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Network Infrastructure Magazine | Saturday, June 03, 2023
As businesses undergo rapid digital transformations, the employment of cloud technologies and the growing complexity of cyber threats push companies to reevaluate their network security strategies. The role of artificial intelligence (AI) in network security is crucial, particularly machine learning algorithms for anomaly detection, performance optimization, and threat identification.
FREMONT, CA: A swift digital transformation is sweeping various businesses. The employment of cloud technologies and the rising complexity of cyber threats have pushed businesses to overhaul their network security strategies. As companies continue to keep up with innovative technologies and aim to maintain secure and robust networks, the significance of artificial intelligence (AI) in maintaining the network security footprint becomes more prominent.
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Over the recent months, the topic of AI has garnered a lot of attention in Europe. The technology brings with it numerous advantages and for tech and business leaders, it brings about the temptation to implement this technology into their businesses. Regardless, the real strength of AI lies within machine learning (ML) algorithms, which function behind the scenes of the technology and can be leveraged to take advantage of the infrastructure environment in numerous ways.
For instance, AI can be used to evaluate and analyse historical and real-time data to detect patterns, boost network performance, and recognise anomalies. It is only through anomaly identification that important values can be discovered. This encompasses capitalising on AI-driven tools to identify unknown behaviour in network traffic, leading to early detection of performance and potential security threat problems.
Various vendors provide network solutions that include anomaly detection within their AI platforms. Some others take it to the next level by executing behavioural monitoring on users, alerting them to potential security and operational challenges before they detrimentally impact the company’s infrastructure.
An approach taken to improve the network is software-driven, and it has resulted in the rising popularity of software-defined wide area networking (SD-WAN) orchestration and network management in the past few years. The capability to gain greater functionality from established hardware by using swiftly evolving software, supported by AI and ML, has unlocked a new level of potential.
Furthermore, this has undermined the importance of AI in secure access service edge (SASE) and SD-WAN solutions. Its ability to actively avoid network downtime and security breaches has become irreplaceable in today’s world of connected companies.
Utilise SASE
SASE is a rising cybersecurity framework that merges various network security functions, such as secure Web gateway, firewall-as-a-service, and zero-trust network access along with wide-area network (WAN) capabilities. Its purpose is to provide a unified, single solution for optimising and securing network traffic across different branches and remote users.
AI is crucial in the improvement of SASE’s network security features. By using machine learning algorithms, SASE can analyse large amounts of data to recognise correlations and patterns, which can aid in the detection of potential threats in real time. AI-powered SASE is also capable of adapting to new threats, allowing companies to remain one step ahead of malicious actors.
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