Data subject rights (DSRs) are not optional check boxes. They are legally enforceable rights granted to individuals whose personal data is processed. Businesses must respect data subject rights throughout all stages of AI development, deployment, and ongoing system management. The GDPR grants individuals several rights over their personal data. Let us focus on four of these here:

1. Right to be informed: As with other data protection frameworks, transparency is key under the GDPR. This right takes the form of a duty to inform prior to the processing taking place. Businesses must include information on how they collect, use, store, and share data, the purpose of processing, the legal basis, data retention periods, and who may receive the data. Privacy notices are the typical repositories for this information. They must be concise, accessible, and written in plain language.
2. Right of access: Data subjects can request access to the exact personal data a business holds about them. Businesses must provide information about processing activities, data categories, and any third parties with whom they share the data.
3. Right to rectification: Data subjects can request organizations to correct incorrect or incomplete data without delay. Businesses must respond promptly and update the data across systems and third-party processors where necessary.
4. Right to object, right to be forgotten and right to revoke consent: It allows individuals to exercise control. The European Data Protection Board (EDPB)  published a case digest on right to object and erasure. Data subjects must be able to object to the use of their data and request its erasure when it is no longer necessary, when they withdraw consent, or for purposes like direct marketing.

Incorporating data minimization in AI Systems

One of the most effective ways businesses can respect data subject rights is by adhering to the data protection principle of data minimization. This GDPR principle requires businesses to collect and process only the minimum personal data necessary to achieve their specific purpose. Avoid over-collecting data, use anonymized or synthetic data for training, and regularly review AI outputs to remove unnecessary personal information.

Implement transparent data practices

Transparency is central to building trust and achieving legal compliance. Always define the purpose of processing, specifically the training of AI models. If businesses rely on legitimate interest, they must show that they gave data subjects the chance to object; otherwise, they invalidate their legal basis.

Clearly inform existing customers in advance when using their data to train AI models, and provide opt-out options before processing begins. Transparency is key. 

When there’s no direct relationship with the individual (such as when using publicly available data or from data brokers), the GDPR requires information to be provided within one month of its collection GDPR Articles 14.  

In 2023, the Italian DPA temporarily banned OpenAI’s ChatGPT, citing a lack of transparency around how it used personal data for training. The DPA later required the company to implement clear privacy notices and provide users with ways to exercise their rights.

Respect the right to access 

Can data owners request access to training data? 

This becomes complicated with large language models, but under the GDPR, individuals have the right to know if and how their data is being used.

How to exercise that right? 

Under the GDPR, individuals have the right to know if and how their personal data is used, including data processed by AI systems. While this is straightforward for users with an existing relationship (who can submit data subject access requests via account settings or customer support), it’s more complicated when there’s no direct connection.

In such cases, organizations must ensure proactive transparency by clearly informing people through privacy policies and AI transparency reports. Failure to uphold this right contributes to loss of trust and accountability in AI use and development.

Develop clear processes for data deletion and rectification 

Can data be corrected or deleted after it has been used to train an AI model? 

While difficult, companies must explore the use of data architectures that allow tracing of personal data contributions. The GDPR (Recital 26) considers even pseudonymous data, like randomly generated user IDs, as personal data since organizations can technically link it back to a person, directly or indirectly.

To reduce data subject risk while improving compliance, companies could implement the following measures:

• Data encryption: Businesses should ensure proper security implementation, especially when handling sensitive personal information.
• Anonymization and pseudonymization: Where possible, anonymize or pseudonymize data before using it in AI models. Anonymization and pseudonymization protect personal data by reducing breach risks and limiting the impact on individuals in case of a data exposure.
• Access control: Implement strict access controls and monitoring to ensure only authorized personnel can access personal data. This prevents unauthorized exposure of sensitive information.

By embedding these practices into AI development pipelines, organizations can take meaningful steps toward compliance, trust-building, and ethical AI deployment.

Ensure security and privacy by design

Organizations should build user trust and meet regulations by embedding privacy from the start, not treating it as an afterthought. This is the core of the privacy by design principle under the GDPR.

Key steps include:

• Promoting user choice and control: Provide clear opt-out options before processing data—whether in email campaigns, mobile app popups, or web trackers.). Empower users with privacy dashboards that let them view, manage, and delete their personal data at any time.
• Secure data handling: Businesses must encrypt personal data used in AI training while transmitting and at rest. Implement strict access control mechanisms to ensure that only authorized personnel can interact with sensitive data.

