The global transition to renewable energy and the exponential growth of AI datacenters are placing new demands on smart power grid infrastructure. While renewable sources offer a sustainable path forward, they also bring variability and unpredictability to power generation. Simultaneously, the rise of generative AI and machine learning workloads is driving up electricity demand significantly. According to the International Energy Agency (IEA), the power consumption of U.S. AI datacenters is projected to reach approximately 34 GW by 2030, doubling from around 17 GW in 2023. This dual challenge—unstable renewable generation and energy-hungry AI infrastructure—is catalyzing large-scale modernization across global power substations and transmission systems.

Operating in remote and rugged environments is challenging on its own, but inconsistent network connectivity adds a serious layer of risk, jeopardizing both worker safety and operational efficiency. A petroleum refinery found itself grew increasingly dependent on connected devices to maintain smooth and safe operations; unreliable connectivity, however, frequently disrupted workflows and hindered performance.

In today’s competitive manufacturing landscape, ensuring consistent assembly quality and maintaining workplace safety are top priorities. Manual processes or hybrid human-machine workflows often introduce variability and risk that traditional monitoring systems fail to catch in real time.

Cryptanalytically relevant quantum computing (CRQC) refers to quantum computers powerful enough to break all currently used public-key cryptographic systems.

While this threat, commonly known as Q-Day, has not yet materialized due to current hardware limitations, many experts agree it's only a matter of time. In response, governments and proactive organizations are already preparing for the shift to post-quantum cryptography (PQC).

Urban mobility is increasingly strained by congestion, traffic violations, and inefficient parking—challenges that demand smarter, real-time traffic management. Lanner, together with DataFromSky, offers an advanced AI-powered solution that turns standard IP cameras into intelligent traffic sensors. By processing video at the edge, directly inside traffic controllers, this joint solution delivers immediate insights and automated control capabilities for modern cities.

Vision AI is transforming construction site management by enabling real-time monitoring, enhancing efficiency, ensuring worker safety, and improving quality control. By leveraging AI-driven video analytics, construction firms can gain valuable insights into site activities, optimize resource allocation, and maintain compliance with safety and regulatory standards.

Web services-based applications have an important presence in public and private organizations. Vulnerabilities stemmed from these types of applications may give rise to unforeseen risks to the business model of these organizations. These applications have the inherent risk of being used by organizations in such a way that their activity is affected, resulting in becoming the main entry point for attackers, leading to security breaches.

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