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The Impact of the GDPR on Artificial Intelligence

By Anas Baig | Reviewed By Maria Khan
Published September 29, 2023 / Updated March 10, 2024

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The European Union’s (EU) General Data Protection Regulation (GDPR) has emerged as a significant legal framework governing data privacy and protection. As the use of Artificial Intelligence (AI) continues to expand across industries, the impact of GDPR on AI has become increasingly relevant.

The inception of AI dates back to the 1950s when researchers and scientists envisioned the possibility of creating machines that could simulate human intelligence. Fast forward to 2023, groundbreaking developments in AI over the past decade have made AI an integral part of our modern society, impacting numerous industries, from healthcare and finance to transportation and entertainment.

While AI brings several opportunities, it also raises significant concerns due to its excessive reliance on data. Therefore, it becomes necessary to assess the use and operation of AI systems in light of the requirements of data privacy laws. Data privacy laws regulate the data lifecycle, including data collection and consent, data minimization and purpose limitation, transparency, algorithm bias and discrimination, and the security and protection of an individual’s data.

Regarding the impact of GDPR on AI, on 25 June 2020, the European Parliament published a study addressing the relationship between the GDPR and AI. The study analyzed how AI is regulated within the GDPR and examined the extent to which AI fits into the GDPR’s conceptual framework. The study's findings emphasize that while the GDPR can be used to regulate AI, it does not give controllers enough direction, and its prescriptions need to be expanded and concretized.

In response to the European Parliament’s initial study, the EU initiated the development of rules on AI with its proposed EU AI Act. The AI Act is one of the first global comprehensive AI laws proposed to regulate the development and use of AI systems. The proposed law seeks to ensure that AI systems used in the EU are transparent, reliable, and safe and that they respect fundamental rights and values.

The Intersection between GDPR and AI

Since its enactment on May 25, 2018, the GDPR has given individuals greater control over their personal data and established guidelines for how organizations should collect, process, and store data. As AI systems often rely on processing massive amounts of data, including personal data, to learn and enhance their performance, therefore, when designing and implementing AI systems, GDPR principles, rights, and provisions become crucial:

Lawful Basis for Data Processing

Companies developing or using AI should determine whether they process personal data, and if so, what legal basis is applicable for their processing activities. They should comply with all requirements in relation to the legal basis they rely on. For example, if they rely on consent, they must ensure that the consent is free, informed, specific, and unambiguous.

Data Minimization and Purpose Limitation

AI developers and systems should collect and process only such personal data as is necessary to accomplish their intended purposes. In addition, the processing of personal data must be restricted to necessary purposes and only be done for legitimate, explicit, and specified purposes.

Anonymization and Pseudonymization

The GDPR emphasizes using anonymization and pseudonymization techniques to safeguard personal data and enhance an individual’s privacy. The GDPR does not regard anonymized data as personal data. Pseudonymization lowers the risk of re-identifying personal data. However, pseudonymized data is still considered personal data. Anonymization and pseudonymization are essential techniques in relation to the operation of AI systems that process personal data.

Accuracy and Storage Limitation

AI systems processing the personal data of individuals must maintain accurate and up-to-date records of such personal data and not retain it for longer than necessary.

Right to Information regarding Automated Decision-Making

GDPR mandates organizations making decisions based solely on automated processing that produce legal or similarly significant effects for data subjects to inform them of such activity, provide meaningful information about the logic involved, and explain the significance and envisaged consequences of the processing. The information should be specific and easily accessible.

Privacy by Design and Privacy by Default

The GDPR strongly emphasizes including data protection measures in AI systems from the design stage and throughout the lifecycle. Organizations must incorporate privacy-preserving technologies and default configurations to guarantee data security and privacy in AI applications.

Data Protection Impact Assessments (DPIAs)

According to Article 35 of the GDPR, organizations must undertake DPIAs for AI applications that pose a significant threat to the rights and freedoms of individuals. Before deploying AI systems, these assessments assist in identifying and mitigating potential data protection risks.

