Network Computing

In the rapidly evolving landscape of network security, the integration of Artificial Intelligence (AI) has emerged as a game-changer. By employing advanced algorithms and machine learning, AI-powered network security has ushered in a new era of proactive security defense, enabling real-time threat detection, adaptive responses, and predictive analytics.

 

 

In today’s AI-driven landscape, modern AI infrastructure faces several core challenges: handling massive data volumes, managing high-speed GPU-accelerated computations, and ensuring low-latency networking across AI resources. These  infrastructures must support parallel processing for demanding tasks like AI training and inferencing, necessitating an advanced, high-throughput network to deliver seamless, secure, and efficient AI workloads.

AI-driven threat detection plays a crucial role in today’s OT/IT cybersecurity. These network security measures are capable of rapidly processing large datasets in order to detect patterns and anomalies that indicate potential security breaches. Machine learning algorithms, for instance,  can spot unusual traffic patterns that point to a likely DDoS attack.

A mid-sized construction company was having a tough time dealing with several cybersecurity challenges in spite of their existing security measures, one of these challenges was keeping up with the volume of security alerts generated by their security tools, not to mention the existing network security team and measures lacked the experience and expertise in dealing with the more sophisticated threats. They found Lanner while seeking for a robust hardware solution on which an AI-enhanced managed detection and response (MDR) service can be built for enabling advanced threat detection, continuous monitoring and real-time response capabilities.

Nowadays, cybersecurity companies are increasingly turning to AI and machine learning to enhance malware detection, as traditional signature-based methods prove insufficient against evolving threats. The AI engine analyzes vast amounts of security data to identify trends, anomalies, and predict potential threats, enabling proactive measures. It establishes a baseline of normal behavior and monitors for deviations, facilitating early detection.

In the wake of the COVID-19 pandemic, many industries have faced the challenge of adapting to hybrid work environments, including sectors that rely heavily on data-intensive operations. Among these, the video game development industry stands out due to its need for handling large, latency-sensitive files and its requirements for high bandwidth.

In today's rapidly evolving cybersecurity landscape, enterprises require advanced network security solutions that can provide comprehensive protection across their network infrastructure. Extended Detection and Response (XDR) is a comprehensive cybersecurity approach that expands upon traditional detection and response capabilities to provide enhanced protection against advanced threats across multiple attack vectors.

Driven by the global pandemic, the retail sector has transformed significantly because of shifts in customer transaction patterns and workforce practices. Stay-at-home orders and safety concerns prompted a surge in online shopping for a broader range of goods.

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