microsoft La última versión Insider Preview de Windows 11 se lanzó en el canal Canary el miércoles, presentando dos actualizaciones importantes. La mayor inclusión es una actualización del emulador Prism para computadoras Arm que brinda soporte para accesorios adicionales para dispositivos que ejecutan Windows 11 en conjuntos de chips basados en Arm. Con esto, estos dispositivos podrán ejecutar más aplicaciones y juegos antiguos, así como la última versión de Adobe Premiere Pro 2025. Junto a esto, la actualización también introdujo un nuevo teclado para el gamepad en pantalla.
Windows 11 Insider Preview recibirá una actualización de Prism Emulator
En Windows desde el interior. Publicación de blogMicrosoft ha anunciado la compilación 27744 de Windows 11 Insider Preview. La nueva compilación actualiza el emulador Prism para permitir que las computadoras portátiles y de escritorio con chips Arm como la serie Snapdragon X ejecuten aplicaciones y juegos no compatibles.
Microsoft dice que el Prism actualizado podrá ejecutar más aplicaciones x86 (x64) de 64 bits agregando soporte para más funciones de CPU que se emularán. Esto significa que software como Adobe Premiere Pro 2025, así como títulos de juegos más antiguos, ahora funcionarán en estos dispositivos.
Los complementos de CPU recién agregados con esta versión del emulador incluyen AVX, AVX2, BMI, FMA, F16C y más. El gigante tecnológico destacó que, si bien estos accesorios no son necesarios para ejecutar Windows, se han vuelto tan populares que algunas aplicaciones esperan que los dispositivos los admitan. En particular, durante la vista previa, solo las aplicaciones x64 podrán utilizar estas nuevas funciones de CPU.
Aparte de esto, Microsoft también ha introducido una nueva distribución del teclado del gamepad en pantalla en Windows 11 Insider Preview. Admite la posibilidad de utilizar el controlador Xbox de los usuarios para navegar y escribir. El teclado también viene con botones aceleradores y tiene alineación vertical para mejorar la navegación del controlador.
Además de esa nueva ventanas 11 El lanzamiento también aborda varios errores y fallas técnicas. El diseño del cuadro de diálogo Desconectar y cerrar sesión en el Administrador de tareas ahora admite el modo oscuro y la escala de texto. También se solucionó otro problema por el cual las computadoras con GPU Nvidia más antiguas hacían que la pantalla apareciera en negro.
microsoft La controvertida función Windows Recall ha vuelto. El gigante tecnológico hizo recientemente el anuncio en su sitio. blog oficial La función estará disponible para los evaluadores del programa Windows Insider en octubre de 2024. Una vez que esté lista, se implementará en todas las PC Copilot Plus, aunque no se ha especificado un período de tiempo específico.
Para darle una actualización rápida, Llamar a Windows Esta característica inicialmente recibió muchas críticas cuando se anunció en mayo. Utiliza inteligencia artificial para tomar capturas de pantalla de todo lo que haces en ventanas 11 Computadora, con el fin de llevar un registro detallado. El usuario puede, por ejemplo, retroceder y localizar archivos perdidos o recordar conversaciones anteriores.
La empresa aseguró a todos que era completamente seguro, pero la gente no lo creía. No confiaban en la marca por su mala fama. Mantenga seguros los datos del usuarioEl investigador de ciberseguridad Alexander Hagenah ha demostrado que es realmente fácil Malware para robar todos los datos Dentro del almacén de llamadas.
microsoft Entonces recordé (nunca mejor dicho) esta característica poco después.
Posible paquete de actualización
El pasado mes de junio, la empresa publicó una actualización en su sitio web explicando todo lo que pretende hacer con la herramienta. Microsoft dijo que inicialmente dejará claro que los usuarios pueden optar por no participar en el retiro. Estará desactivado de forma predeterminada.
Inscribirse en Windows Hello se convertirá en un requisito antes de activar la herramienta y se agregarán capas adicionales de protección. Su publicación afirma que “las capturas de pantalla solo se pueden descifrar y acceder a ellas cuando el usuario se autentica”.
Microsoft también analiza brevemente la nueva experiencia de usuario. La ventana Recuperar se fijará en la barra de tareas cada vez que se guarden capturas de pantalla y podrá eliminar las imágenes en cualquier momento.
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Análisis: una gran preocupación
Aparte de estas descripciones, se desconoce si se agregará o cambiará algo más con el relanzamiento. Se suponía que esto se publicaría en las semanas posteriores al primer retraso; Sin embargo, esas semanas rápidamente se convirtieron en meses. Microsoft no dice por qué tuvo que posponer el retiro hasta después de eso, aunque promete publicar más detalles en octubre.
