As we all know, today, more and more vendors are using machine learning algorithms as the primary detection method to design endpoint security systems. The article illustrates the potential ways in which AI and ML are redefining endpoint security thus reducing the security breach.
- Understanding Artificial Intelligence
- What is machine learning?
- What is Endpoint Security?
- How AI and Machine Learning Redefine Endpoint Security?
Machine learning is one of the key building blocks of AI. Since the 1950s, artificial intelligence is a significant part of the technological world, right from when programmers asked computers to make sense of huge datasets. Modern applications of AI in today’s content include virtual assistants and self-driving cars. Thanks to AI, instances of fraud can be detected easily, and resources such as electricity can be managed more efficiently.
Some of the diverse industries that have started using AI and machine learning for their businesses are healthcare, education, marketing, retail and e-commerce, agriculture, recruitment and human resources, advertising, and much more. Apart from these industries, one more significant use case of AI and machine learning is in endpoint security. Let us now move on to understand the terms artificial intelligence, machine learning, and ‘endpoint security’ and the ways in which AI and machine learning redefine endpoint security.
Understanding Artificial Intelligence
Artificial intelligence deals with using computers to understand human intelligence, and it is the science and engineering that makes intelligent machines, especially intelligent computer programs. It refers to adding human capabilities into machines. AI initiates problem-solving, common sense, and analytical reasoning power in machines. Put simply, AI involves making computer programs that imitate human behaviour.
What is Machine Learning?
Machine learning refers to machines being able to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.
What is Endpoint Security?
In the field of network security, endpoint security is defined as the methodology of protecting a corporate network when accessed through remote devices like wireless devices, mobile devices, and laptops. Each device connecting to the network becomes a potential entry point for security threats. Usually, endpoint security is a system that comprises of security software that is located on a central managed or accessible gateway or server within the network.
How AI and Machine Learning Redefine Endpoint Security?
So, how do AI and machine learning enhance endpoint security?
Identifying and Stopping Malware Attacks
Machine learning has become the primary detection method to identify and stop malware attacks. Initially, AI and machine learning algorithms contributed to improving endpoint security by supporting the back-end of malware protection workflows. Algorithms trained by machine learning helps detect file-based malware and identify the files that are harmful and those that are not based on the metadata and content of the file. For example, Symantec’s Content and Malware Analysis shows how machine learning can be used to block and detect malware. It combines static code file analysis and machine learning to detect, analyze, and block threats and stop breach attempts before they occur.
Performing Real-time Scans of Processes
Another way in which AI and machine learning improve endpoint security is by conducting real-time scans of all processes that have an unknown or suspicious reputation. Supervised and unsupervised machine learning algorithms, commonly known as Hunt and Respond, are used today to identify and resolve potential threats in milliseconds instead of many days. Supervised machine learning algorithms are used to discover patterns in known or stable processes, where an anomalous behaviour or activity will create an alert and help pause the process in real-time. Unsupervised machine learning algorithms help analyze unstructured, large-scale datasets to visualize threat trends across the company, categorize suspicious events, and take immediate action across the entire organization or at a single endpoint.
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Accelerating the Consolidation of Endpoint Security Technologies
The Absolute Software’s 2019 Endpoint Security Trends Report has revealed that any device has more than ten security agents installed. Each of these will often conflict with the other. According to the study, enterprises use a wide array of endpoint agents such as Endpoint Detection and Response (EDR), encryption, AV/AM. These solutions make it almost impossible to standardize a specific test to ensure safety and security without reducing the speed. Machine learning accelerates the consolidation of security endpoints as it helps companies realize that the more complex and layered the endpoint protection, the more the risk of a breach.
Automating Tasks That Overwhelm Today’s Security Analysts
Endpoint security providers prioritize this area in product development, by capitalizing on the innate ability of supervised machine learning to fine-tune algorithms in milliseconds on the basis of incidence data analysis. There is now a strong demand from potential customers as almost everyone is facing a cybersecurity skills shortage while facing an onslaught of breach attempts.
Nowadays, endpoint security is becoming a common IT security function, and concern as more and more employees are bringing consumer mobile devices to their workplaces, and companies are allowing the mobile workforce to make use of these devices on the corporate network. The fastest-growing areas of security spending through the year 2023 are data security and infrastructure protection. These are the areas where AI and machine learning are already proving their potential by being effective technologies that help battle increasingly automated and well-orchestrated breach attempts and cyberattacks.
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