Table of Contents
Introduction : Machine Learning in Cloud Security
Cloud security is a multi-billion dollar industry. Many many media, Big and very big Companies are depending on Cloud Architecture for their data. Big Companies from Amazon, Google to small and medium level companies keep their data on Cloud. In this article, we are going to explore what is the Future Scope of Machine Learning in Cloud Security.
These companies are completely dependent on the maintenance and security of their data and based on the size, Companies are using various security tools, numbers ranging from 25-120. But overall there is a problem integrating those security tools and as a result, the security is being compromised.
Why Machine Learning is Important in Cloud Security
The concept of AI/ ML on cloud is becoming popular day-by-day because of its control over enormous data processing ( BIG DATA etc). However Big tech companies are facing a lot of issues for processing data on the cloud using the native techniques. So transferring even a very small amount of data also it has become a tedious job.
Sometimes the environment is a concern and also how these tools communicate with each other and based on what protocols.
Cloud architecture guarantees the scalability and elasticity of the Data. But on the other hand, it comes with high complexity while uploading and processing the data. Similarly to guarantee the security is equally hard in cloud architecture. Many companies can visualize implementing Machine Learning
Threat Detection
Companies have to have a clear idea of what are the hardware and software taking part in building the architecture in the cloud and how they are communicating with each other.
It’s possible to miss out on some probable loose-ends which can cause threats using the native process.
Handling the possibility of the threat in a single hardware or software is less complicated than its presence when the whole system has interconnected networks and damage it can cause to the system in the cloud is severe beyond the scope of this article.
Some companies are totally relying on Machine learning to solve these problems. Using machine learning algorithms it can foretell the possibility of an attack based on a study of the previous patterns. So before it even happens, precautionary measures can be taken.
Who is Communicating ?
Machine learning can be useful to identify the presence of communicators who are sending signals and sometimes this can be done by a real human being. Sometimes it can be a bot.
In some cases, somebody can use a software tool to create communication. Machine-learning algorithms are able to identify the property of the senders/intruders from the pattern of the signals they send.
From the patterns generated, ML can create actionable insights which can be very helpful to protect the organization from getting attacked by some future threat.
How smart are the organisations ?
In any organization, before you want to change any security system, your approach has to be on par with the company policies, and also your proposal must be approved by the board of directors. So no one can just implement any policy without permission from the key people of the organization.
Many organisations don’t have up-to-date technology infrastructure that can accommodate and enable machine learning for their cloud security.
For some organizations, the hackers/attackers are already 2 steps ahead. Such organizations need to consider AI/ Ml seriously. However, this concept is still early-stage but it will be the future.
For some more clarity check this Article I found on the internet.