Nuestra principal preocupación es la disponibilidad del nuevo paquete para los evaluadores. No hay garantía de que los evaluadores tengan acceso a todos los cambios. Todos los cambios pueden estar disponibles o solo uno. Además, es posible que el nuevo paquete no se vuelva a lanzar este año.
Las pruebas del programa Windows Insider suelen tardar entre semanas y meses antes de ver el lanzamiento oficial. Si Microsoft necesita hacer muchos ajustes, es posible que Recall no se lance hasta el próximo año.
La precisión de la película incluye la reticencia de Wallace a hacer frente a las decisiones de CBS, al menos según Bergman:
“Desde el principio, Mike no estuvo dispuesto a comprometerse a seguir la línea con este asunto y arriesgar su trabajo. En las reuniones con los consultores de CBS, no levantó la voz ni hizo nada que pudiera desestabilizar a la empresa”. [’60 Minutes’ creator] Don Hewitt [played in “The Insider” by Philip Baker Hall]”Ambos eran conocidos por amenazar a la gente en privado y en público. No pronunciaron una sola palabra en la reunión”.
Bergman dejó “60 Minutes” en 1998 (la última toma de “The Insider” es una toma de Pacino saliendo de CBS). Trabajó como consultor en “The Insider” y dijo que sus relaciones profesionales con Hewitt y Wallace nunca se recuperaron. (Hewitt dijo en 2000 que a Bergman “no se le debería permitir acercarse a cien millas de una sala de redacción”).
En 2001, cuando la historia aún no estaba del todo resuelta, Bergman recordó:
“Una vez intenté hablar y reunirme con Mike después de que me denunciara por leer el guión. [of The Insider]”Pensé que estábamos iniciando una conversación civilizada. Lo siguiente que supe, su versión de la reunión fue que yo había llegado a su apartamento en Nueva York a cuatro patas exigiendo que me devolvieran mi trabajo”.
Hablando sobre el legado general de Wallace a The Wrap, Bergman lo llamó “pionero” y “es muy difícil trabajar con él”, pero lo elogió por su valentía periodística: “A fin de cuentas, si es un trabajo difícil, él Probablemente lo haga.” “Mike fue valiente”.
Por supuesto, Wallace debería haber sabido que uno de los riesgos profesionales del periodismo es que a veces el sujeto no aprecia lo que escribe.
The cybersecurity risks that businesses are often most concerned about come from external attacks. But at the same time, threats – both by accident or with malicious intent – by their own employees are overlooked, despite accounting for 58% of cybersecurity breaches in recent years.
As a result, a large proportion of businesses may lack any strategy to address insider risks, leaving them vulnerable to financial, operational and reputational harm.
Understanding the risk
Insider threat has always had the mystique of espionage and spies – but usually it’s nothing of the sort. At one end of the spectrum, you’ve got people who are trying to get access to company data and then accidentally share information, or disgruntled employees. And on the other end, you have nation state actors who could be attempting to access sensitive information from government and corporations or disrupt critical national infrastructure.
It’s a delicate issue for businesses to tackle, because anybody could intentionally or unintentionally be an insider threat, and a balance must be found between the security of an organization versus the personal liberty of an individual.
The first obstacle to implementing effective cybersecurity strategies is when the risk at hand is not fully understood. How do you determine what kind of protective controls you put into place to stop the potential exfiltration of data or disruption when there are so many different motives and methods?
Paul Lewis
Paul Lewis, CISO, Nominet.
Detection, not surveillance
Firstly, a line should be drawn between employee monitoring for possible signs of insider risk and employee surveillance. The latter could have a negative impact on company culture, and ignores the important balance between security and liberty and the legal safeguards that exist.
That being said, some form of threat mitigation and detection should still be in place. One useful tool in the armory is web content URL filtering that blocks malicious websites, for example if you click on a phishing email, or accidentally visit a malicious website and inadvertently open your organization to risk. Technology like this typically works hand in hand with Data Leakage Prevention (DLP). DLP uses keywords and analytics to look for data or information that is sensitive, such as credit card numbers or personally identifiable information and blocks that information leaving the organization.
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Because these types of tools can effectively track browsing habits, they must be tightly controlled and only a small number of people in an organization should have access to that data. Even so, that must go through multiple layers of approval. Business leaders must trust their employees, demonstrate that they do, and only use these tools as safety nets. It’s better to try and detect, protect, and solve the problem.
Put effective intervention methods to use
Background checks and vetting are important measures for mitigating the possibility of an insider threat from the very outset. But when it comes to managing an existing team, other methods will have to be explored. For systems and services, audit records and the cyber equivalent of double entry book-keeping should be considered, for instance.
