DATA SECURITY AND PRIVACY IN THE AGE OF ARTIFICIAL INTELLIGENCE

Nowadays, artificial intelligence penetrates into different spheres of life and is a useful tool because it helps to automate processes, predict, analyze and make decisions. With each passing day, the increasing use of artificial intelligence in various fields leads to an increase in the amount of information collected. Which in turn carries risks and dangers that are associated with data security and privacy. Despite the fact that artificial intelligence has simplified human activities in many ways, when using it, it is necessary to comply with certain digital security rules in order to protect your personal data and prevent the risk of leaks and cyber attacks. This article describes in detail the methods of information protection, discusses the principles of digital protection. The study also raises the main types of cybersecurity violations that a user may be exposed to on the network. The final part of the work contains preventive measures that can help keep personal information from leaks.

АННОТАЦИЯ

Keywords: artificial intelligence, user authentication, cybersecurity, digital literacy, data security.

Ключевые слова: искусственный интеллект, аутентификация пользователей, обеспечение кибербезопасности, цифровая грамотность, безопасность данных.

Introduction

With the development of artificial intelligence and its integration into various aspects of life, there are risks of unauthorized access to any user's data [1,8]. The relevance of this topic is due to the development of artificial intelligence and data collection, which are the basis for the operation of this code system. Data integrity breaches can lead to serious consequences, including the theft of personal data, including biometric data, and the theft of information that can be used for extortion, fraud, etc. Furthermore, artificial intelligence can also be used for massive data collection about users.

Based on the above, it can be noted that the purpose of this research is to analyze the security and privacy issues in the era of artificial intelligence, as well as to develop recommendations for their elimination, resolution, and prevention, depending on their possible forecasting. To achieve this goal, the following tasks need to be addressed:

• Study the security and privacy threats to data.
• Conduct an analysis of problems related to data security and privacy.
• Develop recommendations to enhance data security and privacy.

All the aforementioned tasks need to be considered directly in the context of artificial intelligence. To solve these tasks, it is necessary to use methods of analysis of scientific literature, comparative analysis, and statistical analysis methods. The results of the research can be useful for developers of artificial intelligence systems, information security experts, and anyone interested in data security and privacy issues.

Types of Threats in the Use of Artificial Intelligence

It's important to note that the code of artificial intelligence itself is a dataset that analyzes and collects vast amounts of data, including confidential information. The main data security threats collected by artificial intelligence include:

- External Threats

- Internal Threats

- Artificial Intelligence Threats

External threats represent dangers from external factors, primarily from malicious actors who attempt to gain unauthorized access to user data. Examples of such attacks include hacking and phishing.

Internal threats pose a danger from within the organization and are mainly related to human factors. Typically, these are data leaks caused either by personnel errors or the creation of weak code that can lead to data errors during loading, resulting in data breaches.

It is also worth noting that the system (code) of artificial intelligence itself may contain vulnerabilities in its algorithms. Flaws in machine learning algorithms can lead to attacks on artificial intelligence model data.

A comprehensive approach to security should be adopted to minimize these threats and reduce the risk of data breaches. Among the measures that can be taken are staff training, updating, and testing artificial intelligence systems for vulnerabilities. One way to minimize data breach threats is to develop legislative and regulatory acts, laws that can regulate the collection and analysis of information, as well as protect data used by artificial intelligence [6].

Analysis of Data Breach Incidents

It is necessary to consider incidents of actual data breaches that were related to artificial intelligence. Analyzing data breaches will help identify vulnerabilities and draw important lessons for improving data security and privacy.

Table. 1 provides information on the latest major data breaches associated with the use of artificial intelligence. Here are a few examples of such cases:

Table 1.

Data Breach Incidents Involving Artificial Intelligence

This table illustrates some of the major data breaches involving artificial intelligence, showing the various ways in which AI-related vulnerabilities can lead to significant security incidents.

The examples highlighted above underscore the importance of a comprehensive approach to data security that includes technical, organizational, legislative, and other types of measures. Constant monitoring and adaptation should be key factors in minimizing such risks.

Impact of Artificial Intelligence on Data Privacy

It should also be noted that artificial intelligence significantly impacts data privacy because this code can process and analyze large arrays of various information [2]. Consequently, any artificial intelligence involved in data collection and analysis affects both the security and the privacy of user data. From this, it follows that this aspect entails certain consequences:

1. Improved Analytics: Artificial intelligence can recognize patterns in data, which helps in detecting and preventing fraud and other undesirable actions. However, this can also lead to deeper analysis of personal information without user consent.
2. Automated Decision-Making: Artificial intelligence can make automated decisions based on data, which speeds up processes and enhances efficiency. However, this can also limit personal choice and control over one's own data.
3. Profiling: Artificial intelligence can be used to create detailed user profiles, which can be useful for personalizing services. However, this can also lead to unwanted targeted advertising and loss of anonymity.
4. Security Risks: Artificial intelligence systems can be vulnerable to cyberattacks, which can lead to data breaches and privacy violations.

