6 Ways AI and ML Together Transforming Endpoint security in 2020?

In a time and age where we are asking people to bring their own devices or, where people are accessing important documents remotely, it has become important to secure their emails, laptops as well as mobile and other devices that are being used. Constant security threats and increasing vulnerability can be reasons that require you to work on improving endpoint security. You will observe that a lot of security threat is outside the corporate firewall, which makes it easier to target the security within the devices.

Companies are looking to invest in risk management as well as information security for the betterment of the security structure and to enhance the overall security system and establishment of the company.

In fact, if a report from Gartner is to be believed, then companies will be seen investing close to $175.5Bn in endpoint security by the year 2023. This will include infrastructure protection as well as data security and cloud security.

Another report from Capgemini suggests that enterprises will need to rely on AI and Machine Language as well as other current technologies to improve security and fight against cyber attacks.

Let’s take a look at how Machine Learning and Artificial Intelligence can improve endpoint security and enhance the infrastructure.

1. Identify the Risk Scores

Artificial Intelligence can be used to sweep through the past data and records to determine the risk score and tackle the security threats accordingly.

·         The machine learning technology will take into account behavioral patterns, geolocation, time of login as well as other factors that determine the risk involved with the endpoint device.

·         Accordingly, using an algorithm determined to understand the risk involved, it will deduce the risk score within milliseconds, and help reduce the fraud that can possibly occur.

·         It will understand when there is a possibility of the breach or unidentified access to your login. Accordingly, your team will be able to secure the entire network.

·        The machine learning algorithm depends on historical as well as current data, which is then used for predictive analysis. The algorithm is defined using anomalies and relationships between the factors involved.

2. Enhancing Mobile Security

The biggest threat that is posed to endpoint security comes in the form of mobile devices. It is quite a challenge to ensure that mobile devices are secure and complete access driven. However, with proper measures and correct application of Artificial Intelligence and Machine Learning to your mobile devices, you can achieve this as well. Machine Learning can help you with alternative approaches to login credentials and password driven protection. In fact, you can even secure your device against criminals, by knowing if a criminal is seeking access. Unified endpoint management is one of the best ways for endpoint security. Machine Learning will be enabled to know when a particular system is being used to access the application or document. If there is an unknown activity, this will be immediately mentioned by the ML-driven endpoint security. The machine is trained for optimal detection accuracy and based on the learning a model is created, which will be used to thwart access threats through mobile.

3. Identifying & Thwarting Phishing Attacks

A lot of the endpoint security is threatened by the various phishing attacks, which seem harmless but cause a lot of harm. In fact, they tend to cause alarming reactions by entering your bank accounts and stealing all your private information.

How does this happen?

The cybercriminals attack using emails that assure a particular scheme or event and take the user to the malware-infected site. This is quite a deal as it immediately attacks the security system of the devices connected with the malware site, and threatens to pull down the security.

Artificial Intelligence and Machine Learning can help prevent such malware attacks and ensure quicker and better reactions. For instance, AI and ML will firstly identify the issues in the emails, and detect whether they are genuine or should be considered as spam. How does this work?

·         The technology studies the metadata as well as content within the emails

·         They also study the context of the email and match it with malicious email content, which has been fed into their systems

Accordingly, ML will analyze and return the results. It will ensure that the user does not open the file in case it is malicious, and will deny access immediately. The technology will also study patterns from the past and will also connect with the content as well as the subject line and other details of the email. The idea is to understand whether the email is meant for phishing purposes or not.

4. Supervised Use of Applications

Not all applications that you install are genuine and free of vulnerabilities. As a result, endpoint security becomes crucial at this point. However, it is not possible for you to achieve it solely using your gut feeling. You need technology that can help you know if the application is a virus or suitable for your system. Machine Learning helps redefine endpoint security by learning more about app security and malware attacks through applications. It will contain apps that they believe to be unsecure for use into dynamic containers, thus keeping them away from the actual center. There are certain rules that mark an application unsafe for your system and increase the need for them to be put into a block. You will need to identify these rules and enter them into the Machine Learning algorithm, before getting started.

5. Predict & Respond to Anomalous Behaviour

Security Information and Event Management in sync with AI & ML algorithms can help automate the platform and continuously check the system to observe any anomaly in the activity. It will also help the IT environment be more secure and in sync with the requirements.

What happens in this case? The AI and ML will be used for continuous study of the endpoint data thus enabling new insights into asset management as well as endpoint security. With the collaboration of AI and ML with SIEM, you can constantly track endpoint security and continuously track the IT environment. It will help understanding which codepoints require complete tracking and assessment and also will help know the network that requires a new security system and establishment. It will also help incorporate new nodes with ease.

6. Increasing Routine Efficiency

This is a much-required aspect for your IT systems and for the better and smooth running of the systems. Machine Learning will help automate routine tasks. It will enhance the ability to prioritize the endpoint security within the infrastructure, and also help analyze the incidents within milliseconds.

Basically, your team won’t need to spend time analyzing issues and the cause of these concerns. ML will immediately understand why a particular system is not behaving fine and escalate the reason to the concerned department, thus reducing the delays.

Summing Up

Artificial Intelligence and Machine Learning will help improve endpoint security and make the devices and access efficient and impenetrable. The idea is to evolve with technology, automate the identification of frauds, and improve cybercrime detection. From phishing to frauds, the technologies will help identify and thwart the threats that can undermine the efforts put in by your IT team.

Refrence from : https://www.techiexpert.com/6-ways-ai-and-ml-together-transforming-endpoint-security-in-2020/


Original post: https://www.avagocompany.com/post/6-ways-ai-and-ml-together-transforming-endpoint-security-in-2020

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