The proliferation of Large Language Models (LLMs) has demonstrated remarkable capabilities, elevating the critical importance of LLM safety. However, existing safety methods rely on ad-hoc taxonomy and lack a rigorous, systematic protection, failing to ensure safety for the nuanced and complex behaviors of modern LLM systems. To address this problem, we solve LLM safety from legal compliance perspectives, named safety compliance. In this work, we posit relevant established legal frameworks as safety standards for defining and measuring safety compliance, including the EU AI Act and GDPR, which serve as core legal frameworks for AI safety and data security in Europe. To bridge the gap between LLM safety and legal compliance, we first develop a new benchmark for safety compliance by generating realistic LLM safety scenarios seeded with legal statutes. Subsequently, we align Qwen3-8B using Group Policy Optimization (GRPO) to construct a safety reasoner, Compliance Reasoner, which effectively aligns LLMs with legal standards to mitigate safety risks. Our comprehensive experiments demonstrate that the Compliance Reasoner achieves superior performance on the new benchmark, with average improvements of +10.45% for the EU AI Act and +11.85% for GDPR.

Despite the widespread interest in machine learning (ML), the engineering industry has not yet fully adopted ML-based methods, which has left engineers and stakeholders uncertain about the legal and regulatory frameworks that govern their decisions. This gap remains unaddressed as an engineer's decision-making process, typically governed by professional ethics and practical guidelines, now intersects with complex algorithmic outputs. To bridge this gap, this paper explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies. Drawing on recent precedents and experiences gained from other fields, this paper argues that analogical reasoning can provide a basis for embedding ML within existing engineering codes while maintaining professional accountability and meeting safety requirements. In exploring these issues, the discussion focuses on established liability doctrines, such as negligence and product liability, and highlights how courts have evaluated the use of predictive models. We further analyze how legislative bodies and standard-setting organizations can furnish explicit guidance equivalent to prior endorsements of emergent technologies. This exploration stresses the vitality of understanding the interplay between technical justifications and legal precedents for shaping an informed stance on ML's legitimacy in engineering practice. Finally, our analysis catalyzes a legal framework for integrating ML through which stakeholders can critically assess the responsibilities, liabilities, and benefits inherent in ML-driven engineering solutions.

The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future. This paper examines the fundamental role and functionality of standardization, with a particular focus on its importance in Europe's regulatory framework. Based on this, selected topics in context of standardization activities in context intelligent manufacturing and digital twins are highlighted and, by that, an overview of the Industry 4.0 standards framework is provided. This paper serves both as an informative guide to the existing standards in Industry 4.0 with respect to Artificial Intelligence and Digital Twins, and as a call to action for increased cooperation between standardization bodies and the research community. By fostering such collaboration, we aim to facilitate the continued development and implementation of standards that will drive innovation and progress in the manufacturing sector.

This paper aims to present new alarming trends in the field of child sexual abuse through imagery, as part of SafeLine's research activities in the field of cybercrime, child sexual abuse material and the protection of children's rights to safe online experiences. It focuses primarily on the phenomenon of AI-generated CSAM, sophisticated ways employed for its production which are discussed in dark web forums and the crucial role that the open-source AI models play in the evolution of this overwhelming phenomenon. The paper's main contribution is a correlation analysis between the hotline's reports and domain names identified in dark web forums, where users' discussions focus on exchanging information specifically related to the generation of AI-CSAM. The objective was to reveal the close connection of clear net and dark web content, which was accomplished through the use of the ATLAS dataset of the Voyager system. Furthermore, through the analysis of a set of posts' content drilled from the above dataset, valuable conclusions on forum members' techniques employed for the production of AI-generated CSAM are also drawn, while users' views on this type of content and routes followed in order to overcome technological barriers set with the aim of preventing malicious purposes are also presented. As the ultimate contribution of this research, an overview of the current legislative developments in all country members of the INHOPE organization and the issues arising in the process of regulating the AI- CSAM is presented, shedding light in the legal challenges regarding the regulation and limitation of the phenomenon.

Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders.

Rep. Dan Crenshaw, R-Texas, warned of the "real problem" Congress must tackle regarding drone security on Tuesday, following a House Intelligence Committee classified briefing on the mysterious sightings. "I think it's inaccurate for the Biden administration to say that they're absolutely sure that there's [sic] no problems here – they're not absolutely sure," Crenshaw told Fox News anchor Martha MacCallum on "The Story" Tuesday. "There's about 100 cases of these sightings that are still under under active investigation. Now, keep in mind, there was like 6,000 before a lot of them had been assessed to just be planes, manned aircraft, things like that… satellites. As a member of the House Intelligence Committee, Crenshaw emphasized that one of the "biggest problems" in addressing drone security is the lack of a clear "legal framework." A map depicts the various locations mystery drones have been spotted in Northeastern USA in December 2024. "Since 2017, 2018, we've given the federal government authority to detect and mitigate drone activity across the United States, but that tends to be pretty limited," said Crenshaw. "So DOJ has authority, DOD has authority, DOE - Department of Energy - has authority, DHS has some authority.

We've all got to die sometime. But whatever you think awaits us after death, it's unlikely to involve a suped-up gaming PC, a fiber connection, and tons of digital video games. Last week, a Steam support representative said that in addition to not being able to transfer your account to another person, you also can't leave it to your beneficiaries. But this policy against the inheriting of PC games might be in violation of a relatively recent United States law. A ResetEra poster named delete12345 asked a Steam support representative if they could transfer the ownership of their Steam account after they died through their will.

If the government is clear that the legislative framework is not adequate then it should update that legislative framework.

The European Parliament is calling for new regulations to ensure streaming services pay artists fairly. The proposal also calls for more transparency around how algorithms generate suggestions for which artists to stream and what tracks get the most promotion. The proposed changes will be designed to ensure smaller artists are compensated fairly. Currently, royalty rates are set in a way that makes artists accept lower pay for the distribution of their content in exchange for visibility on streaming platforms like Spotify and Apple Music. The members of the European Parliament (MEPs) are primarily concerned with introducing new legal frameworks to help support artists.

The Philippines plans to propose the creation of a Southeast Asian regulatory framework to set rules on artificial intelligence (AI), based on the country's own draft legislation, the speaker of its Congress said on Wednesday. At the World Economic Forum in Davos, Martin Romualdez said on that the Philippines would present a legal framework to the Association of Southeast Asian Nations (ASEAN) when it chairs the bloc in 2026. "We'd like to give as a gift to the ASEAN a legal framework.