Utilizing artificial intelligence (AI) to monitor

and assess online threats

It makes sense to use AI tools and services to increase cyber security, but where do you even begin?

Virtually every industry that artificial intelligence (AI) touches is undergoing a radical transformation, and cyber security is no exception.

Technology has the power to fundamentally change how businesses and security teams respond to the constantly shifting threat landscape.

Research suggests that artificial intelligence (AI) might increase productivity, but as the technology is still in its early stages, security professionals run the risk of becoming unduly reliant on it.

AI is shown to be quite helpful in monitoring and assessing the cyber dangers that an enterprise is exposed to. Additionally, teams can evaluate the threat landscape unique to their company with the aid of a variety of new and developing capabilities, including as AI-infused security information and event management (SIEM) and pattern recognition.

The most prominent risks and perils of the modern world
According to Embroker’s analysis, the top cyber security concerns for 2024 include social engineering attacks, third-party exposure, and setup problems.

Verizon figures show that 74% of all data breaches occurred last year involved the use of social engineering techniques in particular, and these assaults are unfortunately becoming more sophisticated.

Hackers are become increasingly skilled at tricking workers into divulging their credentials, whether by spoofing or traditional phishing, all the while making their attacks more difficult to detect.

Supply chains are becoming a target for cybercriminals as well; some high-profile hacks last year made news because they exposed a third party’s attack surface.

Numerous issues could potentially stem from straightforward configuration errors. These could include simple errors like ignoring system updates or patches or using weak passwords. They may also mean leaving gadgets at their factory default settings.

The dynamic surroundings for recognising risks
An AI-based regime that greatly improves danger hunting with machine learning has long since superseded the outdated rules-based threat identification method.

Palo Alto Networks claims that it’s been a practical revolution, different from signature-based strategies, heuristic-based threat detection techniques, and, more recently, anomaly-based detection systems. While each of these detection techniques was necessary, generative AI and machine learning have given rise to a new class of threat modeling.

According to EC-Council University, cyber security professionals will unavoidably want to use AI tools and services because of all the advantages these systems have to offer, such pattern detection and insight-giving, not to mention the ability to take autonomous mitigation actions.

AI is very good at pattern insights, which is why it’s been applied widely in the workplace to identify trends that humans might find difficult to identify otherwise. Artificial intelligence (AI) has the potential to identify cyber security anomalies that are so subtle that they would normally go unnoticed, highlighting them for further investigation by cyber security experts. Depending on the type of issues raised, certain systems can also provide advise on what actions to take based on patterns that have been found.

The most sophisticated AI systems are even capable of taking mitigation actions on their own; humans need simply intervene to approve choices.

How AI may be used to identify risks more quickly than ever

The cyber security sector is responding forcefully to the issue of cybercriminals leveraging AI to exploit new forms of attack, relying on AI solutions to better defend enterprises against an expanding wave of threats. Artificial intelligence (AI) solutions have the capability to analyze threats originating from many systems, while also considering threat intelligence, the current state of networks, and data that is stored within the company.

According to Joseph Steinberg, a cyber security specialist and member of CompTIA’s Cybersecurity Advisory Council, in the end, these systems are able to assess the dangers, make forecasts, and then prioritize what has to be addressed and when. “That can dramatically improve the security of an organization,” Steinberg continues, “because remember, if one attack gets through because somebody prioritized [incorrectly]… that can lead to a catastrophe.”

Constructing a threat detection model is a difficult task. It frequently consists of multiple layers that call for knowledge in other domains, such as the organization’s unique threat landscape and machine learning system implementation.

Determining the aspects of the data the AI will focus on after first defining the issue and gathering and preparing the data is one streamlined method of creating an analytical model to identify hazards.

Regarding the data to use, EC-Council University advises compiling data on the IT asset inventory by utilizing the most recent information on dangers that are particular to the industry and the world at large.

Finally, frequent updates and implementation are necessary to improve the AI model’s performance and usefulness.

Using AI tools as soon as possible may seem obvious, especially considering the scarcity of workers in the cyber security industry.

Adopting AI tools and systems can, in fact, assist in risk analysis and danger detection significantly more quickly and efficiently than humans alone could. However, it’s also essential to deploy these systems with strong configuration and within the parameters of well-thought-out policies. When attempting to assess new risks, the last thing an organization wants is to unintentionally increase the attack surface.