Organizations that are more mature may use honeypots or canary tokens to decoy information on their system that looks sensitive but is fake; if anybody accesses this system or releases information, it can be tracked very easily and, if disturbed, is a good indicator of an insider threat.
Adopting a deterrence strategy is also useful, such as information classification. Systems with a large amount of sensitive information stored in them, data that could be sold or retained to use against someone, are going to be clear targets for insiders. A protective marking on it, such as “confidential”, could either entice or deter these individuals, as it makes clear that certain information is important, tracked and handled cautiously. This allows organizations to ring fence and apply controls to the specific information that is sensitive to them.
Responding to an insider incident
Incident response to insider threats is very similar to other types of data breaches, but with one significant caveat. As an employee they are by default a trusted individual. Therefore, they are potentially able to do significantly more damage than an external threat actor as they know the internal workings of the company and their way around potentially complex systems. Revoking complete access for any employee, for instance, should be a matter of priority when trying to mitigate the impact of any insider threat when suspected of carrying out a malicious breach.
Reporting the incident is ultimately the same type of process, but the way organizations initially approach the individual will differ from third-party actors. It’s especially important, in these circumstances, to have irrefutable evidence, as accusing somebody who is innocent could also cause significant damage to a business and the individual.
Insider threats too often sit in the blind spot of businesses. But by focusing on external threats exclusively – perhaps in favour of avoiding tension or perceptions of mistrust in the workplace – organisations and their employees are left vulnerable to the genuine threat insiders pose, often greater than the threat posed by third-party actors. It is a crucial element of any robust cyber strategy, and not to be overlooked.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
Imagine a future where machines think like us, understand like us, and perhaps even surpass our own intellectual capabilities. This isn’t just a scene from a science fiction movie; it’s a goal that experts like Scott Aaronson from OpenAI are working towards. Aaronson, a prominent figure in quantum computing, has shifted his focus to a new frontier: Artificial General Intelligence (AGI). This is the kind of intelligence that could match or even exceed human brainpower. Wes Roth explores deeper into this new technology and what we can expect in the near future from OpenAI and others developing AGI and Scaling Laws of Neural Nets.
At OpenAI, Aaronson is deeply involved in the quest to create AGI. He’s looking at the big picture, trying to figure out how to make sure these powerful AI systems don’t accidentally cause harm. It’s a major concern for those in the AI field because as these systems become more complex, the risks grow too.
Aaronson sees a connection between the way our brains work and how neural networks in AI operate. He suggests that the complexity of AI could one day be on par with the human brain, which has about 100 trillion synapses. This idea is fascinating because it suggests that machines could potentially think and learn like we do.
OpenAI AGI
There’s been a lot of buzz about a paper that Aaronson reviewed. It talked about creating an AI model with 100 trillion parameters. That’s a huge number, and it’s sparked a lot of debate. People are wondering if it’s even possible to build such a model and what it would mean for the future of AI. One of the big questions Aaronson is asking is whether AI systems like GPT really understand what they’re doing or if they’re just good at pretending. It’s an important distinction because true understanding is a big step towards AGI.
Here are some other articles you may find of interest on the subject of Artificial General Intelligence (AGI) :
Scaling Laws of Neural Nets
But Aaronson isn’t just critiquing other people’s work; he’s also helping to build a mathematical framework to make AI safer. This framework is all about predicting and preventing the risks that come with more advanced AI systems. There’s a lot of interest in how the number of parameters in an AI system affects its performance. Some people think that there’s a certain number of parameters that an AI needs to have before it can act like a human. If that’s true, then maybe AGI has been possible for a long time, and we just didn’t have the computing power or the data to make it happen.
Aaronson also thinks about what it would mean for AI to reach the complexity of a cat’s brain. That might not sound like much, but it would be a big step forward for AI capabilities. Then there’s the idea of Transformative AI (TII). This is AI that could take over jobs that people do from far away. It’s a big deal because it could change entire industries and affect jobs all over the world.
People have different ideas about how many parameters an AI needs to reach AGI. These estimates are based on ongoing research and a better understanding of how neural networks grow and change. Aaronson’s own work on the computational complexity of linear optics is helping to shed light on what’s needed for AGI.
Scott Aaronson’s insights give us a peek into the current state of AGI research. The way parameters in neural networks scale and the ethical issues around AI development are at the heart of this fast-moving field. As we push the limits of AI, conversations between experts like Aaronson and the broader AI community will play a crucial role in shaping what AGI will look like in the future.
