August 10, 2020
A few weeks ago, Ivar Beljaars, our VP of Sales and Business Development, discussed why growth-oriented telecom operators invest in AI to deliver new Value-Added Services (VAS).
Since the fastest growth in this industry comes from VAS delivered over mobile networks, the demand for artificial intelligence in network security is growing as well.
In this article, I review the reasons US telecoms like Comcast, Charter Communications, Verizon and others use AI algorithms – including CUJO AI solutions – for developing top-line end-user network security services.
Artificial Intelligence Is a Superior Network Security Solution
Machine Learning (ML) algorithms are in the spotlight when it comes to scalable technology, due to the absence of limitations that traditional rule-based security solutions have.
Major telecoms are already relying on AI-based platforms and solutions for network security, making it the baseline or even the best practice.
Speed and Capacity
AI-driven security provides real-time protection against occurring anomalies and other malicious activities (such as suspicious connection at an unusual time, e.g., files extraction during the weekend), therefore providing critical response way earlier than a human would do manually.
From the end-user perspective, this is what they’d expect with the Internet service by default – seamless, real-time protection and immediate response to threats as they emerge.
Here at CUJO AI, we’ve been believing in and discerning the effectiveness of ML algorithms for years. These advanced ML algorithms are the foundational core of our SENTRY digital security service: They enable the whole system to analyze complex data sets within seconds and make decisions without any human intervention, therefore providing proactive and reliable responses to swiftly emerging and even globally yet unknown cyberthreats.
Reliability and Effectiveness
AI-driven solutions perform on a 24/7 basis, without any interruptions or cognitive limitations, hence providing a reliable and effective security baseline for the enterprises in their network monitoring.
End users can be sure that their Internet Service Provider (ISP) is as protective over the night shift or on holidays as it is during business hours. AI-based security systems can also better handle increased loads – an ability which has proven crucial as the COVID-19 pandemic forced the world to move to the work-from-home era (in addition to a surge in active cyberattacks).
Before the AI evolved and was adapted to security needs, sophisticated malicious activities couldn’t be controlled, and threat actors were able to easily hide their activity. Now AI helps to monitor and detect deviations from normal network behavior (network or single user’s baseline) and ML algorithms empower systems to thwart the actual malicious actions performed by threat actors in real time, therefore lowering or even fully preventing the damage caused by a cyberattack.
Of course, there is always an opposing opinion stating that AI will never fully replace human-supported systems from a cognitive perspective, but in reality, we strongly believe that it does not need to.
When experienced cybersecurity experts provision ML-based algorithms, it allows them to avoid time-consuming, repetitive tasks and instead concentrate on the meaning of emerging tendencies – for example, to make a conclusion on a set of Tactics, Techniques and Procedures (TTPs) currently used by a threat actor. As a result of such AI and analyst tandem, the results are much more promising and insightful than those found using traditional systems – it can not only provide reliable protection but also proactively predict upcoming cyberthreats – and CUJO AI’s SENTRY solution is no exception.
From the perspective of the ISP, AI-based network security solutions allow a lowered expense margin as well as ensure that operations are less dependable on external factors.
AI-driven technology shows the potential of addressing new threats and risks that require machine speed and reliability rather than human interaction. Time-consuming data analysis tasks can be performed in a timely manner using the computational power of ML algorithms.
Prospects for AI Network Security Systems
AI-driven solutions tend to be more reliable than traditional systems since they eliminate the cognitive limitations of a human analyst, which are inevitable when human beings analyze continuous or complex data structures. With the help of ML algorithms, the possibility for an unexpected failure is significantly lowered.
In addition, cybercriminals are using AI and ML to launch their attacks. This means that AI/ML-driven cybersecurity solutions are no longer simply nice to have. Instead, they are crucial to stop modern, well-resourced and sophisticated-as-never-before cyberattacks.
AI empowers ISPs to detect threats as well as react and block malicious activities on their networks within minutes, as opposed to the previously standard processes where a simple phishing campaign could successfully run for days until a human analyst interacted with the phishing website. The difference is real, and users want that value – and as telecom operators increasingly offer AI-based network security services to their users, they are also ready to pay for that difference.
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