Considering the above consequences, conclusions can be drawn about how to minimize the negative impact of artificial intelligence on data privacy. Figure No. 1 illustrates possible ways to minimize the impact of artificial intelligence on data privacy:

• Implementing robust security measures: Strengthening the security protocols of AI systems to protect against breaches.
• Enhancing transparency: Users should be informed about how their data is used and processed by AI systems.
• Regulating AI applications: Developing clear regulations that govern AI data use, ensuring compliance with privacy laws.
• Consent mechanisms: Ensuring that users provide explicit consent for data collection and processing, particularly for sensitive information.
• Ethical guidelines: Adopting ethical guidelines for AI development and deployment to safeguard user privacy and ensure fair use of AI.

These strategies help balance the benefits of AI in data processing with the protection of individual privacy rights.

Figure No. 1. Ways to Minimize the Impact of Artificial Intelligence

It can be concluded that artificial intelligence offers numerous opportunities to enhance data processing.

Ethical Aspects of Using Artificial Intelligence

Issues related to confidentiality, fairness, transparency, accountability, and data management are becoming increasingly relevant in the context of artificial intelligence use.

Firstly, data confidentiality poses a significant challenge. In order for artificial intelligence to effectively analyze data without violating privacy rights, it is necessary to develop methods and mechanisms that protect personal information. It is important that artificial intelligence systems do not disclose personal data without the explicit consent of the users. This can be achieved by implementing anonymization techniques and other data protection methods.

Secondly, the fairness of artificial intelligence algorithms requires special attention. It is necessary to minimize the risk of bias and discrimination by testing algorithms for systemic errors that may lead to unfair treatment of people based on gender, race, age, or other social characteristics. To this end, diversity and inclusivity need to be incorporated into the development and testing process of artificial intelligence models, and algorithms should be regularly checked and calibrated to eliminate identified biases.

Furthermore, transparency in artificial intelligence processes is a key aspect of building user trust. Users should understand how their data is used and analyzed. This requires the development of mechanisms that can track and explain artificial intelligence decision-making processes. It is important to create accessible and understandable reports and documentation describing the logic and stages of algorithm operation.

It is also crucial to address the issue of accountability for decisions made with the help of artificial intelligence. It is necessary to clearly define who is responsible for the consequences of using artificial intelligence: developers, users, or other stakeholders. This requires a system to track and correct erroneous or harmful decisions made by artificial intelligence [4]. This may involve implementing feedback mechanisms, legal and ethical standards, as well as creating independent committees to review incidents related to the use of artificial intelligence.

Finally, data management requires comprehensive security measures to protect data from unauthorized access or leaks. These measures should include the use of modern encryption methods, regular vulnerability checks, and training users and employees on information security [6]. It is also important to develop and implement data management policies that will regulate access to data and its use.

To address all these issues, it is necessary to develop and implement ethical principles and standards regulating the use of artificial intelligence. Key elements of such standards include regulatory frameworks defining data usage rules; ethical codes setting behavior standards for developers and users of artificial intelligence; control and audit mechanisms ensuring compliance with ethical and legislative requirements; and educational programs raising awareness of the importance of ethical issues in the field of artificial intelligence. Thus, ethical aspects are an integral part of the development and implementation of artificial intelligence, and they must be carefully considered to ensure responsible use of technologies.

Ensuring Data Security and Privacy

Organizations can implement various measures to ensure data security and privacy. Developing and implementing a clear privacy policy is a primary step. Such a policy should clearly define what data is collected, how it is used, and how it is protected. This allows for the establishment of transparent data processing procedures and ensures compliance with user rights. Regular data security training for employees is necessary to increase their awareness of the importance of data protection. Employees should understand how to prevent information leaks and effectively respond to potential threats.

Limiting access to data based on the roles of employees within an organization helps minimize risks. This means that each employee has access only to the information necessary to perform their duties, reducing the likelihood of unauthorized access. Ensuring the physical security of premises where data is stored is critically important [5]. This includes access control, video surveillance, and other measures aimed at preventing unauthorized entry.

Applying strong encryption to protect data both during storage and in transit prevents unauthorized access. Encryption provides an additional layer of security, protecting data from cyber-attacks and leaks. Regularly creating backups of data is necessary to ensure their recovery in case of loss or damage. This helps avoid data losses and ensures their availability in critical situations.

Developing a security incident response plan includes notification procedures and recovery after breaches. This allows for a rapid response to incidents and minimizes their consequences. Conducting regular security audits and assessments helps identify and address vulnerabilities. [3] This contributes to maintaining a high level of data security and allows for timely updates to protective measures.

Implementing a device management policy, including encryption, remote data wiping, and locking in case of loss or theft, provides additional data protection on mobile devices and other media. Compliance with all applicable data protection laws and regulations, such as GDPR in the European Union, is mandatory. This helps ensure compliance with legal requirements and protects users' rights [7].

These measures help minimize risks related to data security and ensure their privacy in accordance with ethical standards and legislation.

