AI inference-ready networks are essential infrastructure for turning AI's potential into performance. The Ryder Cup is an almost-century-old tournament pitting Europe against the United States in an elite showcase of golf skill and strategy. At the 2025 event, nearly a quarter of a million spectators gathered to watch three days of fierce competition on the fairways. From a technology and logistics perspective, pulling off an event of this scale is no easy feat. The Ryder Cup's infrastructure must accommodate the tens of thousands of network users who flood the venue (this year, at Bethpage Black in Farmingdale, New York) every day. To manage this IT complexity, Ryder Cup engaged technology partner HPE to create a central hub for its operations.

Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities. At the same time, AI/ML-based traffic/load prediction is one of the most appealing data-driven solutions to improve the Wi-Fi experience, either through the enablement of autonomous operation or by boosting troubleshooting with forecasted network utilization. In this paper, we study the suitability and feasibility of adopting AI/ML-based load prediction in practical enterprise Wi-Fi networks. While leveraging AI/ML solutions can potentially contribute to optimizing Wi-Fi networks in terms of energy efficiency, performance, and reliability, their effective adoption is constrained to aspects like data availability and quality, computational capabilities, and energy consumption. Our results show that hardware-constrained AI/ML models can potentially predict network load with less than 20% average error and 3% 85th-percentile error, which constitutes a suitable input for proactively driving Wi-Fi network optimization.

Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack. Lateral Movement intrusions have become more intricate with the increasing complexity and interconnected nature of enterprise networks, and require equally sophisticated detection mechanisms to proactively detect such threats in near real-time at enterprise scale. In this paper, the authors propose a novel, lightweight method for Lateral Movement detection using user behavioral analysis and machine learning. Specifically, this paper introduces a novel methodology for cyber domain-specific feature engineering that identifies Lateral Movement behavior on a per-user basis. Furthermore, the engineered features have also been used to develop two supervised machine learning models for Lateral Movement identification that have demonstrably outperformed models previously seen in literature while maintaining robust performance on datasets with high class imbalance. The models and methodology introduced in this paper have also been designed in collaboration with security operators to be relevant and interpretable in order to maximize impact and minimize time to value as a cyber threat detection toolkit. The underlying goal of the paper is to provide a computationally efficient, domain-specific approach to near real-time Lateral Movement detection that is interpretable and robust to enterprise-scale data volumes and class imbalance.

To meet expectations of performance and reliability for next-gen applications for the hybrid workforce, IT needs to reimagine network management using AI and ML solutions to provide extended visibility and proactive mitigation. To meet expectations of performance and reliability for next-gen applications for the hybrid workforce, IT needs to reimagine network management using AI and ML solutions to provide extended visibility and proactive mitigation.

With this level of interaction, a new identity problem is emerging as machines operate on behalf of humans. Collaboration between humans and machines is a working reality today. Along with this comes the need for secure communication as machines operate increasingly on behalf of humans. While people need usernames and passwords to identify themselves, machines also need to identify themselves to one another. But instead of usernames and passwords, machines use keys and certificates that serve as machine identities so they can connect and communicate securely.

Malicious attackers have turned to AI to invade enterprise networks. To combat attacks, organizations need to embrace AI in turn. Join this VB Live event to learn more about the powerful, proactive AI security solutions that are enabling intelligent threat detection and response, security operations and maintenance, and more. Check off another consequence of COVID: It's directly responsible for the uptick in security risks for organizations. Many companies were forced to accelerate digital transformation, adopting brand-new technologies and policies to meet pandemic challenges.

Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls

Cyber attacks and threats are considered major disruptors to businesses, nations and consumers alike. Artificial intelligence is seen as a major disruptive force too, but of the positive kind, fuelling a new era of hyper connectivity, hyper intelligence and hyper performance. An increasingly complex business environment is leading organisations to embrace forms of artificial intelligence such as machine learning and facial recognition technology, while using data to build more intimate relationships with consumers. But the flip side of these innovations is that the'attack surfaces' of an organisation are multiplying, creating a fast-growing world of vulnerability to cyber crime that didn't exist before. At the same time, AI use is on the rise among cyber criminals, who are using it to help drive attacks, employing the technology to uncover unsecured points of entry in enterprise networks.

By using a combination of machines, sensory devices, embedded computational intelligence and various communication mechanisms, CPS monitor physical elements with computer-based algorithms tied to the internet. This means they are capable of autonomously functioning based on their physical surroundings. In light of the advancements in analytics, artificial intelligence (AI) and communications, there is an increased demand for intelligent machines that can interact with the environment around them, such as driverless cars which monitor and communicate with their surroundings, and smart appliances that optimise energy consumption. CPS are stimulating significant changes in quality of life and forming the basis of smart infrastructure, products, and services. As this kind of technology continues to become more integrated into our everyday lives, here are four areas of CPS we can expect to come to the fore.

My passion for security came to the forefront when I ran the Security business at Juniper. Given this confluence of factors, the opportunity to apply Artificial Intelligence (AI) to dramatically innovate how we could detect and respond to advanced attacks (the new norm), created an incredible opportunity to build a next-generation security company. Which directly led to the creation of Vectra. Digital Transformation can become a task rife with complexities and it is no surprise that security risks become a byproduct of that. Effective Network Detection and Response (NDR) provides visibility that simplifies complexity, replacing it with the confidence that a customer's security capabilities will enable, not inhibit, their journey – legacy methods disproportionately focused on prevention are brittle and frankly designed in such a way that makes this journey needlessly costly and difficult.