Embedding privacy and security into system architecture from the outset not only ensures compliance, trust-building, and ethical AI deployment.

Maintain ongoing communication and feedback loops

Transparency shouldn’t stop at data collection. When introducing AI processing, update your privacy notices to reflect new processing activities, as required by the GDPR. Use layered notices to highlight AI-specific practices like model training, profiling or automated decision-making. Importantly, inform users before processing, not after. True consent means giving people a real choice. Building feedback loops as user input is essential for improving fairness, spotting issues, and building trust in your AI systems.

Conclusion

As AI continues to shape modern business, respecting data subject rights is not just a legal obligation; it’s a foundation for responsible innovation. By embedding privacy by design, adopting transparent data practices, and enabling user control, organizations can align AI development with GDPR principles and foster long-term trust. Data protection isn’t a compliance checkbox, it’s a strategic imperative for ethical and sustainable AI.

Feel free to reach out to us for any clarification of AI compliance needs.

The post Respecting Data Subject Rights in AI: A Practical Guide for Businesses appeared first on TechGDPR.

So, how does the GDPR relate to AI, and what foundational principles should organizations understand to stay compliant?

Personal data and AI: A complex relationship

At its core, the GDPR is designed to protect personal data: any information that relates to an identified or identifiable individual (Article 4(1), GDPR). AI systems, particularly those using machine learning, thrive on data. Whether it’s customer profiles, behavioural patterns, facial recognition data, or voice recordings, much of the data used in AI training and operations falls under the GDPR’s scope.

Key principles that govern AI under the GDPR

The GDPR outlines several fundamental principles that guide lawful data processing (Article 5, GDPR).
These foundational principles are perhaps the least understood aspects of EU data protection law. When applied to AI, the following principles are especially critical:  

• lawfulness, fairness, and transparency, 
• purpose limitation, 
• data minimization, 
• accuracy, 
• storage limitation, 
• integrity, confidentiality, and accountability

Let’s break down how some of the core GDPR principles affect AI development and deployment.

Lawfulness, fairness, and transparency

AI systems must have a clear legal basis for processing personal data. Organizations must clearly disclose how they collect and use data. This applies to the use of data for model training as much as the use of data in model output or inferences. In addition to the transparency requirement that applies when personal data is submitted to algorithmic decision-making, the GDPR provides for the right not to be subject solely to AI automated decisions. For example, when AI applications make decisions that affect people, such as denying them a loan or job, organizations must ensure individuals understand how the decision was made and provide a recourse mechanism for human intervention

Purpose limitation

Organizations must collect data for specific, explicit, and legitimate purposes. AI systems should not reuse personal data for unrelated tasks without further consent or justification. In AI, this prevents training or deploying models using data gathered for unrelated tasks. Organizations must define purposes clearly and inform users at the time of data collection. Reusing data without consent risks non-compliance with the GDPR. Perform a compatibility assessment before processing the new purpose further. This principle protects user trust and ensures responsible data use.

Data minimization

Organizations should process only the data necessary for the task. This poses a challenge for AI, which often thrives on large datasets. Organizations must carefully assess what data is important and avoid overcollection. This increases risks and may violate data protection regulations. Organizations should perform data audits to identify and eliminate non-essential data. Moreover, organizations can sometimes use synthetic or anonymized data instead of real personal data. Data minimization reduces exposure to breaches and ensures ethical AI development. It’s a key principle for building privacy-preserving and trustworthy AI systems.

Accuracy

AI outputs must be based on accurate data. Poor data quality can result in harmful or biased outcomes, which can violate GDPR and damage trust. Accuracy requires that personal data used in AI systems is correct, complete, and up to date. According to ICO, organizations are obligated to ensure data accuracy and correct errors promptly. Low-quality data can undermine both compliance and credibility of AI systems. Regular data validation, cleansing, and monitoring are essential. Organizations should provide users with ways to challenge or correct inaccurate outputs. Ensuring accuracy builds trust, fairness, and legal defensibility in AI applications.

Storage limitation

Organizations should not keep personal data longer than necessary, which affects how long they can retain and reuse AI training datasets. Organizations must delete or anonymize personal data once they no longer need it for its original purpose. AI training datasets containing personal data must have defined retention periods. Retaining data indefinitely increases privacy and legal risks. Organizations should regularly review data to decide what to archive, delete, or anonymize. Reusing old datasets requires checking if the original legal basis and purpose still apply. Organizations must document and enforce data retention policies. This principle ensures compliance, efficiency, and reduced data exposure.

