As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems

The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics that share operational knowledge and techniques both within and across different environments (fiat-based, crypto-assets, etc.). Traditional rule-based systems lack the adaptability needed to detect sophisticated or coordinated criminal behaviors (patterns), highlighting the need for strategies that analyze actors' interactions to uncover suspicious activities and extract their modus operandi. For this reason, in this work, we propose an approach that integrates graph machine learning and network analysis to improve the detection of well-known topological patterns within transactional graphs. However, a key challenge lies in the limitations of traditional financial datasets, which often provide sparse, unlabeled information that is difficult to use for graph-based pattern analysis. Therefore, we firstly propose a four-step preprocessing framework that involves (i) extracting graph structures, (ii) considering data temporality to manage large node sets, (iii) detecting communities within, and (iv) applying automatic labeling strategies to generate weak ground-truth labels. Then, once the data is processed, Graph Autoencoders are implemented to distinguish among the well-known topological patterns. Specifically, three different GAE variants are implemented and compared in this analysis. Preliminary results show that this pattern-focused, topology-driven method is effective for detecting complex financial crime schemes, offering a promising alternative to conventional rule-based detection systems.

Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require a high computational cost, one main reason being that RLHF assigns both the generation and alignment tasks to the LLM simultaneously. In this paper, we introduce Proxy-RLHF, which decouples the generation and alignment processes of LLMs, achieving alignment with human values at a much lower computational cost. We start with a novel Markov Decision Process (MDP) designed for the alignment process and employ Reinforcement Learning (RL) to train a streamlined proxy model that oversees the token generation of the LLM, without altering the LLM itself. Experiments show that our method achieves a comparable level of alignment with only 1\% of the training parameters of other methods.

The credit rating agency Moody's has warned that water companies face an "elevated" risk from cyber attackers targeting drinking water, as suppliers wait on permission from the industry regulator to ramp up spending on digital security. Moody's said, in a report to investors, that hackers are increasingly zeroing in on infrastructure companies, including water and wastewater treatment companies, and the use of AI (artificial intelligence) could accelerate this trend. Last month, Southern Water, which supplies 4.6 million customers in the south of England, said the Black Basta ransomware group had claimed to have accessed its systems, posting a "limited amount" of data on the dark web. Separately, South Staffordshire Water apologised in 2022 after hackers stole customers' personal data. Moody's warned that the growing use of data-logging equipment to monitor water consumption, and the use of digital smart meters, made companies more vulnerable to attacks.

As AI technology like ChatGPT evolves, so do the strategies and tactics used by cybercriminals. Steve Flynn, Sales and Marketing Director at ESET Southern Africa, says ongoing awareness is crucial in understanding how to manage potential cybersecurity challenges posed by these developing tools. As artificial intelligence (AI) technology becomes a new reality for individuals and businesses, its potential impact on cybersecurity cannot be ignored. OpenAI and its language model, ChatGPT, are no exception and while these tools offer significant benefits to almost every industry, they also present new challenges for digital security. ChatGPT raises concerns due to its natural language processing capabilities, which could be used to create highly personalised and sophisticated cyberattacks.

CEO at VRIZE, helping clients accelerate digital transformation. For technology professionals, business leaders and observers, the news is hardly surprising. Back in April 2020, as the pandemic raged on, Microsoft's SEO, Satya Nadella, presciently noted, "We've seen two years' worth of digital transformation in two months." A big part of this digital transformation was led by artificial intelligence. The pandemic has only accelerated this growth, as the walls around artificial intelligence research and development are rapidly starting to drop everywhere.

Rapid advances in AI and machine learning are defining cybersecurity's future daily. Identities are the new security perimeter and Zero Trust Security frameworks are capitalizing on AI's insights to thwart breaches in milliseconds. Advances in AI and machine learning are also driving the transformation of endpoint security toward greater accuracy and contextually intelligence. Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023.

How can we forecast, prevent, and (when necessary) mitigate the harmful effects of malicious uses of AI? This is the question posed by a 100-page report released last week, written by 26 authors from 14 institutions. The report, which is the result of a two-day workshop in Oxford, UK followed by months of research, provides a sweeping landscape of the security implications of artificial intelligence. The authors, who include representatives from the Future of Humanity Institute, the Center for the Study of Existential Risk, OpenAI, and the Center for a New American Security, argue that AI is not only changing the nature and scope of existing threats, but also expanding the range of threats we will face. They are excited about many beneficial applications of AI, including the ways in which it will assist defensive capabilities.

You may have a basic understanding of what Artificial Intelligence, or AI, is. But are you familiar with the range of issues it raises for your fundamental rights? Here, we provide a brief overview of the issues at stake, as well as a look at how Access Now is working to help ensure that when companies develop AI technology -- and governments adopt or regulate it -- your rights are protected. AI refers to the theory and development of computer systems that can act without explicit human instruction and can self-modify as necessary. "AI" is used broadly to refer to a wide range of technological approaches that encompass everything from so-called machine learning to the development of autonomous, connected objects to the futuristic concept of "the Singularity", as our colleagues at Privacy International explain.

Cybersecurity continues to threaten the global landscape in 2018 and 95 percent of CIOs surveyed said they expect cyber threats to increase and impact their organisations, said the survey, presented during Gartner Symposium/ITxpo in Ontario. For the majority of CIOs, cybersecurity and artificial intelligence (AI) will significantly change how they do their jobs in the near future, a new survey said on Tuesday. Cybersecurity continues to threaten the global landscape in 2018 and 95 percent of CIOs surveyed said they expect cyber threats to increase and impact their organisations, said the survey, presented during Gartner Symposium/ITxpo in Ontario. "In response to these concerns, the survey found that digital security ranks high on the CIO agenda as 35 percent of respondents said they have already invested and deployed some aspect of digital security, and 36 percent are in the process of planning to implement some form of digital security," said Andy Rowsell-Jones, Vice President and Distinguished Analyst at Gartner. "CIOs are also increasingly adopting AI in their organisations. Predominantly, AI is being used initially, either to boost the customer experience or to fight fraud," he added.