Disclaimer: GDPR is a very large and complex piece of legislation, and I am not a legal practitioner. The following information has been reviewed by a legal strategist and this blog has been updated, December 2024. You should seek your own legal counsel for any specific requirements.
Let’s be honest—marketers are feeling all the emotions about AI.
Excitement? Definitely. Fear? Maybe just a touch. Overwhelm? Well, yes.
And then the EU AI Data Act and UK GDPR regulations come in to complicate things a little further.
While AI promises to revolutionise social media marketing in a positive way, it also brings a host of challenges that can’t be ignored.
Penalties for mishandling personal data continue to appear, and organisations face financial repercussions and lasting damage to their reputation. GDPR doesn’t stop at how businesses collect and manage data on their websites—it extends to every corner of their digital activity, including social media.
Now, with AI tools playing a larger role in content creation and audience targeting, the scrutiny is even greater. Generative AI, which often relies on vast amounts of user data for machine learning, raises important questions about privacy and compliance.
Are our social media activities and use of AI tools truly GDPR-compliant?
Let’s find out what we need to consider…
AI and the Foundations of GDPR Compliance
At the heart of GDPR lies one simple rule: keep it lawful, fair, and transparent.
If you’re new to GDPR or need a refresher, check out this blog, which focuses on the fundamentals of GDPR and its impact on social media marketing.
Every data-processing activity using AI tools need to comply with one of the six legal bases, namely consent, contract, legal obligation, vital interests, public task, or legitimate interest.
Documenting how and why you’re using data is just as important. And let’s not forget transparency. If your audience can’t understand how their data is being used, then you’re doing it wrong.
In social media marketing, this means understanding how social media platforms collect and use data. For example, when using Meta’s Custom Audiences to target ads, organisations need to ensure their legal basis for processing is well-documented and compliant.
Transparency and AI Decision-Making
Whenever you are processing personal data – whether to train a new AI system, or make predictions using an existing one – you must have an appropriate lawful basis to do so, and you must be transparent about how you process personal data in an AI system. Transparency requires informing individuals about how their data is being used and ensuring that AI processing can be understood and audited to identify biases.
Under the EU AI Act, an ‘AI system’ is defined as “a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments”.
An AI system may use many AI tools as building blocks to achieve its objectives. For example, a comprehensive marketing automation platform (AI system) might integrate AI tools such as Grammarly for improving written content quality, Phrasee for generating and optimizing email subject lines, Adzooma for automating and enhancing ad campaign performance, and Crimson Hexagon (now part of Brandwatch) for AI-driven social media sentiment analysis.
To ensure compliance and fairness in data processing activities, organisations should share straightforward explanations within data capture forms and update privacy policies.
This guidance from the Information Commissioner’s Office (ICO) offers good practice that can help you comply with the transparency principle of GPDR.
User Consent and Rights
AI tools can be used to analyse customer data from sources such as online behaviour, purchase history, and social media activity. This enables organisations to profile and target specific audiences with tailored content, improving engagement and conversion rates.
GDPR requires explicit, informed consent for AI-driven profiling, meaning organisations must be transparent about how they use collect data and use AI tools for analysis.
For social media marketers, this might mean, for example, ensuring lead generation ads include clear consent checkboxes, a privacy policy link and options for users to opt out of receiving future communications.
Profiling and Automated Decision-Making
UK GDPR has provisions for automated individual decision-making (making a decision solely by automated means without any human involvement).
Individuals have the right not to be subject to decisions based solely on automated processing, including profiling. Discriminatory profiling, such as targeting ads based on race or gender stereotypes, can violate Article 22(1) and Recital 71 of GDPR/UK GDPR if they produce significant legal, or similarly significant, effects.
To comply with regulations, organisations must build safeguards that prioritise fairness and accountability. This can include human oversight—ensuring a real person reviews and validates decisions made by AI—and providing users with opt-out options for automated processes. As it is difficult to judge what might be deemed to impact an individual’s right to privacy in a significant way it is advisable to use the smallest datasets possible.
Minimising Data and Managing Retention
When it comes to GDPR, less is more, especially with data. The principle of data minimisation is clear: organisations should collect only what’s necessary for their specific purposes and have policies in place to determine how long that data is retained.
AI systems, often hungry for vast amounts of information, can make this a tricky balancing act. In social media, this means limiting the scope of data collection during ad campaigns or analytics. For example, ensure that Meta tracking pixels only collect data necessary for the campaign’s objectives and establish a clear retention timeline for audience insights.
As mentioned, AI thrives on data. Such as analysing behaviour, predicting trends, and refining targeting. But this reliance can easily lead to over-collection, sometimes pulling in data that isn’t strictly relevant to the task at hand. This not only increases the risk of non-compliance but can also erode user trust.