Integrity, confidentiality and accountability

Security is crucial. Organizations must implement robust technical and organizational measures (e.g., encryption, access control), particularly as AI systems often aggregate and process data across multiple sources, increasing the risk of breaches. Integrity and confidentiality require that personal data is protected against unauthorized access, alteration, or loss. Implementing tools like the Privacy tech directory helps both companies and individuals safeguard personal information and comply with privacy regulations. Security measures should align with the sensitivity and volume of data processed. Regular audits, penetration tests, and employee training strengthen protection. Maintaining integrity, confidentiality, and accountability ensures responsible system use with clear oversight and traceability of actions and decisions. Accountability mechanisms such as logging, monitoring, and clear roles and responsibilities enhance trust, support regulatory compliance, and foster responsible AI deployment.

Automated decision-making and profiling

Article 22 of the GDPR gives individuals the right not to be subject to decisions based solely on automated processing, including profiling, which significantly affects them. This applies to applications such as credit scoring, job application filtering, and predictive policing. Unless exceptions such as  explicit consent or contractual necessity apply, organizations must ensure human oversight and provide meaningful information about the logic involved. 

The challenge of explainability

One of the greatest tensions between AI and the GDPR is explainability. Many AI models, especially deep learning systems, are not easily interpretable. Yet, Recital 71, GDPR emphasizes transparency and the right to understand meaning information on how decisions are made.  Many AI systems operate as “black boxes,” making their decisions hard to interpret. This lack of transparency can undermine user trust and legal compliance. Organizations must adopt Explainable AI (XAI) techniques to clarify how decisions are made. Clear explanations help individuals understand, contest, or seek redress for decisions. XAI supports both ethical AI development and adherence to data protection principles.

Best practices for developing and using GDPR-compliant AI

Organizations can align AI systems with GDPR by conducting Data Protection Impact Assessments (DPIAs), implementing Privacy by Design and by Default (Article 25), maintaining detailed records, ensuring human oversight, and providing clear, accessible privacy notices. Consult with your DPO to ensure your AI technologies comply with any applicable legislation which could potentially include the GDPR, the CCPA, the EU Artificial Intelligence Act, etc. 

International data transfers

If your AI system transfers personal data outside the EU (e.g., to cloud servers in the US), ensure adequate safeguards are in place. Some commonly used safeguards include Standard Contractual Clauses (SCCs)  which are detailed in the GDPR and serve to govern international data flows.

Conclusion

AI holds incredible potential to transform industries and improve lives. However, to align with the GDPR and respect individuals’ rights in the data-driven era, developers and organizations must use AI responsibly. Understanding and embedding GDPR compliance into AI development is a crucial step toward building ethical, transparent, and sustainable technologies. Consult your DPO to ensure AI systems comply with the GDPR and other global privacy laws.

Feel free to reach out to us for any clarification of AI compliance needs.

The post AI and the GDPR: Understanding the Foundations of Compliance appeared first on TechGDPR.

E-commerce businesses process large amounts of personal data, including contact details, payment information, and browsing history, requiring data protection. By implementing strong data protection practices and security measures like encryption and access controls, businesses could reduce the risks of breaches and cyberattacks. 

GDPR compliance for e-commerce businesses demonstrates commitment to protecting customer privacy, and encouraging continued customer relationships, giving businesses a competitive advantage over those that are not GDPR-compliant.

Here are seven actionable steps that may help e-commerce businesses navigate GDPR compliance effectively.

Conduct a data audit 

When deciding to work towards GDPR compliance in e-commerce, it is important to start by conducting a comprehensive inventory of data collection processes. 

The steps to carry out the audit could include:

• Identify all personal data categories collected, such as contact details, payment details, and activity logs, and the granular purposes this collection serves. Determining the retention period is important, as the GDPR does not allow indefinite retention.
• Review how and where personal data is collected and stored, whether on cloud servers, local databases, or third-party platforms. Regularly review third parties and minimize retention periods, with clear specifications on when data will be securely deleted. Additionally, document the security measures implemented to protect the data.

Access consent management

Access to customer data can be limited to authorized employees, IT administrators, and secure third-party providers based on a need to know basis.

Consent for cookies can be effectively implemented through a cookie banner, allowing users to manage or withdraw consent anytime. Use clear opt-in mechanisms for newsletters, cookies, and marketing, avoiding pre-checked boxes. Maintain consent logs for audit compliance, ensuring each data use has separate, revocable consent without affecting core services.