Security and Accountability

Organizations must be responsible for the data processing by their AI systems and ensure that AI applications handling personal data are equipped with security algorithms to safeguard the personal data. Further, such organizations should also adopt other appropriate technical and organizational measures in line with the nature of the risk posed by their processing activities.

Cross-Border Data Transfers

GDPR emphasizes ensuring appropriate safeguards for cross-border transfers of personal data. Therefore, organizations developing and using AI systems need to ensure that if they transfer any personal data internationally, they have sufficient controls in place, such as Binding Corporate Rules (BCRs) or Standard Contractual Clauses (SCCs).

Rights of Individuals

AI systems must respect and adhere to the data subject rights granted under GDPR, such as the right to access, the right to rectification, the right to erasure, the right to restrict processing, the right to data portability, and the right to object. The GDPR also prohibits subjecting individuals to automated decision-making unless one of the specified exceptions applies, i.e. contractual, explicit consent, and legal authorization.

AI is evolving rapidly, which underscores the importance of organizations understanding the implications of the usage of AI on data processing while ensuring compliance with GDPR.

Article 6 and Article 7 of the GDPR mandate that organizations must have a lawful basis for processing personal data. Consent is one of the lawful bases for processing personal data. Consent must be free, specific, informed, and an unambiguous indication of the data subject’s wishes. Additionally, organizations should provide individuals the right to withdraw their consent at any time.

If an organization developing or using AI processes personal data and relies on consent as the legal basis for such processing, it should ensure that it provides the data subjects with sufficient information regarding the processing as per the GDPR and obtains valid consent.


According to Articles 12, 13, and 14 of GDPR, which address transparency and the right to information, businesses must inform individuals about how their personal data is processed in a clear, understandable, and easily accessible manner. This includes informing data subjects on the use of automated decision-making, such as profiling, and the logic behind it. Organizations should be open and honest about how AI is used, how the personal data of individuals is processed, the importance of AI-driven decisions, and any dangers or side effects that might be involved.

The Complexity of AI Algorithms

Deep learning algorithms, in particular, can be quite complicated and function as "black boxes." It is difficult to provide people with a clear and understandable description of the complex layers of computations and transformations that take place within these algorithms.

AI models continuously update and learn, adapting to new environments and data. Due to the model's dynamic nature, it can be difficult to provide explanations because the model's behavior may change over time, and earlier ones may become outdated.

Some AI models and algorithms are protected by intellectual property laws and are developed using proprietary methods. Concerns about losing competitive advantage may arise if these models' inner workings are disclosed.

AI systems have the capacity to learn from biased input and make possibly biased decisions. Ethical issues can arise when attempting to justify choices made with biased data. Thus, this process must be handled carefully. When providing information about the processing of personal data by AI systems, organizations should consider the foregoing factors and provide accurate and easily understandable information to the data subjects.

B. Data Minimization and Purpose Limitation

Article 5(1)(c) of GDPR outlines the principle of data minimization and requires that personal data be adequate, relevant, and limited to what is necessary in relation to the purposes for which it is processed. In the context of AI, this means that organizations should only gather and process the minimum personal information required to carry out a particular processing operation.

It is crucial for AI applications to identify the data necessary for model training and decision-making without relying on excessive or irrelevant data. This significantly reduces sensitive data exposure, minimizes privacy concerns, and guarantees adherence to the data minimization principle.

GDPR’s Article 5(1)(b) outlines the purpose limitation principle and states that personal data must be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes; further processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes is generally not be considered to be incompatible with the initial purposes, provided appropriate safeguards are ensured for protecting the rights and freedoms of data subjects. AI applications must comply with the intended purposes stated to data subjects at the time of collecting their data. The purpose limitation principle will be violated if the same data is used for unrelated purposes without additional consent or an otherwise applicable legal basis.

Complex Data Processing

Large datasets can be necessary for optimizing the performance and accuracy of certain AI models. Since AI models thrive on abundant data for learning, this need can impede stringent data minimization efforts. AI systems may also find patterns or connections in data that the data controller did not initially intend or anticipate. As a result, it may become difficult to uphold the purpose limitation principle since new data uses may arise.

