Artificial intelligence (AI) has significantly driven advancement in the healthcare field by enabling the integration of highly advanced algorithms to improve diagnostics, patient surveillance, and treatment planning. Nonetheless, dependence on sensitive health data and automated decision-making exposes such systems to escalating risks of privacy breaches and is under rigorous regulatory oversight. In particular, the EU AI Act classifies AI uses pertaining to healthcare as “high-risk”, thus requiring the application of strict provisions related to transparency, safety, and privacy. This paper presents a comprehensive overview of the diverse privacy attacks that can target machine learning (ML)-based healthcare systems, including data-centric and model-centric attacks. We then propose a novel privacy-preserving architecture that integrates federated learning with secure computation protocols to minimally expose data while ensuring strong model performance. We outline an ongoing monitoring mechanism compliant with EU AI Act specifications and GDPR standards to further improve trust and compliance. We further elaborate on an independent adaptive algorithm that automatically tunes the level of cryptographic protection based on contextual factors like risk severity, computational capacity, and regulatory environment. This research aims to serve as a blueprint for designing trustworthy, high-risk AI systems in healthcare under emerging regulations by providing an in-depth review of ML-specific privacy threats and proposing a holistic technical solution.

Integrating artificial intelligence into border control systems may help to strengthen security and make operations more efficient. For example, the emerging application of artificial intelligence for lie detection when inspecting passengers presents significant opportunities for future implementation. However, as it makes use of technology that is associated with artificial intelligence, the system is classified as high risk, in accordance with the EU AI Act and, therefore, must adhere to rigorous regulatory requirements to mitigate potential risks. This manuscript distinctly amalgamates the technical, ethical, and legal aspects, thereby offering an extensive examination of the AI-based lie detection systems utilized in border security. This academic paper is uniquely set apart from others because it undertakes a thorough investigation into the categorization of these emerging technologies in terms of the regulatory framework established by the EU AI Act, which classifies them as high risk. It further makes an assessment of practical case studies, including notable examples such as iBorderCtrl and AVATAR. This in-depth analysis seeks to emphasize not only the enormous challenges ahead for practitioners but also the progress made in this emerging field of study. Furthermore, it seeks to investigate threats, vulnerabilities, and privacy concerns associated with AI, while providing security controls to address difficulties related to lie detection. Finally, we propose a framework that encompasses the EU AI Act’s principles and serves as a foundation for future approaches and research projects. By analyzing current methodologies and considering future directions, the paper aims to provide a comprehensive understanding of the viability and consequences of deploying AI lie detection capabilities in border control.

Large language models (LLMs) have shown remarkable skills across various activities, including text generation and code synthesis. Their widespread applicability, however, raises substantial concerns about security, privacy, and possibly misuse. Of recent legislative efforts, the most notable is the proposed EU AI Act, which classifies specific AI applications as high-risk. For detailed regulatory guidance, also refer to the GDPR and HIPAA privacy rules. This paper introduces SecureLLM, a novel framework that integrates lightweight cryptographic protocols, decentralized fine-tuning strategies, and differential privacy to mitigate data leakage and adversarial attacks in LLM ecosystems. We propose SecureLLM as a conceptual security architecture for LLMs, offering a unified approach that can be adapted and tested in real-world deployments. While extensive empirical benchmarks are deferred to future studies, we include a small-scale demonstration illustrating how differential privacy can reduce membership inference risks with a manageable overhead. The SecureLLM framework underscores the potential of cryptography, differential privacy, and decentralized fine-tuning for creating safer and more compliant AI systems.

Artificial Intelligence (AI) is increasingly incorporated into medical devices, revolutionizing diagnostics, treatment planning, and patient monitoring. To ensure AI’s safe and ethical use, the European Commission published the AI Act in 2024, which places stringent obligations on AI systems, especially those classified as high-risk, such as medical devices. This paper evaluates the impact of the EU AI Act on existing regulations such as the Medical Device Regulation (MDR) and the In Vitro Diagnostic Regulation (IVDR). It explores challenges related to compliance, certification processes, and potential conflicts between the AI Act and existing medical device frameworks while providing recommendations for harmonization.

The European Union’s Artificial Intelligence Act (EU AI Act) proposes a comprehensive regulatory framework for rtificial ntelligence (AI), emphasizing transparency, accountability, and ethical considerations, particularly in high-risk sectors like healthcare. This study assesses the readiness of European healthcare institutions for compliance with the EU AI Act. Using a systematic analysis of news articles and publications, we examine the current discourse on AI governance, compliance challenges, and implementation strategies within the European healthcare context. Our study findings indicate a big gap between AI adoption and compliance preparedness; hence, healthcare institutions should have increased awareness and strategic planning.