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What are the future trends of SIEM?
The SIEM landscape has undergone some evolution since the early days of inception. There are several trends that have contributed massively to this including:

1. Advanced Analytics
SIEM software today can support big data and provide credible risk assessments of not only infrastructure but also personnel resources. This includes analyzing the security threats posed by employees and computers alike.

2. Threat intelligence platforms
Modern SIEM software utilizes threat intelligence platforms to detect threats to an organization’s technology infrastructure. Some emerging threats can be found from scenarios formulated by theories from these platforms.

3. Forensics
SIEM is now utilizing its forensics capabilities to piece together events after the fact. This means that after a threat has been neutralized or a security breach has occurred the SIEM system is able to follow the data collected and find out exactly what happened and how it can be prevented in the future. The addition of forensics to SIEM makes this system a one stop shop handling monitoring, analysis and rectification of occurring security issues.

For companies this means using SIEM is more cost effective as they can train a limited number of staff to use one all-inclusive tool.

How is SIEM going to change in 2020?
The future and relevance of SIEM is going to be dependent on the ability of software to adapt. These needs include integration with new technologies and increasing flexibility.

As a result, in 2020 we will most likely see

1. Stronger cloud management and monitoring capabilities
More companies are choosing cloud monitoring and management of their data. This is a convenient way for them to deal with all the data they collect from their clients. However, the cloud is not always secure so IT professionals strive to strengthen their own cloud monitoring and management systems internally to prevent security threats or breaches that may not be detected by their cloud service provider.

Companies no longer accept cloud services that don’t offer in-depth analysis. This is because the more information they have the better they are able to protect their company infrastructure and data. This expectation is more likely to grow in 2020 as companies using the cloud look for similar protection capabilities like those enjoyed by companies with on-premise cloud.

2. Better orchestration
SIEM currently offers basic workflow automation which has so far been quite efficient. But as companies experience growth, additional capabilities are required. In 2020, we will see more commercialization of machine learning and artificial intelligence which will call for faster SIEM orchestration so that different departments within an organization have the same level of protection. Security protocols and their executions will be faster and more efficient and effective.

3. Better MDRs-SIEM collaboration
MDRs stand for managed detection and response providers. These are outsourced professionals who take care of detecting, ascertaining and responding to threats.
Most time MDRs and SIEM are pitted against each other. They can, however, work together where the organization’s IT team implements SIEM in-house and the outsourced service provider also implements the MDR. In 2020, threats of hacking and unauthorized access are likely to increase as technological advancements also increase. Having a two prong approach to detection and analysis of security threats is not only a good idea but also a prudent way to find a lasting solution.

How do machine learning, artificial intelligence and big data affect SIEM?
Older versions of SIEM aren’t capable of handling the volume of data coming from social media applications and the web because it is unstructured and fast moving. And because of its unstructured format, such big data may contain threats that the system isn’t able to quickly detect.
But using big data technology helps analyze huge volumes of data. So big data analytics will provide SIEM systems with security correlations in the pools of data analyzed helping SIEM software detect any threats.

When it comes to machine learning it can be used to in two forms: supervised and unsupervised. The supervised versions of machine learning will sift through structured data that has specific algorithms and rules. The unsupervised applications of machine learning, which are more popularly used in SIEM, go through unstructured data generated from multiple sources finding threats embedded within. The advantage of using machine learning is that these applications can scan data faster than people and in real time meaning one can discover and thwart threats faster. Cyber security experts know that a security breach can go undetected working nefariously behind the scenes.
Artificial intelligence helps in SIEM because it analyzes huge volumes of data in a shorter amount of time and also finds any hidden relationships in data. It’s also a self-curing system which corrects its own faults without human intervention making it more effective each time. Using AI enables IT teams to predict future threats and mitigate them.

Attacking next generation cyber threats needs next generation solutions. This translates to using emerging trends, technologies and tools to become more efficient at securing data. From AI to machine learning the SIEM landscape is showing a versatility that IT experts once only dreamed of.