AI - data protection & data security

For readers in a hurry:

• The difference between data protection and data security: Data protection protects personal data against misuse and unauthorized access, while data security ensures the more comprehensive technical protection of all data against threats such as loss, damage and theft.
• Risks for data protection and data security: AI systems are dependent on large amounts of data, which entails risks such as data breaches, data corruption and inadequate data protection. These can significantly affect the accuracy and fairness of AI models.
• Optimization strategies: Measures such as data minimization, data encryption, strict access controls, data anonymization, regular security checks, transparency and user control are crucial to keeping data secure and protecting privacy.

Data protection vs. data security

With the rapid progress in the field of artificial intelligence (AI) and machine learning (ML), the need to ensure the security and protection of the data used within these systems is also growing. But what actually distinguishes data protection from data security?

Data protection refers to the protection of personal data against misuse and unauthorized access in order to safeguard the rights and privacy of the data subjects. This protection is regulated by laws such as the GDPR. Data security, on the other hand, involves protecting all data from threats such as loss, damage and theft, through technical and organizational measures such as firewalls and encryption. While both concepts aim to protect data, data protection focuses specifically on personal data and compliance with legal regulations. Data security, on the other hand, offers more comprehensive technical protection for all data.

Risks for data protection and data security

Data is at the heart of artificial intelligence. AI systems learn from the data with which they are trained and can constantly evolve. The information sent to the AI model with a user request can therefore not only be used to process and respond, but also to further improve the AI and its capabilities. However, this data dependency harbors potential risks, which significantly increases the need for reliable data security measures. As the following examples show, a data breach or manipulation can have serious consequences and significantly affect the accuracy, reliability and even fairness of AI models.

• Data breaches: Hackers can steal sensitive data that is used to train AI models. Personal information such as customer or financial data can fall into the wrong hands.
• Data falsification: Malicious actors can manipulate the training data to falsify the results of the AI model or deliberately steer it in the wrong direction. An example of this would be a facial recognition system that is trained with a dataset that contains more images of people of a certain ethnicity. This could lead to biased results as the system is less accurate or reliable in recognizing faces of other ethnic groups.
• Data protection concerns: AI systems often require large amounts of data, the use of which must comply with applicable data protection regulations. Are users sufficiently informed about how their data is used? Are effective techniques in place to anonymize this data?

Use cases and examples

Financial services: AI is used to detect fraud in credit card transactions. A data breach exposing customer information could have catastrophic consequences. (Reference: https://towardsdatascience.com/tagged/fraud-detection)

Healthcare: AI is used for medical diagnosis and treatment planning. Data breaches could expose sensitive patient data and manipulated data could lead to incorrect diagnoses. (Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/)

Autonomous vehicles: AI is used for self-driving cars. If sensor data from vehicles is hacked, safety can be seriously compromised. (Reference: https://appinventiv.com/blog/ai-in-self-driving-cars/)

Use cases and examples

Optimizing data protection and data security

Various factors play an important role in the optimization of data protection and data security. Some approaches to ensure the security and integrity of your data are presented below:

• Data minimization: Only collect the data that is required for the AI task. Less data reduces the attack surface.
• Data encryption: Encrypt data at rest and during transmission to prevent unauthorized access.
• Access controls: Implement strict access controls to restrict who can access and modify data.
• Data anonymization: Consider anonymizing data wherever possible to ensure data security and protect privacy.
• Regular security checks: Regularly check your AI systems for vulnerabilities and implement security patches.
• Transparency and user control: Make transparent how data is used in AI models and give users control over their data security.

Example from practice

Let's imagine the following scenario to illustrate the above data security measures in an application example:

A retail company wants to develop an AI system,

which gives customers product recommendations based on previous purchases.

Data minimization: Instead of recording all customer data (name, address, telephone number, etc.), the system concentrates on purchase history data (items purchased, quantity, date). This reduces the amount of sensitive data stored and reduces the attack surface for hackers.

Encryption of data: All customer purchase data, including anonymized data, is encrypted at rest (stored in databases) and during transmission (between systems) using secure algorithms. This makes it unreadable for unauthorized persons.

Access controls: Only authorized personnel (data scientist, security expert) are granted access to customer data and the AI model. Multi-factor authentication and role-based access controls ensure that only the required level of access is granted.

Anonymization of data: Although not always possible in this scenario (recommendations require some user identification), the system could consider anonymizing purchase data by removing customer names and replacing them with unique identifiers.

Regular security checks: The company conducts regular penetration tests to identify vulnerabilities in the AI system and data storage infrastructure. Security patches are implemented immediately to eliminate any risks identified.

Transparency and user control: The company clearly communicates in its privacy policy how customers' purchase data is used for AI-supported recommendations. Customers are given control over their data and have the option of refusing data collection or requesting the deletion of their data.

Conclusion

Data protection and data security are of central importance when using AI. The proper handling of sensitive data is not only a technical obligation, but also an ethical one. Companies must ensure that their AI systems comply with applicable data protection laws, such as the GDPR in Europe. An integrated approach from the outset not only ensures compliance, but also user trust in these technologies.

By implementing solid security measures and following best practices, companies can minimize potential risks and ensure the integrity of their AI models. Want to learn more about the regulations and uses of AI systems? Take a look at our blog posts such as "AI agents - intelligent helpers" or "Chatbots - development and practical examples". Of course, we are also available for a non-binding personal consultation via our contact page.

About Business Automatica GmbH:

Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.