Trump Warned of a Tren de Aragua'Invasion.' Hundreds of records obtained by WIRED show thin intelligence on the Venezuelan gang in the United States, describing fragmented, low-level crime rather than a coordinated terrorist threat. Alleged members of Tren de Aragua sit handcuffed during a preliminary hearing on July 9, 2025, in Santiago, Chile, where they faced homicide charges. As the Trump administration publicly cast Venezuela's Tren de Aragua (TdA) as a unified terrorist force tied to President Nicolás Maduro and operating inside the United States, hundreds of internal US government records obtained by WIRED tell a far less certain story. Intelligence taskings, law-enforcement bulletins, and drug-task-force assessments show that agencies spent much of 2025 struggling to determine whether TdA even functioned as an organized entity in the US at all--let alone as a coordinated national security threat.

The model is built to detect when crimes are being "contemplated." A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on building other state-or county-specific models. Over the past year, Elder says, Securus has been piloting the AI tools to monitor inmate conversations in real time (the company declined to specify where this is taking place, but its customers include jails holding people awaiting trial, prisons for those serving sentences, and Immigrations and Customs Enforcement detention facilities). "We can point that large language model at an entire treasure trove [of data]," Elder says, "to detect and understand when crimes are being thought about or contemplated, so that you're catching it much earlier in the cycle."

Buying something in the shops used to be as simple as choosing the item and handing over the money. But in recent years, the great British shopping experience has dramatically changed. In 2025, artificial intelligence (AI) is patrolling Britain's retail stores to keep an eye on customers as they stock up on essentials. Now, people are subjected to a slew of AI-powered tech, including intelligent surveillance cameras, robots, facial recognition systems and online age checks. Home Bargains is the latest to follow the trend, with a new AI-enabled security system that watches you while you scan your own items.

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on four criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field

This survey paper offers a thorough analysis of techniques and algorithms used in the identification of crime leaders within criminal networks. For each technique, the paper examines its effectiveness, limitations, potential for improvement, and future prospects. The main challenge faced by existing survey papers focusing on algorithms for identifying crime leaders and predicting crimes is effectively categorizing these algorithms. To address this limitation, this paper proposes a new methodological taxonomy that hierarchically classifies algorithms into more detailed categories and specific techniques. The paper includes empirical and experimental evaluations to rank the different techniques. The combination of the methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of the techniques and algorithms for identifying crime leaders, assisting researchers in making informed decisions. Moreover, the paper offers valuable insights into the future prospects of techniques for identifying crime leaders, emphasizing potential advancements and opportunities for further research. Here's an overview of our empirical analysis findings and experimental insights, along with the solution we've devised: (1) PageRank and Eigenvector centrality are reliable for mapping network connections, (2) Katz Centrality can effectively identify influential criminals through indirect links, stressing their significance in criminal networks, (3) current models fail to account for the specific impacts of criminal influence levels, the importance of socio-economic context, and the dynamic nature of criminal networks and hierarchies, and (4) we propose enhancements, such as incorporating temporal dynamics and sentiment analysis to reflect the fluidity of criminal activities and relationships, which could improve the detection of key criminals .

Some of Britain's biggest retailers, including Tesco, John Lewis and Sainsbury's, have been urged to pull out of a new policing strategy amid warnings it risks wrongly criminalising people of colour, women and LGBTQ people. A coalition of 14 human rights groups has written to the main retailers – also including Marks & Spencer, the Co-op, Next, Boots and Primark – saying that their participation in a new government-backed scheme that relies heavily on facial recognition technology to combat shoplifting will "amplify existing inequalities in the criminal justice system". The letter, from Liberty, Amnesty International and Big Brother Watch, among others, questions the unchecked rollout of a technology that has provoked fierce criticism over its impact on privacy and human rights at a time when the European Union is seeking to ban the technology in public spaces through proposed legislation. "Facial recognition technology notoriously misidentifies people of colour, women and LGBTQ people, meaning that already marginalised groups are more likely to be subject to an invasive stop by police, or at increased risk of physical surveillance, monitoring and harassment by workers in your stores," the letter states.Its authors also express dismay that the move will "reverse steps" that big retailers introduced during the Black Lives Matter movement, including high-profile commitments to be champions of diversity, equality and inclusion. Meanwhile, concerns over the broadening use of facial recognition technology have further intensified after the emergence of details of a police watchlist used to justify the contentious decision to use biometric surveillance at July's Formula One British Grand Prix at Silverstone.

Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.

On August 8th, 2021, Spanish police and customs agents intercepted the cargo ship NATALIA on suspicion of narcotics trafficking. The ship was en route from Lebanon via Iskenderun, Turkey, to Lagos, Nigeria, and hidden on board was nearly 20 tons of hashish worth $470 million. That may sound like the opening scene of an action flick, but it's the kind of occurrence that happens more frequently than you might expect on the high seas. Drug smuggling, illegal fishing, and piracy are constant threats. Following a number of recent piracy incidents in the Gulf of Aden, Iran, Russia, and China recently began naval and air drills seeking to counter maritime piracy.

Organizations are embracing digital transformation to enhance operations. Artificial intelligence (AI) in particular is revolutionizing the ways companies collaborate and conduct business. However, as these technologies spread across industries, these systems give rise to new attack points and vulnerabilities; especially for criminal activity. A study published in the journal Crime Science analyzed a vast spectrum of AI-enabled crimes in the years ahead ranging from military robots and autonomous attack drones to AI-assisted stalking. To assess the risks associated with these various potential criminal scenarios, the review featured a two-day workshop of individuals from the private and public sector, academics, police agencies, and more.

While money is the prime necessity of life, it is also the feeding grain for criminals and terrorist organisations. One term that we hear quite often regarding money-related crimes, is'Money Laundering'. In simple words, 'money laundering' is the unlawful act of deliberately trying to conceal the origins of financial assets, in order to legitimise the financial transactions used for criminal offences. The money launderers try to mask the trail of their assets by introducing illegal profits in their financial history, which makes the process of money tracing'obscure' for the financial institutions. The illegal money, which is not traceable, is then used to carry out criminal activities.