Here is how hyper automation impacts cybersecurity

Hyper-automation relies on AI and machine learning to automate processes that would otherwise be done by people. Because of a scarcity of cyber-security skills, it emphasises the significance of automation. Cyber threats are getting increasingly persistent and sophisticated. Cybercriminals are already beginning to use artificial intelligence (AI) to carry out sophisticated social engineering attacks.

In today's digital world, hyper-automation has gone from an option to a requirement. It has developed into a major way of working that has had an impact on companies. Hyper-automation is a godsend to cybersecurity that will help all businesses in the future.

Is hyper-automation a viable cybersecurity option for the future?

While it may seem to be just another business buzzword, it emphasizes the importance of automation as the cyber-security skills shortage intensifies.

According to an ESG worldwide survey, 53% of respondents said their company has an issue with cyber security skills. To make matters worse, cyber-attacks are becoming more widespread and sophisticated. In truth, cybercriminals are beginning to use artificial intelligence in sophisticated social engineering tactics.

In today's digital environment, hyper-automation has progressed from an option to a necessity. It has developed into a major way of working that has had an impact on companies. Hyper-automation is a benefit to security that will help all businesses in the future.

The company usually uses a variety of stand-alone solutions that are not interconnected. The goal of hyper-automation is to decrease organisational debt in order to boost value and brand. In the domain of cybersecurity, a patchwork not only puts the ecosystem in jeopardy, but it also endangers the ability of cyber guards to protect the environment and respond to incidents at lightning pace. 62 percent of organisations have no idea where their most critical data is kept, leaving them vulnerable to cyber-attacks.

How Can Hyper-Automation Be Used to Protect Data?

Sharing information on potential attacks could considerably improve data protection systems. Most data protection platforms, for example, are capable of detecting and responding to events that meet a pre-defined threshold condition.

The prevention of the transmission of ransomware is one application of "threshold alerting." If X files are secured in a certain amount of time, for example, a customized script can be run to stop a particular method, deactivate a user account, alter firewall settings, or close down the afflicted server. This is excellent, but it does not prevent the attack from being launched.

By investigating the events that occurred prior to the occurrence, AI can be utilised to do a forensic investigation of the incident. The data acquired might be shared with other businesses that use the same platform, and it could analyse the most prevalent patterns to determine the most likely cause of the crash through a process of natural selection. Now, the customized script might be run based on the most plausible events that occurred before the previous strike, possibly preventing it from happening again.

Nevertheless, in order for the program to genuinely learn, we'd have to know if it worked, which would necessitate allowing the attack to take performed in a safe environment like a sandbox. This is only one example of how automated automation can be used to avoid a Ransomware assault; the same approach could be applied to a far broader range of threat vectors.

The truth is that we are still discovering what AI and machine learning can achieve when it comes to securing our sensitive information, so hyper-automated solutions may take some time to catch on.

Not just that, but AI and machine learning require a significant number of resources, such as memory and computing power. Let's not forget that AI's effectiveness is dependent on huge, accurate collections of data, which will take time to assemble or obtain through other ways.

In any case, hyper automation is a foregone conclusion, as it will soon be the only option to keep up with the fast-changing threat landscape and compensate for the shortage of IT security personnel.

Hyper Automation Tools in Action

Further automation, artificial intelligence, and machine learning can help IT operate a more secure remote network in the following methods:

Improved Threat Intelligence

The ability to know what programmes, devices, and software are in use throughout your whole environment is the first line of defence. Hyper Automation technologies can detect what's going on in the environment, identify vulnerabilities, and track any changes in consumption or device behaviour. Continuous sensing, discovery, and detection of security concerns allows IT to prioritise significant issues for remedy, further protecting the network from attackers.

Improved Visibility at the Edges

IT can use natural language processing (NLP) to query all networking devices and acquire real-time intelligence throughout the organisation in seconds. It uses sensor-based structure to give instant operational knowledge, real-time inventory, and security settings throughout the edge.

Risky Prioritization

Patching updates are one of the most important methods for keeping the network safe. IT can discover what is being actively targeted using machine learning and predictive analytics, allowing risk response to be prioritised depending on threat priorities. Patch dependability statistics may be supplied automatically using actionable intelligence gathered from numerous public and crowdsourcing sentiment data sources.

Proactive Remediation

Automated device and app use control can help uncover vulnerabilities, prioritise risks, and address the ones that are the most dangerous right away. Automated remediation allows IT operations to shift from a reactive reaction paradigm to a proactive, "self-secure" model, correcting vulnerabilities before threat actors can exploit them.