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.

 

 

Today’s cyber adversaries no longer need advanced expertise to carry out attacks. Although highly skilled attackers have historically achieved greater success, modern technology, the widespread availability of sophisticated tools, and reduced costs have lowered the barrier to entry. Even less experienced actors can now exploit these resources to devise new and effective methods of breaching user accounts, penetrating infrastructure, moving laterally within networks, and extracting sensitive data—often within hours rather than days.

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).

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.

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.

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