AI Applications

As per a survey, it takes an average of 196 days for a company to detect a data breach. The average cost of recovering from a common data breach, according to Norton, is $3.86 million. Artificial Intelligence (AI) comes into play at this point. AI delivers insights that assist businesses in identifying risks. These insights can aid in reducing reaction times and ensuring that businesses follow security best practices.

Impact Of AI On Cybersecurity

While artificial intelligence might help with security, it can also allow thieves to gain access to systems without the need for human interaction. The positive news about AI’s influence on cybersecurity is explained in the list below.

  • Management Of Vulnerabilities

Organizations are having difficulty managing and prioritizing the enormous number of new vulnerabilities that they encounter regularly. Vulnerability management approaches used in the past only responded to occurrences after hackers had already exploited the flaw.

Vulnerability databases’ vulnerability management skills can be improved using AI and machine learning approaches. Furthermore, when AI is used to power tools like user and event behavior analytics (UEBA), it may study user activity on servers and endpoints and discover abnormalities that could signal an unknown assault. This can help businesses defend themselves even before vulnerabilities are publicly disclosed and patched.

  • Hunting For Threats

Signatures or attack indicators are used by traditional security technologies to identify attacks. This method can quickly identify dangers that have already been identified. On the other hand, signature-based techniques are unable to detect dangers that have yet to be uncovered. They detect only around 90% of threats.

Traditional detection rates can be increased by up to 95% using AI. The issue is that you could receive a lot of false positives. A mix of AI and conventional techniques would be the best solution. This combination of traditional and new methods can boost detection rates by up to 100%, reducing false positives.

By using behavior analysis, AI may also improve danger hunting. For example, you may create profiles for every application in your organization’s network by evaluating data from endpoints.

  • Securing The Network

Traditional network security approaches concentrate on two key areas: establishing security policies and comprehending the network environment. Consider the following factors:

Rules—security policies can aid in the differentiation of genuine and malicious network connections. A zero-trust approach can also be imposed through policies. It can be difficult, however, to create and maintain policies for a large number of networks.

Environment—the majority of businesses lack specific naming conventions for apps and workloads. As a result, security teams must devote a significant amount of time to establishing which workloads belong to which application.

AI Applications In Cybersecurity: Real-Life Examples

Machine learning is capable of scanning vast volumes of data fast and analyzing it with statistics. It’s no surprise that modern businesses create massive volumes of data. Thus technology is such a helpful tool.

  • Checking For Security

Immigration officials and customs officers can discover persons who are lying about their intentions through security screening. The screening procedure, on the other hand, is prone to errors. Furthermore, human-based screening might result in mistakes since individuals become weary and easily distracted.

  • Crime Prevention And Security

The New York Police Department has been using the Computer Statistics (CompStat) AI system since 1995. CompStat is an early kind of AI that incorporates organizational management and philosophy but relies on various software tools. The technology was the first to be utilized for “predictive policing,” Since then, numerous police departments around the United States have employed CompStat to investigate crimes.

  • Examine Endpoints On Mobile Devices

Google is analyzing mobile endpoint risks using AI. This study may be used by businesses to safeguard the rising number of personal mobile devices.

Zimperium and MobileIron have established a partnership to assist businesses in embracing artificial intelligence-based mobile anti-malware solutions. Combining Zimperium’s AI-based threat detection with MobileIron’s compliance and security engine may address network, device, and application threats.

  • Threat Detection Enabled By AI

ED&F Man Holdings, a commodities dealer, had a security breach a few years back. The company’s cybersecurity protocols and technologies needed to be improved, according to an independent review.

Vectra’s AI-based threat detection and response technology, Cognito, was chosen by the firm. Cognito gathers and saves network metadata, then enhances it with security intelligence. It detects and prioritizes threats in real-time using this metadata, as well as machine learning algorithms.


As more technology is interwoven into daily life, the influence of AI on our lives will continue to rise. Some experts feel AI has a detrimental impact on technology, while others believe AI can significantly enhance human lives. The significant advantages of cybersecurity include faster threat analysis and mitigation. Hackers’ capacity to launch more sophisticated cyber and technology-based attacks is a source of concern.

So, if you want to implement machine learning algorithms in your company, contact the ONPASSIVE team.