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A collection of over 250 uses for artificial intelligence

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Network Security Threat Detection

Introduction

In the telecommunications sector, it is crucial to focus on the network security due to the constant threats from various corners. The constant threat of cyber-attacks from different sources can lead to loss of data and service interferences. The industry is now relying on the use of machine learning (ML), natural language processing (NLP), and artificial intelligence (AI) to enhance its protection against these threats. The following technologies provide new ways of identifying threats which can be done in real-time through the analysis of large amounts of network data and recognizing certain patterns that may indicate a security breach.

Challenges

There are several issues that telecommunications companies encounter in network security threat detection. First, it is simply impossible to perform a manual analysis of all the data that is processed within their networks. Second, modern cyber threats are highly advanced and cannot be identified using basic detection methods. Signature-based and pattern-based detection methods are efficient only for known threats while zero-day threats and advanced persistent threats cannot be identified using these methods. Third, telecom companies have to meet security demands while providing fast and seamless services. Last but not the least, they also have to meet certain regulatory compliances which can pose a challenge to security.

AI Solutions

These challenges can be addressed by AI, ML, and NLP. There is a huge amount of information that is available on the network and ML algorithms are capable of identifying the anomalies or the unusual activities which could indicate a threat to security. NLP can be applied to unstructured data like emails or social media for phishing or social engineering. This is where AI can help in the process of threat detection and emulation, so that the humans can concentrate on strategic thinking. For example, the AT&T Alien Labs Open Threat Exchange (OTX) provides real-time threat information to over 100,000 participants in 140 countries and leverages AI and ML to identify threats. In a similar manner, Verizon applies AI and ML to secure its network and its customers against threats.

Benefits

There are a number of benefits that can be derived from the application of AI, ML, and NLP in network security threat detection when used in conjunction with telecom companies. It can also enhance the efficiency of detecting threats which makes the telecom companies to handle incidents promptly. It can also lighten the burden on human analysts and make them tackle the most challenging issues. Also, these technologies can assist telecom companies in complying with the existing legal standards since the systems are capable of generating precise accounts of security events. Last but not the least, they can enhance the customer satisfaction by minimizing the downtime due to security incidents.

Return on Investment

The use of funds for AI, ML, and NLP in network security threat detection can greatly improve the ROI for the telecommunications companies. A study by Capgemini state that AI has the potential of reducing costs that are incurred in managing cybersecurity by up to 20%. In addition, effective threat detection helps in avoiding the costly breaches and disruptions in services. For instance, a study by IBM revealed that the cost of a data breach in 2020 was $3.86 million on average. Thus, AI and ML are able to avoid such breaches and thereby lead to huge financial savings for telecom companies.

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