The solution? Regularly review your data practices to ensure you’re only using what’s essential. Minimal data doesn’t mean minimal impact; it means smarter, safer marketing.
AI Tools and Cross-Border Data Transfers
Many AI tools powering marketing campaigns are hosted outside the UK or EU, which raises another important compliance challenge: cross-border data transfers.
Under GDPR, transferring data internationally isn’t as simple as clicking ‘accept’ on a vendor’s terms. Robust mechanisms like the UK International Data Transfer Agreement (IDTA) and Standard Contractual Clauses (SCCs) are required to ensure that personal data remains protected, no matter where it’s processed.
For organisations, this means doing due diligence when selecting AI vendors. Start by prioritising tools with data centres in jurisdictions deemed GDPR-compliant, such as those covered by an adequacy decision. Where this isn’t possible, ensure that any transfer mechanism aligns with GDPR requirements.
Also important is having a solid Data Processing Agreement (DPA) in place. This document should clearly outline the responsibilities of the vendor, including how they process, store, and secure data. By vetting your vendors and securing strong agreements, you can confidently use AI tools while safeguarding personal data and maintaining compliance.
Security and Ethical Considerations
Integrating AI into marketing comes with its own set of security risks. From potential data breaches to vulnerabilities in AI models, these risks can have serious consequences if not properly managed.
High-risk AI systems, as listed in Annex III of the EU AI Act include AI used in biometrics, critical infrastructure, education, employment, essential services, law enforcement, migration, and justice.
GDPR steps in here, mandating robust security measures to protect personal data. This includes encryption, access controls, and regular audits to ensure systems remain secure and compliant.
But security is only part of the story. Ethical responsibility is just as important, especially when it comes to biases in AI algorithms. Left unchecked, these biases can lead to unfair treatment, whether in targeting ads, profiling, or automated decision-making. While GDPR doesn’t explicitly call out bias, its principles of fairness and lawfulness demand that AI algorithms do not produce discriminatory outcomes or treat individuals unfairly based on protected attributes such as race, gender or age.
Compliance and Accountability
Organisations must make Data Protection Impact Assessments (DPIAs) a regular part of operations. DPIAs help identify and address potential risks associated with processing personal data, ensuring that businesses take proactive steps to protect user rights and privacy.
To demonstrate compliance, thorough documentation and audit trails are required. These records demonstrate a company’s commitment to compliance and provide a clear roadmap for accountability. Whether it’s detailing how AI systems process data or logging responses to user rights requests, this transparency shows that the organisation is serious about upholding GDPR principles.
For social media campaigns, this could include logging the consent process for Facebook or LinkedIn lead generation ads or documenting the setup of tracking pixels to show GDPR compliance during audits. Conducting DPIAs for these activities can also help identify and address potential risks.
GDPR and AI for Social Media Marketing: A Checklist
To ensure your AI-driven social media marketing aligns with GDPR, follow this checklist:
- Establish a Legal Basis
- Define a clear legal basis for AI data processing, such as consent or legitimate interest
- Document all legal justifications to ensure compliance
- Prioritise Transparency
- Provide user-friendly explanations of AI’s role in decision-making
- Include clear privacy notices that outline how data is collected, processed, and used
- Obtain Informed Consent
- Secure explicit, informed consent for AI-driven profiling and targeted advertising
- Make it easy for users to withdraw consent or opt out of automated processes
- Safeguard User Rights
- Ensure users can access, correct, erase, and object to the processing of their data
- Implement mechanisms to process user requests efficiently
- Minimise Data Usage
- Collect only the data necessary for specific purposes
- Regularly review data practices to avoid over-collection and reduce risks
- Manage Cross-Border Transfers
- Use tools with data centres in GDPR-compliant jurisdictions when possible
- Establish strong Data Processing Agreements (DPAs) with AI vendors
- Ensure cross-border transfers are protected by mechanisms like Standard Contractual Clauses (SCCs)
- Address Security Risks
- Implement encryption, access controls, and regular audits to protect data.
- Monitor AI systems for vulnerabilities and act promptly to address breaches
- Mitigate Bias
- Conduct algorithm audits to identify and reduce biases in AI systems
- Design AI processes with fairness and impartiality in mind
- Conduct Regular DPIAs
- Perform Data Protection Impact Assessments (DPIAs) for AI-driven activities
- Evaluate risks and document measures taken to ensure compliance
- Maintain Records
- Keep thorough records of data processing activities and decisions
- Ensure all actions align with GDPR principles of lawfulness, fairness, and transparency