Data from third-party sources may be incorporated into AI models to improve their performance, making it difficult to ensure that this new data complies with GDPR guidelines, particularly if it was not initially obtained for that purpose. Therefore, organizations need to keep the principles of data minimization and purpose limitation at the forefront when planning and executing their processing operations to ensure their compliance with the GDPR.

C. Right Not to be Subject to Automated Decision-Making

GDPR places a general prohibition on decision-making based solely on automated processing, which produces legal effects concerning the data subject or similarly significantly affects them. This prohibition does not apply if the decision:

  • is necessary for entering into, or performance of, a contract between the data subject and a data controller,
  • is authorized by EU or member state law to which the controller is subject and which also lays down suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests, or
  • is based on the data subject’s explicit consent.

It is important to note that if the decision is necessary for the purposes of a contract or is based on consent, the data controller is required to implement suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests, at least the right to obtain human intervention on the part of the controller, to express their point of view and to contest the decision.

Automated decision-making based on sensitive personal data shall not be conducted unless the data subject provides their explicit consent or the processing is necessary for reasons of substantial public interest, in accordance with EU or member state law, and suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests are in place.

D. Data Security and Privacy by Design

Article 32 of GDPR requires organizations to implement appropriate organizational and technical safeguards in place to guarantee a level of security suitable for the risk posed by any processing operations. This includes taking precautions to guard against accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, personal data transmitted, stored, or otherwise processed. AI applications that handle personal data must follow appropriate security measures to protect the information processed by them.

Article 25 of GDPR introduces the concept of "data protection by design and default." This principle mandates organizations consider data security and privacy concerns from the design stage of AI systems and throughout their cycle. It further requires incorporating privacy elements into the architecture of AI applications and ensuring that privacy settings are always set and configured to their most stringent options.

Best AI Practices to Comply with GDPR

With AI evolving and being adapted rapidly, it's crucial to establish AI practices that comply with GDPR regulations:

Privacy by Design

  • Integrate data security and privacy safeguards from the early stages of AI development. Consider privacy risks as you build the application, and ensure that privacy is a top priority throughout the whole life cycle of the AI system.
  • Implement data minimization and purpose limitation principles to collect and process only the information required for legitimate and specified activities, avoiding the collection of enormous amounts of data.
  • Implement data anonymization and pseudonymization techniques to protect the privacy of data.
  • Use DPIAs to assess how AI applications could pose a risk to the rights and freedoms of data subjects and undertake appropriate controls.
  • Develop clear, specific, and transparent data governance standards for AI projects.
  • Ensure transparency and accountability in AI decision-making processes, particularly concerning personal data.

Transparent Data Processing

  • Inform individuals (data subjects) clearly and in an easily understandable way about how their data will be utilized in AI algorithms. Explain the AI system's functioning and probable results.
  • Provide clear terms of service and privacy rules describing how personal data protection will be ensured.

Data Minimization and Purpose Limitation

  • Specify the specific, explicit, and legitimate purpose(s) for which the AI system will process the data.
  • Collect only the information required for such purposes.
  • Ensure that any data processing carried out by the AI system is in line with the original stated purposes, falls into the purview of the consent initially obtained, or is supported by a valid legal basis.

Data Security

  • Use robust encryption and secure data handling procedures to safeguard personal information while AI systems process and transmit it.
  • Conduct routine security audits and vulnerability assessments to discover and mitigate potential data security risks in AI systems.
  • Ensure that access to data is restricted to authorized personnel only and that suitable access controls are in place.

Data Protection Impact Assessments (DPIAs)

  • Conduct DPIAs in accordance with Article 35 of the GDPR for AI projects involving high-risk processing operations, such as those with severe privacy consequences or involving new technologies or sensitive data.
  • Prior to implementation, evaluate and mitigate any potential privacy issues and develop AI systems in compliance with the GDPR requirements.
  • Whenever collecting or processing the personal data of individuals for the purposes of AI systems, especially for automated decision-making purposes, ensure to obtain appropriate legal backing and obtain valid consent of data subjects where applicable.
  • Implement mechanisms to record and manage consent preferences, enabling individuals to easily modify or withdraw their consent.

