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The positive impact of hyper-automation on cyber and data security.

Hyper-automation depends on AI and ML to automate tasks to be performed by humans. He points to a much-needed focus on the importance of automation as there is a lack of cybersecurity skills. Cyber attacks are becoming more persistent and increasingly evolving. Cybercriminals are even beginning to use AI to carry out advanced social engineering attacks.

In today's digital world, hyper-automation has shifted from a choice to a state of survival. It has become a new way of working, changing businesses. Hyper-automation is a windfall that will continue to positively impact cybersecurity for every business.

Organizations use numerous technologies that are often isolated tools and are not integrated. Hyper-automation aims to reduce this “organizational debt” to improve value and brand. In the context of cybersecurity, a patch piece not only puts the environment at risk, but also affects the cyber defender's ability to secure the environment and respond to threats at machine speed. 62% of companies don't know where their most sensitive data is, something that leads to cyber threats.

Non-traditional sensor telemetry, multiple feeds, and threat intelligence must be superimposed on Cyber COP (Cyber Common Operating Picture) to serve AI-driven predictability modeling for next-generation systems and actionable results. This is a potential future for how hyper-automation can prevent cybersecurity

Loosely integrated security systems initiate gaps, and hyper-automation attempts to solve this on a much larger scale. Going forward, it is possible that hyper-automation, along with AI and ML, will meet the requirements of next-generation security solutions.

Cybersecurity solutions can be greatly improved by sharing information about potential threats. For example, most cybersecurity platforms can identify and respond to events that match a predefined threshold condition.

One use case of threshold warning is to prevent the spread of ransomware. For example, if X numbers of files are encrypted within a given time frame, a custom script can be implemented that can stop a particular process, disable the user account, configure firewall settings, or shut down the affected server. That's great, but it doesn't stop the attack from starting. The hyper-automation model can be used to conduct forensic analysis of the event by examining the events that occurred before the event. The information collected can be made available to other organizations using the same system and can compare the most common structures with the natural selection process and choose the most likely cause of the event. Now, the custom script can be run based on the most likely events that occurred before the previous attack, thus potentially stopping the launch of the attack.

However, in order for the system to really learn, it is important to understand how it works, which means ensuring that the attack occurs in a controlled environment such as a sandbox. This is just one example of how hyper-automation can be used to prevent a ransomware attack; however, the same process can also be used to identify a much wider range of attack vectors.

We are still learning AI and ML, and therefore there is time for widespread adoption of hyper-automated systems. Hyper-automation is an inevitable trend, as it will be the only way to keep up with the imminent threat scenario and make up for the lack of IT security experts.

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