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In the ever-evolving landscape of digital security, organizations are facing an uphill battle against cybersecurity threats. With the stakes higher than ever, the emergence of Artificial Intelligence (AI) and machine learning technologies is providing a new line of defense. These advancements are particularly effective when combined with User Behavior Analytics (UBA), a method that scrutinizes how users interact with systems and data. This powerful alliance is reshaping the way businesses protect themselves from the costly repercussions of insider threats and data breaches.
The latest research, including findings from IBM’s 2023 Cost of a Data Breach Report, underscores the significant toll that insider incidents can take on a company’s time and finances. To combat these issues, AI and machine learning are stepping into the spotlight. These technologies have the ability to analyze vast datasets, identifying patterns and irregularities that might otherwise go unnoticed by human analysts. By harnessing AI and machine learning, organizations can enhance their detection capabilities, identifying potential insider threats with greater speed and accuracy. UBA takes this a step further by monitoring user behavior and establishing a baseline—typically over a period of at least 7 days—to pinpoint deviations that could indicate a security threat.
Protect your business from insider threats
Here are some other articles you may find of interest on the subject of artificial intelligence :
The integration of UBA with Security Information and Event Management (SIEM) systems, such as Q Radar SIEM, significantly strengthens an organization’s threat detection and response capabilities. A UBA dashboard provides a comprehensive overview of user activities, risk levels, and detailed information on potential security incidents. This enables security teams to assess user risk, create watch lists, and manage alerts more effectively. Moreover, UBA applications are instrumental in generating offenses and offering high-level insights into security threats by correlating events and IP addresses. This results in actionable intelligence that helps security professionals quickly address and mitigate risks.
One of the key benefits of incorporating AI into security investigations is the acceleration of the analysis process. This allows security teams to shift their focus to developing proactive defense strategies. AI-powered tools offer access to insights in natural language and visual relationship graphs, which deepen the understanding and investigation of alerts. This not only shortens response times but also enables a more comprehensive analysis of threats.
Q Radar SIEM
The role of the security professional is crucial in refining the SIEM system’s analysis and future responses. A feedback loop is essential for the system to evolve continuously and effectively counteract emerging threats. This ongoing process of adaptation and improvement is vital for maintaining a robust defense against the sophisticated tactics employed by malicious insiders.
The use of UBA to analyze user behavior and identify anomalies that may indicate potential threats.
Integrate UBA with Security Information and Event Management (SIEM) solutions to enhance threat detection and response.
UBA learns user patterns over a period of at least 7 days to detect suspicious activities.
UBA can provide detailed information about an employee’s behavior, including identities, offenses, timelines, and indicators of compromise (IoCs).
Use UBA app to generate offenses and provide high-level information about potential security threats, including correlated events and IP addresses.
Use the MITRE ATT&CK framework within the SIEM solution to map tactics and techniques during automated investigations.
Use natural language insights and visual relationship graphs to enhance the understanding and investigation of alerts.
The fusion of Q Radar SIEM with AI and automation represents a significant advancement in optimizing security operations. It equips security teams with the necessary skills and insights to address threats swiftly, encouraging a proactive approach to cybersecurity. By adopting these advanced technologies, organizations can concentrate on strengthening their defenses, ensuring a solid security posture against the risks posed by insider threats and data breaches.
As the digital world becomes increasingly complex, the importance of innovative security measures cannot be overstated. The combination of AI, machine learning, and UBA is a testament to the dynamic nature of cybersecurity. It is a clear indication that as threats evolve, so too must the strategies to combat them. The integration of these technologies into SIEM systems like Q Radar is a step forward for organizations looking to safeguard their assets and maintain their competitive edge in a world where security breaches can have far-reaching consequences.
In the realm of cybersecurity, the only constant is change. The threats that organizations face today may be vastly different from those they will encounter tomorrow. It is for this reason that the continuous improvement of security systems, through the integration of AI and UBA, is not just beneficial but necessary. The proactive stance enabled by these technologies allows organizations to stay ahead of threats, rather than simply reacting to them. This proactive approach is the cornerstone of a strong cybersecurity strategy, one that can adapt to the ever-changing landscape of digital threats.
In essence, the marriage of AI-enhanced UBA with SIEM systems represents a significant stride in the ongoing battle against cybersecurity threats. It is a clear demonstration of how technology can be leveraged to create more secure environments for businesses and their data. As organizations continue to navigate the complexities of digital security, the tools and strategies they employ will be critical in determining their success in thwarting insider threats and preventing data breaches. The integration of AI and UBA into cybersecurity practices is not just a trend; it is an essential component of a modern, resilient approach to protecting an organization’s most valuable assets.
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