Right to Information

  • Provide individuals with sufficient and accurate information as per Articles 12, 13, and 14 of the GDPR regarding the processing of their personal data.
  • Offer individuals thorough justifications for AI-driven decisions that impact them so they can comprehend the rationale thereof.

Data Subject Rights and Safeguards

  • Provide appropriate and easily accessible means for data subjects to exercise their rights under the GDPR.
  • Provide mechanisms for human intervention in relation to automated decision-making as applicable under the GDPR, such as an appeal process, and for data subjects to express their point of view or object to AI-driven decisions.

Training and Awareness

  • Educate employees and stakeholders involved in AI development and deployment about GDPR requirements and responsible AI practices.
  • Foster a culture of data privacy and responsible AI use within the organization.

Monitoring and Accountability

  • Establish procedures for continual GDPR compliance monitoring and AI system auditing to ensure ethical AI usage.
  • Appoint a Data Protection Officer (DPO) or other accountable personnel to supervise GDPR compliance in AI projects.


The GDPR profoundly impacts AI technologies' development, deployment, and usage. It strongly emphasizes the privacy of individuals and their rights and imposes strict guidelines for processing personal data.

Controllers using AI for processing should comply with the principles of the GDPR and take a prudent and risk-focused approach. It is important to note that the EU AI Act is also expected to come into effect in the future, providing more concrete compliance obligations for AI systems.

If you’re struggling to comply with the GDPR, request a demo to witness how Securiti can help you in your GDPR compliance journey.

Key Takeaways:

  1. Impact of GDPR on AI: The General Data Protection Regulation (GDPR) significantly impacts Artificial Intelligence (AI) by imposing strict guidelines on data privacy and protection. As AI systems rely heavily on data, understanding and adhering to GDPR principles is crucial for organizations deploying AI technologies.
  2. GDPR Principles Relevant to AI: Key GDPR principles such as lawful basis for data processing, data minimization, purpose limitation, and rights of individuals directly affect how AI systems collect, process, and use personal data.
  3. Challenges with AI and GDPR Compliance: AI's complex algorithms and continuous learning capabilities pose challenges in ensuring transparency, obtaining valid consent, and adhering to data minimization principles under GDPR.
  4. New Developments: In response to GDPR and the unique challenges posed by AI, the European Union has proposed the EU AI Act to regulate AI systems more comprehensively, aiming to ensure transparency, reliability, and safety while respecting fundamental rights.
  5. Privacy by Design: GDPR mandates integrating data protection measures from the design stage of AI systems, emphasizing privacy-preserving technologies and secure data processing practices.
  6. Transparency and Automated Decision-Making: Organizations must inform individuals about automated decision-making processes, including profiling, and ensure decisions are explainable and respect the individual's rights.
  7. Data Protection Impact Assessments (DPIAs): For AI applications that pose a high risk to individuals' rights and freedoms, GDPR requires conducting DPIAs to identify, assess, and mitigate data protection risks.
  8. Rights of Individuals: GDPR grants individuals specific rights over their data, including the right to access, rectification, erasure, and the right not to be subject to decisions based solely on automated processing.
  9. Security and Accountability: Organizations must ensure AI applications handling personal data are secure and that they can demonstrate compliance with GDPR through appropriate technical and organizational measures.
  10. Cross-Border Data Transfers: GDPR emphasizes the need for adequate safeguards for the international transfer of personal data, which is particularly relevant for global AI systems.
  11. Consent and Transparency in AI: For AI systems relying on personal data, obtaining explicit consent and providing clear, accessible information about data processing activities are essential for GDPR compliance.
  12. Best Practices for AI and GDPR Compliance: Organizations should adopt privacy by design, conduct regular security and privacy assessments, manage consent effectively, and ensure transparency in AI-driven decisions.
  13. Conclusion: The GDPR poses both challenges and opportunities for AI development and use. Organizations must navigate these complexities carefully to leverage AI's benefits while ensuring compliance and protecting individuals' data privacy rights.
  14. Securiti's Role in GDPR Compliance: Securiti offers solutions to help organizations automate and manage their GDPR compliance efforts, particularly in the context of AI technologies, ensuring they meet regulatory requirements efficiently.

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