Consulting Cyber Telco

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

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.

SwissCognitiveConstant 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 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 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 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.[…]

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