Cyber threats are growing more complex and difficult to prevent with each passing day. Cybercriminals are now using cutting-edge technology and advanced hacking techniques for exploiting security endpoints and infiltrating software.
Since endpoints 70% of all security breaches originate from endpoints, endpoint security is a significant concern for organizations worldwide. To combat this increasing frequency of cyber attacks on IT endpoints, many companies have started to leverage AI-based solutions.
With the help of AI-based detection and response technologies, it’s easier to implement cloud-native endpoint protection solutions effectively against a wide range of endpoints.
These solutions can help businesses to design greater persistence and resilience against endpoint security threats. In this article, we will examine how AI and machine learning can help businesses enhance endpoint security.
What is Endpoint Security?
Endpoint security is an approach to cybersecurity that focuses on the protection of computer networks remotely connected by client devices. In other words, we use the technique to safeguard the security of networks by monitoring and protecting every user device, workstation, mobile devices, and other connected devices individually.
Each of these devices is considered a single endpoint. However, the connection of these devices to the network also develops attack paths for cyber threats. In endpoint security, we use a central platform to manage and monitor each endpoint of the network. Therefore, we need to install a client or agent in each one of the endpoints.
Endpoint security can address the following challenges related to cybersecurity.
- Data classification
- Network access control (NAC)
- Endpoint encryption
- Endpoint detection & response
- User control
How Can AI and Machine Learning Revolutionize Endpoint Security
Artificial Intelligence and machine learning, it turns out, are effective against organized, automated, and targeted cyber attacks. With the cost of preventing endpoint attacks reaching $9 million per attack, the need for intelligent endpoint security solutions has never been higher. Here are some ways AI and machine learning are fortifying endpoint security.
Enhance IT Asset Management
Leveraging core features of machine learning can enhance IT asset management and improve device security Simultaneously. With the advent of interconnected networks, IT management and endpoint security have started to become more and more integrated. As a result, resolving endpoint security challenges is becoming increasingly difficult.
Many experts believe that endpoint security and IT management are just two sides of the same problem. This is why it’s best to start configuring security on devices at the BIOS level. Many security solutions such as Absolute Software use machine learning to standardize the data related to security advocacy analytics and asset analytics.
These solutions help IT security managers to understand why certain devices are performing at par. At the same time, these managers can have a clear overview of all endpoints, which can help them monitor security breaches more effectively.
Use Machine Learning to Compute Risk Scores
Machine learning algorithms are effective at analyzing behavioral patterns and deriving risk scores from these patterns. We can leverage these algorithms to analyze geolocation, behavioral patterns, time of login, and other similar variables to secure and control endpoints.
Supervised learning techniques can leverage historical data to classify elements based on the given data or use regression models to predict the behavior of certain elements. On the other hand, unsupervised learning techniques can help companies find interrelationships and anomalies between variables that are invaluable for supervised learning techniques.
We can combine both supervised and unsupervised learning techniques to optimize risk scores and identify doubtful behavior. This can help us thwart breach attempts, reduce fraud, stop the theft of access credentials, and secure each endpoint connected to the network.
Automating Security Analysis
According to Gartner, by the year 2025, 85% of successful breaches of modern enterprise user endpoints will result from user and configuration errors. This is why it’s essential for security analysts to perform security analysis in detail and implement all the best practices of endpoint security.
Machine learning has the potential to automate various manual routine incident analysis processes. These manual processes can be overwhelming for any security analyst. Leveraging machine learning to optimize algorithms according to the insight gained from incidence data can enhance endpoint security.
Identifying Malware
Machine learning can classify instances based on their data attributes. Therefore, machine learning algorithms can be used for detecting method and preventing malware attacks.
In the beginning, machine learning algorithms were used to improve endpoint security by contributing to the back-end of virus protection workflows. However, nowadays, endpoint security solutions use these algorithms as their primary malware detection techniques. The majority of advanced endpoint security solutions leverage ML-trained models to identify file-based malware in a system.
At the same time, they can learn which files are malicious by analyzing the metadata and content of particular files. Similarly, many endpoint security solutions use ML models, as well as static code, to thwart breach attempts and block threats before they infiltrate your system.
Monitoring Application Security
Machine learning algorithms can be extremely useful in determining which given application is unsafe to use. Upon detecting a malicious application, AI-powered solutions can isolate them inside containers so they don’t harm the entire system.
Depending on an application’s threat score, we can limit the device permissions given to these applications. In other words, these dynamic application security tools can block or log doubtful actions of an application, according to the security rules, set up by you. Similarly, many solutions are using predictive analytics to let users know the extent of danger an application might have.
Mobile Device Security
Mobile devices pose a significant threat to the security of organizations and enterprises. According to the Dimensional Enterprise Mobile Security Survey, digital security professionals feel the risk of data loss is higher on mobile devices, despite the cost of mobile braches being similar to desktop breaches. Therefore, mobile security represents a unique challenge for endpoint security control.
In most cases, cybercriminals steal mobile device to extract reserved access credentials and passwords, rather than hacking into an organization. Insecure passwords are the easiest way for hackers to steal sensitive information and sell it.
This is why many companies have started to abandon traditional passwords for techniques such as the zero sign-on approach. A zero sign-on approach leverages Unified Endpoint Management (UEM) to provision identity certifications for managed apps on mobile devices.
MobileIron – one of the pioneers of AI-based endpoint security also uses machine learning to identify endpoint security threats more effectively. The diagram below illustrates how the company incorporates machine learning in its existing UEM platform:
Ensuring Compliance
Compliance is a major issue in industries such as insurance, healthcare, and financial services. Organizations need to ensure that every endpoint adheres to all regulatory and internal standards.
We can use machine learning to identify, classify, and protect sensitive data. At the same time, it can help systems generate alerts, whenever some user tries to gain unauthorized access to sensitive data, which is essential for preventing data leaks.
Conclusion
With the rise of edge-computing and the Internet of Things (IoT), the need for strengthening endpoint security has never been higher. However, by leveraging AI and machine learning-based solutions, we can enhance endpoint security in our organizations and develop a comprehensive cybersecurity strategy.
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