Palo Alto Networks Bringing Machine Learning to Ruggedized 5G Environments

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By Georgia Cooke | 2Q 2024 | IN-7345

Palo Alto Networks announces a new ruggedized PA-400R Series offering, extending Machine Learning (ML)-powered Next-Generation Firewall (NGFW) security to extreme environments.

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Palo Alto Networks Announces ML-Powered Ruggedized 5G Security


Palo Alto Networks has announced the introduction of three new appliances in its PA-400R NGFW Series, bringing the total to four ruggedized appliances. Proclaimed as the “World’s First ML-powered NGFW,” the PA-400R Series offers an attractive range of appliances for customers looking to secure environments with a wide range of temperature, humidity, and dust. With demand from the oil & gas, mining, and energy sectors, these devices from an industry leader are poised to have a major impact as 5G adoption reaches remote regions, with customers having a range of appliances to choose from.

Palo Alto Networks now offer both an integrated 5G modem and Small Form-factor Pluggable (SFP) fiber access for ruggedized NGFWs as well as DIN-rail mounted form factor models. With PAN-OS Software-Defined Wide Area Network (SD-WAN) supported over 5G and fail-to-wire, customers will find the range of features extremely competitive. Combined with Palo Alto Networks’ industry-leading Operational Technology (OT) security, the PA-400R Series provides comprehensive security available for ruggedized networks seeking to secure public and private infrastructure from both known and unknown attacks, and to ensure network availability and uptime.

5G Security: A Market Primed for Onboard ML


This announcement is a significant disruption to the ruggedized NGFW market, with Palo Alto Networks’ emphasis on Artificial Intelligence (AI) being especially crucial to the long-term future of 5G security. 5G networks represent a colossal security management challenge due to their dynamism, multi edge access and end-device variety. While the computational limitations of edge devices means that true 5G networking is not yet possible on these compact appliances, this move from an industry innovator could herald the future of 5G security. With cybercriminals exploiting vulnerabilities increasingly quickly, firms must embrace new technologies to stay ahead in the security arms race. On-device Machine Learning (ML) empowers inline attack prevention, even for unknown new threats, and automates policy recommendations to offload management burden and improve security. As hardware technology develops, and ML models become increasingly compact, the first company to fully integrate ML in the ruggedized 5G space will have a significant advantage.

The Road to Adoption


The targeted sectors for ruggedized security are extremely lucrative, and under great security threat. Confidential operator information from these environments is a prime target for cyber criminals, with very successful ransomware attacks such as the Colonial Pipeline attack perpetrated in recent years. Oil companies, in particular, should also consider the additional threat from hacktivists. Given the edge computing model of 5G networks, investing in environment-appropriate physical security at the network gateway is essential in order to avoid breaches. ML-powered solutions that automate the threat management process are particularly important in these environments, given the difficulties of supplying dedicated additional technicians.

However, 5G coverage is still limited in many of the extreme environments ruggedized customers operate in, but provision is accelerating in sectors such as mining, where 93% of companies are either investing in or planning to invest in 5G networks. 5G networks are already prevalent in regions such as Saudi Arabia, where 13% of companies are rated as 5G mature. Globally, coverage is increasing, with all countries studied in recent ABI Research analysis accelerating at over 20% annually.

Technical benefits of ML-powered physical security are clear, but commercial challenges in conjunction with slow 5G adoption make the market future of these devices uncertain. While ML is primed at a high level to address the management challenges of a 5G network, the exact implementation technicalities remain to be seen. Adding capabilities in hardware automatically increases the attack surface, so the real-world security benefits of the ML addition must be proven. The hardware requirements of supporting ML capability vary, but are likely to increase cost, further driving the need for Palo Alto Networks to educate consumers on the benefits to exploit its “world-first” position. This release demonstrates Palo Alto Networks’ status as a world-leading innovator, in concurrence with the findings of a Competitive Ranking published by ABI research in October 2023. However, end users must be convinced of the promise of ML-enabled security in this sphere to achieve successful adoption.


Companies Mentioned