# GDPR and Large Language Models: Technical and Legal Obstacles
## 4 Discussion
Practical applications of high-risk AI systems further underscore the challenges of aligning advanced AI technologies with regulatory and ethical standards. A recent study on AI-based lie detection for border control systems illustrates the complex interplay between technical performance, ethical concerns, and strict regulatory compliance under the EU AI Act [88]. By evaluating real-world case studies such as iBorderCtrl and AVATAR, the study emphasizes the necessity for rigorous risk assessments and tailored security controls in high-stakes environments. **Preliminary Quantitative Assessments and Simulation Results. ** Preliminary evaluations of privacy-preserving LLMs highlight notable trade-offs between privacy and model performance. For instance, training with differential privacy (DP) often reduces accuracy and even amplifies biases: [PERSON] et al. (2019) report that DP-SGD can significantly lower overall accuracy and disproportionately hurt underrepresented classes [81]. On text generation tasks, DP also tends to degrade quality (e.g., a strong privacy setting \(\varepsilon\approx 3\) only modestly reduced a GPT-2 model's BLEU score from about 66.8 to 64.6 [89]). Recent techniques seek to mitigate such losses. [PERSON] et al. (2022) propose \"Selective\" DP, which protects only sensitive tokens and yields a much better privacy-utility trade-off: in experiments, Selective-DPSGD roughly halved the perplexity increase caused by DP (bringing test perplexity down to 160 from DP-SGD's 306, versus 61 with no privacy) at equivalent privacy levels [48]. Meanwhile, modular \"machine unlearning\" methods improve the efficiency of data removal from trained models. [PERSON] et al. (2021) introduced a \"SISA\" training strategy that accelerates retraining after deletion requests by 2-5\(\times\) with minimal impact on accuracy [31]--for example, SISA achieved about 4.6\(\times\) faster unlearning on a Purchase dataset model and 2.4\(\times\) on SVHN, and even a 1.36\(\times\) speed-up for ImageNet-scale models (with only minor accuracy degradation). These results illustrate that privacy enhancements (whether during training or post-hoc) do incur performance costs, but careful design (e.g., SDP and SISA) can substantially narrow the gap while upholding strong privacy guarantees. _Proposed Framework for GDPR-Aligned LLM Governance_  
Bridging the gap between GDPR obligations and the technical realities of Large Language Models (LLMs) requires an approach that addresses data governance, technical safeguards, continuous compliance, and transparency. We propose a four-layer governance model that integrates existing regulatory guidance with novel conceptual contributions to align LLM development and deployment with GDPR. This model synthesizes the expectations of multiple Data Protection Authorities (DPAs) and reflects GDPR's core principles of lawfulness, fairness, and accountability, while providing practical mechanisms for end-to-end compliance. The first layer, \"Data Governance and Consent Management\", focuses on creating a robust foundation before any training or deployment of the model. This involves maintaining detailed records of data origins, especially where personal data may be present, as well as establishing lawful bases for data processing (such as explicit consent or legitimate interest). It also requires verifying that the training data collected is fit for purpose, thereby fulfilling the principle of data minimization and ensuring that only what is strictly necessary is used. Strong access controls and organizational policies are put in place so that sensitive data remains protected and so that any relevant GDPR obligations--such as handling data subject requests--can be efficiently managed. The second layer, \"Technical Privacy Enhancements\", addresses the safeguards needed during training and inference. Here, developers may rely on techniques such as differential privacy (DP) to ensure that individual data points are not easily traced or reconstructed from the model's parameters.