Legal and Regulatory Aspects of Data Protection

An important aspect of data protection is the establishment of legal frameworks to ensure privacy and security of information when using artificial intelligence.

One of the most significant legal acts is the General Data Protection Regulation (GDPR), which applies within the European Union. GDPR sets strict requirements for the processing of EU citizens' personal data, including rights to access, correct, and delete data, as well as the mandatory obtaining of consent for its processing. This regulation ensures a high level of data protection and obliges organizations to implement appropriate measures to comply with these requirements.

The California Consumer Privacy Act (CCPA) is an important example of regional legislation in the United States. CCPA provides consumers with new rights regarding the use of their personal data, including the right to know what data is collected and the ability to prohibit its sale. This legislation emphasizes the need for transparency and control over personal data by users.

International data security standards, such as ISO/IEC 27001, provide frameworks for managing information security and help organizations protect information from unauthorized access. [3] These standards include requirements for security policies, risk management, and control measures necessary to ensure data protection on a global scale.

National legislation of various countries also plays a crucial role in data protection. Each country has its own laws and regulations concerning data protection, which may vary depending on the jurisdiction. Organizations need to consider these differences and adapt their policies in accordance with local requirements.

International agreements such as the Privacy Shield between the EU and the USA regulate the transfer of data between countries and continents. These agreements ensure compliance with data protection standards during cross-border data transfer, facilitating the protection of user privacy on an international level.

Industry standards also play a crucial role in data protection. For example, in the healthcare sector in the USA, the HIPAA law sets strict requirements for the protection of medical data. The financial sector also has specific standards and requirements for data protection that must be considered.

To comply with these legal and regulatory requirements, organizations need to develop and implement a data protection policy that conforms to all applicable laws and standards. Regular training for employees on data protection, monitoring, and auditing systems for security compliance, as well as the implementation of technical and organizational measures such as encryption, access management, and backup, are integral elements of this policy.

It is also important for organizations to keep abreast of changes in legislation and regulatory requirements to timely adapt their data protection policies and procedures. This will help ensure ongoing compliance with current requirements and minimize risks associated with data security and privacy.

Conclusion

Protecting data privacy and preventing discrimination in the use of artificial intelligence are crucial aspects that require not only strict adherence to rules and regulations but also the implementation of appropriate control mechanisms. Transparency and accountability in artificial intelligence processes also play a key role:

Users should have the ability to understand how their data is used, and decisions made based on artificial intelligence must be explainable and verifiable. Compliance with legislation such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is necessary to meet data protection requirements. Continuous training of employees and adaptation of data protection policies to the changing environment are integral elements of ensuring security. Technical and organizational measures, such as data encryption and access management, are essential for effective information protection. Readiness for changes in legislation and technology is important for promptly responding to new threats and requirements. A comprehensive approach to the use of artificial intelligence, incorporating technical, ethical, legal, and educational aspects, is necessary to ensure fair and responsible use of technology and data protection at all levels.

Possible directions for future research and development in the field of security and privacy in artificial intelligence systems represent a broad spectrum of aspects that attract the attention of both the academic and industrial communities.

One of the main directions is the enhancement of cryptographic methods, which includes the development of new and improvement of existing technologies to ensure more robust data protection. Another important direction is the expansion of blockchain technology applications, which can enhance transparency and reliability of data storage through the creation of decentralized and tamper-proof systems for data recording.

Federated learning is becoming increasingly significant in the field of data privacy, allowing data processing directly on user devices and minimizing the risks of centralized information storage. Improving algorithms for differential privacy is also crucial for ensuring data confidentiality, and research into homomorphic encryption could expand the possibilities for computations over encrypted data.

Other directions include the development of regulatory sandboxes for testing new technologies without violating data security and legal compliance. Creating tools for the automatic auditing of artificial intelligence systems and integrating ethical principles into algorithms also require further research.

International cooperation in developing unified standards and agreements for data protection plays a key role in ensuring security and privacy on a global scale. Additionally, researching the impact of artificial intelligence on society and developing measures to minimize its negative effects are integral parts of sustainable development of artificial intelligence technologies.

For organizations aiming to improve data protection when using artificial intelligence systems, it is advisable to adopt several practical measures.

Firstly, developing and implementing a privacy policy that clearly defines data collection, usage, access, and protection is essential. Regular training and workshops for employees on data protection and the use of artificial intelligence will also help to increase staff awareness and competence.

An important step is to minimize data collection, gathering only the necessary information. Encrypting data during storage and transmission, along with regular audits and monitoring of systems, will help prevent unauthorized access and identify potential vulnerabilities.

Limiting access to data and developing an incident response plan are also important for ensuring data security. Compliance with legislation such as GDPR, as well as using data anonymization and pseudonymization, will help reduce risks during data processing.

The integration of "data protection by design" and "data protection by default" principles at the early stages of system and product development should also be considered. Collaborating with security experts, ensuring technical support, and regularly updating software also play a crucial role in ensuring data security. Applying these recommendations will help organizations create a more robust data protection system and use artificial intelligence more responsibly and securely.