Review and update privacy notice

A companies’ privacy notice should be clear, easily understood, and transparent to ensure GDPR compliance and build customers’ trust. The privacy notice should clearly state:

• What data you collect and why (e.g., personal details, payment information, browsing behaviour),
• How data is being used,
• Explain purposes of data collection and processing, and
• How customers can exercise their rights, such as requesting data deletion or correction.

It is important to regularly review and update one’s privacy notice in order to reflect any changes in data collection, processing, or legal regulations to maintain compliance.

Enhance security to protect customer information

With the rise of cyber attacks worldwide, protecting  personal data is an essential aspect of GDPR compliance for e-commerce businesses. Customers trust businesses with sensitive information, payment details, address, and browsing history. Implementing good data security measures will help reduce data breaches. Implementing strong data security measures reduces breaches, while a structured response plan ensures quick recovery and minimizes damage.

To minimize security risks, e-commerce businesses may implement:

• End-to-end encryption: Encrypting sensitive customer data both in transit at rest may prevent unauthorized  access. This ensures that unauthorized individuals cannot read the data, even if intercepted, without the correct encryption key. It could be a standard protocol for all online transactions.
• Multi-factor authentication (MFA): Access control may require additional verification steps, such as one-time passwords (OTP) or biometric authentication. This process will reduce unauthorized logins.
• Regular security audits: This could be conducted to identify vulnerabilities through routine system checks. These assessments may help prevent data leak and ensure GDPR compliance.
• Access control & monitoring: Role-based access control (RBAC) which restricts users based on predefined role, to ensure that only authorised personnel have access to sensitive personal data.

Investing in robust data security could create a security plan which protects customers and also ensures GDPR compliance in all operations.

Offer employees training

Employees are first in line of defence when talking about data protection. Regular comprehensive GDPR training is important for e-commerce businesses. Breaches occur due to human error, such as mishandling sensitive data or falling for phishing scams. The employer is responsible for ensuring that employees are well-trained on data protection and compliance requirements.

Businesses should provide ongoing training and workshops to regularly update the employees knowledge on data protection, evolving threats, and regulatory changes to raise awareness within the organization.

Establish data subject rights procedure

Under the GDPR, data subjects have rights, including access, erasure, rectification, and objection to control of their personal data.

E-commerce must have clear procedures on how to handle and respond to these requests efficiently. GDPR compliance requires a response within one month-delays or non compliance can lead to fines.

To ensure compliance, businesses may:

• Appoint a data protection officer (DPO) according to the European commission or an internal team with the guidance of a DPO to monitor compliance and data protection issues. “It is much easier and cost effective” to appoint an external DPO.
• Create a clear and accessible process for handling data subject requests, such as an email address or request form on the website.
• Implement automated tools to manage and track data subject requests within the required time frame.
• Keep records of all requests to demonstrate compliance if audited.

Review third-party agreements

E-commerce businesses sometimes utilize third-party vendors, such as payment processors, cloud storage providers, and marketing platforms, to handle customer data. Therefore, it’s crucial to ensure these vendors comply with data protection regulations to safeguard customer information and avoid potential risks.

Under the GDPR, having a data protection agreement with a third party vendor is required  to comply with data protection regulations if the vendor processes personal data on your behalf.

Here are steps that could be considered to manage risks associated with third-party vendors:

• Identify all third party vendors that process customer data and assess their data security measures.
• Ensure that all vendors handling personal data have existing supplier agreement, outlining responsibilities, security measures, and data processing activities.
• If a vendor transfers data outside the EU/EEA, ensure they follow GDPR requirements
• Regularly review vendor policies, conduct security audits, and ensure that the vendors comply with GDPR requirements.

Conclusion

By implementing these seven actionable steps, e-commerce can mitigate risk, protect customer data, avoid penalties, and build trust.

Hiring an external DPO officer in the absence of an internal data protection team or to advise and provide competent GDPR support to the internal DPO, will ensure  proper compliance in line with the GDPR, and gain a competitive advantage in the market.

The post Seven Actionable Steps to Achieve GDPR Compliance for E-Commerce Businesses appeared first on TechGDPR.

Exploring key aspects of password security involves evaluating password strength to resist brute force attacks and using password managers for secure and unique passwords. It also includes leveraging multi-factor authentication (MFA) to enhance protection and recognizing the risks of using browser-suggested passwords and potential vulnerabilities if the browser or device gets compromised.

How secure is my password?

