational consequences.
Limit the amount of data transferred
The principle of data minimization is key – it is worth avoiding administration AI tools contain personal information, financial data, and confidential company documents. Even if the provider declares anonymization, this does not guarantee complete privacy.
Use tools with a clear privacy policy
It's worth choosing solutions that transparently disclose how they process user data. A reliable provider should clearly indicate whether data is saved, how long it is stored, and whether it can be used for model training.
Implement the "privacy by design" principle
Companies using AI should design processes to protect privacy from the outset, including through data anonymization, pseudonymization, and encryption. It's also important to establish internal policies for employee use of AI tools.
Educate employees and users
Lack of awareness is one of the leading causes of data incidents. Regular training in secure AI use helps prevent sensitive information from inadvertently entering models.
Monitor your use of AI tools
Companies should maintain control over how employees use AI-based tools. For larger organizations, solutions that enable the creation of internal, secure AI models running within the company's infrastructure can be helpful.
Be critical of AI responses
AI can generate erroneous or fabricated content (so-called hallucinations). Therefore, each response should be treated as a starting point, not a final decision. Verifying information with trusted sources is essential, especially in legal, medical, and financial matters.
Applying these principles allows you to limit the risks of using artificial intelligence and allows these tools to be used more consciously and responsibly.
Technical security
In addition to a responsible approach by users and clear privacy policies, the use of technical solutions, which minimize the risks associated with data processing in artificial intelligence systems.
Data encryption
One of the basic protection mechanisms is data encryption at rest and in motionThis ensures that even if the transmission is intercepted, the information remains unreadable to unauthorized persons.
Access control
Access to AI tools in companies should be limited to those who truly need them. Using multi-factor authentication (MFA) and granting permissions based on the principle of least privilege increases security.
Environment isolation – AI sandboxes
Many companies use the so-called sandbox environments, which allow AI testing in an isolated environment, without the risk of unauthorized access to production data. This allows for the secure testing of new features or models.
Security audits
Regular tests and audits They allow you to assess whether the model and its infrastructure are adequately secured. This applies not only to the AI itself, but also to servers, APIs, and integration systems with other applications.
Limiting data storage
Responsible tool providers should have policies in place that limit how long data is retained. Users should check whether they have the option to delete their chat history or disable it altogether.
Applying the above safeguards doesn't completely eliminate risk, but it significantly reduces it. Implementing encryption, access controls, and regular audits, combined with the informed use of artificial intelligence, creates a solid foundation for the safe use of this technology.
Future challenges and trends
Artificial intelligence is developing at an incredibly rapid pace, and with it comes new challenges in terms of security and privacy. Many institutions are already warning that current regulations may not keep pace with the rapid development of technology.
The increasing role of regulation
In the coming years, regulations such as: EU AI Act, which will introduce stringent requirements for systems deemed high-risk. Similar regulations are being developed in the US, UK, and Asia. The goal is to ensure transparency, accountability, and minimize the risks associated with AI.
Development of attacks on AI models
With the growing popularity of AI-based tools, the number of threats is also growing. Researchers are already pointing to the development adversarial attacks, model poisoning Whether prompt injection, which may lead to data theft, content manipulation or generating malicious responses.
The growing importance of ethics and trust in AI
Technology companies are increasingly emphasizing the need to build "trusted models" (trusted AI) – those that are transparent, verifiable, and ethical. User trust will become a key competitive factor for AI tool providers.
User awareness is the key to security
Even the best regulations and technical safeguards will not replace responsible use of technology. In the future, it will play an increasingly important role. user education, both among individuals and companies. A conscious approach to data protection and critical thinking will become essential in a world where AI will be present in almost every industry.
The coming years will show how effectively we can combine technological development with privacy and security protection.
Frequently asked questions
What are the biggest privacy threats in AI?
Does AI remember my personal data?
How can I protect my data when using AI?
Can I completely trust AI answers?
Can the use of AI violate the law?
Is my conversation with AI private?
Artificial intelligence offers enormous possibilities, but its use poses real challenges regarding privacy and data security. When using it, it's important to remember that conversations with the model are not confidential, and the information entered may be processed and stored by the provider.
A conscious approach is key – avoiding the disclosure of sensitive data, using tools with transparent rules, and verifying responses with other sources. As technology advances, legal regulations and ethical standards play an increasingly important role, but security always starts with the user.
Using AI tools responsibly and critically is the best way to reap their benefits while minimizing privacy and data protection risks.
English 
Polski