This, with the ever - increasing sophistication of cyberwar, calls for novel solutions. In this regard, Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity - related strategies. While existing literature has focused much on the defensive use of LLMs, when it comes to their offensive utilization, very little has been reported - name ly, concerning V ulnerability A ssessment (VA) report validation. Consequentially, this paper tries to fill that gap by investigating the capabilities of LLMs in automating and improving the validation process of the report of the VA . From the critical review of the related literature, this paper hereby proposes a new approach to using the LLMs in the automation of the analysis and within the validation process of the report of the VA that could potentially reduce the number of false positives and generally enhance efficiency. These results are promisi ng for LLM automatization for improving validation on reports coming from VA in order to improve accuracy while reducing human effort and security postures. The contribution of this paper provides further evidence about the offensive and defensive LLM capabilities and therefor helps in devising more appropriate cybersecurity strategies and tools accordingly.

AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to reshape methodology and design of future visual analytics applications? What will be our role as visualization researchers in the future? What are opportunities, open challenges and threats in the context of an increasingly powerful AI? This Visualization Viewpoint discusses these questions in the special context of biomedical data analytics as an example of a domain in which critical decisions are taken based on complex and sensitive data, with high requirements on transparency, efficiency, and reliability. We map recent trends and developments in AI on the elements of interactive visualization and visual analytics workflows and highlight the potential of AI to transform biomedical visualization as a research field. Given that agency and responsibility have to remain with human experts, we argue that it is helpful to keep the focus on human-centered workflows, and to use visual analytics as a tool for integrating ``AI-in-the-loop''. This is in contrast to the more traditional term ``human-in-the-loop'', which focuses on incorporating human expertise into AI-based systems.

Computational notebooks such as Jupyter Notebook have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed interactive visualization tools that support notebooks. However, little is known about the appropriate design of visual analytics (VA) tools in notebooks. To bridge this critical research gap, we investigate the design strategies in this space by analyzing 159 notebook VA tools and their users' feedback. Our analysis encompasses 62 systems from academic papers and 103 systems sourced from a pool of 55k notebooks containing interactive visualizations that we obtain via scraping 8.6 million notebooks on GitHub. We also examine findings from 15 user studies and user feedback in 379 GitHub issues. Through this work, we identify unique design opportunities and considerations for future notebook VA tools, such as using and manipulating multimodal data in notebooks as well as balancing the degree of visualization-notebook integration. Finally, we develop SuperNOVA, an open-source interactive tool to help researchers explore existing notebook VA tools and search for related work.