One of the ways to access the strength of a password is through entropy. Entropy measures password complexity by assessing its randomness, indicating how unpredictable and difficult it is for attackers to guess. Higher entropy, or more randomness, in lay man’s terms means a more secure password. Factors that contribute to higher password entropy include:

• Length: Longer passwords are generally harder to crack.
• Complexity: Including a mix of uppercase and lowercase letters, numbers, and symbols.
• Unpredictability: Avoiding predictable patterns like common words and phrases.

If one is curious about understanding how secure their password is this Password Entropy Calculator helps an individual understand password strength and evaluate their own passwords. A secure password should have high entropy, which makes it resistant to brute-force attacks, where attackers systematically try every possible combination of passwords or keys until they find the correct one.

How password managers enhance security?

According to the German Federal Office for Information Security (BSI), using a password manager is one of the most effective ways to securely store and manage passwords. These standards ensure that the strategies outlined are both robust and reliable, offering a trusted framework for enhancing password security. Password managers are powerful tools for improving password security and convenience. They securely store and manage passwords, making it easier to use complex, unique credentials for each account. This not only enhances security by reducing the risk of weak or reused passwords, but also simplifies the online experience by eliminating the need to remember multiple passwords. Password managers enhance security by:

• Generating strong passwords: Password managers create random, complex passwords that are nearly impossible to crack.
• Secure /storage: Passwords are encrypted and stored securely, reducing the risk of exposure.
• Unique passwords for every account: Using unique passwords for each account limits the damage if one account is compromised (for instance if logging into a service while using public WiFi leads to a third party intercepting an individual’s credentials).
• Automatic filling: Password managers can auto fill login credentials, reducing the risk of phishing attacks by ensuring only the authentic individual can  enter credentials on legitimate sites.

There are many popular password managers that offer both free and premium versions to suit individual or organizational needs. Organizational password management needs often focus on collaboration, centralized control, and compliance with security policies, requiring features like shared vaults, role-based access, and audit trails. In contrast, individual users prioritize personal security, ease of use, and cross-device synchronization to protect their accounts.

How Multi-factor Authentication (MFA) adds an extra layer of security

While strong passwords are essential, they are not reliable. The European Union has emphasised how MFA protects consumer sensitive data, enhances operational resilience, and mitigates cybersecurity risks. Multi-factor Authentication (MFA) adds an extra layer of security by requiring users to provide two or more verification factors to access an account. These factors typically include a combination of at least two of the following:

• Something you know: A password or PIN.
• Something you have [i.e. physically]: A smartphone, hardware token, or security key.
• Something you are: Biometric data, such as fingerprints or facial biometrics.
• Somewhere you are: The location matches the expected location (VPNs).

MFA significantly reduces the risk of unauthorized access, even if a password is compromised. According to Microsoft, MFA can prevent 99.9% of account compromise attacks, making it a crucial component of any security strategy. 

Password security and compliance

Many industries are subject to regulations that require high password security to protect sensitive data such as:

• The General Data Protection Regulation (GDPR): Mandates the protection of personal data for EU residents.
• The Payment Card Industry Data Security Standard (PCI DSS): Requires strong password policies for organizations handling credit card data.
• Health Insurance Portability and Accountability Act (HIPAA): Enforces password security to safeguard patient information.

Failure to comply with these regulations can result in huge fines and legal consequences. Implementing best practices for password security is not just about protection best practices, it’s a compliance necessity.

Are browser-suggested passwords safe?

They are generally safe and convenient because modern web browsers like Chrome, Firefox, and Safari use encrypted storage and advanced algorithms offering built-in password managers that suggest and store passwords. While convenient, there are some risks to consider.

• Limited security features: Browser-based password managers may not offer the same level of encryption and security as dedicated password manager apps.
• Device dependency: If a device is compromised or lost, the stored passwords may be at risk, especially if the device lacks proper security controls.
• Synchronization risks: Attackers could make passwords synced across devices via a cloud service vulnerable if they compromise the cloud account.
• Phishing vulnerability: Phishing websites can exploit auto fill features by cloning legitimate sites.

When choosing to use browser-suggested passwords, ensure an up-to-date browser, use strong device security, and consider enabling MFA for cloud accounts.

Conclusion

Password security is a staple of digital safety and regulatory compliance. Creating strong, unique passwords, using password managers, and enabling multi factor authentication helps individuals and organizations reduce unauthorized access and breaches.

While browser-suggested passwords offer convenience, understanding their limitations and risks is essential. Ultimately, a proactive approach to password security can protect an individual’s data, ensure compliance, and build trust with customers.

Feel free to reach out to TechGDPR for any clarification of technical compliance needs.

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