Discover The Potential Of Artificial Intelligence & Machine Learning In Cybersecurity

source: securityinfowatch 

AI cybersecurity, supported by machine learning, will soon be a powerful tool. As with other industries, human interaction has always been essential and indispensable in the field of security.  While cyber security currently relies heavily on human input, we are gradually witnessing that technology is becoming better than the human factor in specific tasks. 

Every technical improvement brings us a little closer to completing human roles more effectively; Among these developments, some  areas of research are at the core of it all: 

Artificial Intelligence 

Is designed to give computers the full ability to respond to the human mind; This is the end-to-end system that includes many other systems, including machine learning and deep learning. 

Machine Learning 

Uses existing behaviour patterns and shapes decision-making based on past data and inferences. Human intervention is still needed for some changes; Machine learning is probably the most relevant AI cybersecurity discipline to date. 

Deep Learning 

Works similarly to machine learning by making decisions from past patterns but making adjustments on its own. 

Deep learning in cybersecurity currently falls under the realm of machine learning, so we will focus mostly on machine learning here. 

What AI and Machine Learning Can Do For Cybersecurity? 

Artificial intelligence and cyber security have been described as revolutionary and much closer than we think. However, this is only a partial fact that must be approached with conservative expectations!

The truth is that we may see relatively incremental improvements going forward. But in theory, what might seem incremental compared to a fully autonomous future is still a leap beyond what we were capable of in the past. 

As we explore the potential security implications of machine learning and AI, it is important to frame current vulnerabilities in cybersecurity; There are many processes and aspects that we have long accepted as normal that can be addressed under the umbrella of AI technologies. 

Human Error In Configuration 

Human error is an important part of cybersecurity vulnerabilities.  For example, it can be very difficult to manage proper system configuration, even with large IT teams involved in the setup.

In the context of continuous innovation, multi-layered computer security has become more widespread than ever before. Response tools can help teams find, modify, and upgrade issues that arise when network systems are replaced. 

Manual Configuration Security Assessment

Consider how newer Internet infrastructure such as cloud computing can be stacked on top of older on-premises frameworks.  On enterprise systems, the IT team will need to ensure compliance to secure these systems. Manual configuration security assessment processes make the difference because they balance endless updates with mundane day-to-day support tasks. 

And with intelligent, adaptive automation, teams can receive timely advice on newly discovered issues. They can also get advice on follow-up options, or even install system structure apically adjust settings as needed. 

Human Efficiency In Repetitive Activities 

Human efficiency is another weak point in the cyber security industry as no manual process can be replicated perfectly every time, especially in a dynamic environment like ours. 

Single setup of multiple enterprise peripherals is among the time-consuming tasks. Even after the initial setup, IT teams find themselves resetting the same devices later to correct misconfigurations or outdated settings that cannot be corrected in remote updates. 

Additionally, when employees are tasked with dealing with threats,  the scope of said threat can change rapidly. Human focus may slow down due to unexpected challenges, and a system based on artificial intelligence and machine learning can move with minimal delay. 

Exhaustion Due To Frequent Airtime-consuming Exhaustion From too many threat alerts in organizations creates another vulnerability if not handled carefully. This increases the attack surfaces as the security layers mentioned above become more elaborate and pervasive. 

Many security systems are tuned to react to several known issues with a barrage of purely reflexive alerts; As a result, these individual claims leave human teams to analyze potential decisions and take action. 

Threats And Vulnerabilities

The large influx of alerts makes this level of decision-making particularly taxing. Ultimately, decision burnout becomes a daily experience for cyber security staff! Being proactive about these identified threats and vulnerabilities is ideal, but many teams lack the time factor and staff to cover all their bases. 

Sometimes teams have to decide to face the biggest fears first and leave secondary goals behind. Using AI in cybersecurity efforts can allow IT teams to manage more of these threats efficiently and practically. Countering each of these threats can be much easier if they are grouped by automated tagging. In addition, some concerns may be able to be addressed by the machine learning algorithm itself. 

Done By Mohamed Mouafak