Cyberattacks are becoming more and more costly, and this growth is exponential. More and more devices are being connected to the internet as a result of technological advancements in enterprises.
Consequently, combating these attacks manually is getting harder. When it comes to cybersecurity, artificial intelligence can help.
In essence, cybersecurity is all about defending your business against online attacks that could lead to identity theft, business data theft, intellectual property theft, financial theft, fraudulent wire transfers, reputational damage, and non-compliance issues.
If such attacks are effective, your company could suffer significant losses and customer relationships could be harmed.
With this in mind, it’s essential that you take action to help protect your company’s data from attackers as much as you can. In this article, we’ll go through what AI can do for your business’ cybersecurity and some real-life examples of where AI cybersecurity is being used today.
How Is AI Used In Cybersecurity?
The brain of AI is machine learning (ML), a kind of algorithm that enables computers to examine data, learn, and make decisions in a manner that is comparable to human behavior.
In cybersecurity, machine learning algorithms can automatically find and evaluate security incidents. Some can even react to threats automatically.
Although there are many machine learning algorithms, the majority of them do one of the following:
Classification
These algorithms attempt to apply what they have learned to new, unobserved data by drawing on their prior observations. The process of classification entails labeling artifacts with one of the multiple categories.
Clustering
This finds connections between datasets and groups them according to shared characteristics. Clustering operates directly on fresh data without taking precedent into account.
Regression
This finds correlations among several datasets and determines how they relate to one another. Regression can be used to forecast system calls made by operating systems, and anomalies can be found by comparing the prediction with the actual call.
How Can AI Improve Cybersecurity?
While artificial intelligence might help with security, the same technology can also provide hackers unrestricted access to networks. The ways in which AI can enhance cybersecurity are listed below.
Network Security
The two major focuses of conventional network security strategies are developing security policies and comprehending the network environment.
You can discern between safe and dangerous network connections with the aid of security policies. Zero-trust models can also be imposed by policies, but it can be difficult to develop and maintain policies for numerous networks.
For apps and workloads, the majority of businesses lack explicit naming conventions. Security teams must therefore spend a lot of time figuring out which group of workloads are associated with a certain application.
By identifying patterns in network traffic and making recommendations for security policies and functional workload grouping, AI can improve network security.
Vulnerability Management
Traditional vulnerability management strategies don’t react to issues until hackers have already taken use of the vulnerability. Companies are having trouble organizing and prioritizing the numerous new vulnerabilities they discover every day.
The vulnerability management capabilities of vulnerability databases can be enhanced by AI and machine learning techniques.
Furthermore, AI-powered solutions like user and event behavior analytics (UEBA) can examine user behavior on servers and endpoints and then spot anomalies that might be a sign of an unidentified attack.
Even before vulnerabilities are formally identified and addressed, this can aid in protecting companies.
Data Centers
Critical data center activities like power use, backup power, internal temperatures, bandwidth utilization, and cooling filters can all be monitored and optimized by AI. AI offers insights into what aspects of data center infrastructure might increase security and efficiency.
AI can trigger alarms that inform you when you need to attend to hardware faults, which can help you save money on maintenance. You can repair your equipment before it sustains more damage thanks to AI-based notifications.
Identifying Threats
Threats are detected by traditional security techniques using attack indicators or signatures. This method makes it simple to find dangers that have already been found.
However, threats that have not yet been found cannot be detected by signature-based methods. In actuality, they can only detect roughly 90% of threats.
The detection rate of traditional methods can be increased by AI by up to 95%. The issue is that you can receive several false positives. The best solution would be a blend of AI and conventional techniques. This fusion of the traditional and innovative can reduce false positives by up to 100% while increasing detection rates.
Real-Life Examples Of AI Applications In Cybersecurity
Let’s look at some current examples of real-world applications of AI cybersecurity now that we have a basic understanding of what it involves.
Analyze Mobile Endpoints
To assess threats to mobile endpoints, Google is using AI. This analysis can be used by businesses to safeguard the increasing number of personal mobile devices.
A partnership between Zimperium and MobileIron was announced with the goal of assisting businesses in implementing artificial intelligence-based mobile anti-malware solutions.
Threats to the network, devices, and applications can be addressed through the combination of MobileIron’s compliance and security engine and Zimperium’s AI-based threat detection.
Security And Crime Prevention
The New York Police Department has been using the Computer Statistics (CompStat) AI system since 1995. CompStat is a rudimentary type of AI that makes use of various software tools but also incorporates organizational management and philosophy.
The system was the first “predictive policing” tool, and since then, many police departments all throughout the United States have employed CompStat to look into crimes.
Military Reconnaissance
Artificial intelligence has enormous military potential. Since that military technology is filled with cameras, sensors, communication networks, and data that may be analyzed by artificial intelligence, there is a natural convergence between the two fields.
For military personnel, a US startup called Shield AI has created an AI-based monitoring system. Hivemind, a machine learning application that enables unmanned vehicles to gradually learn about their surroundings, is the technology’s driving force.
This enables military teams in the field to investigate potentially dangerous areas, like building interiors, tunnels, and caves, with a significantly lower risk to human personnel.
Threat Screening For Events
A technology powered by AI called Evolv Technology offers extensive event threat screening. The device analyzes videos of incoming visitors in real time using facial recognition software and artificial intelligence to identify whether they are authorized individuals—such as regular visitors, VIPs, staff, and other people who should be allowed admission.
A visitor’s profile will be provided to security personnel if it is determined that they are a non-permissible person of interest so that a human expert can review and confirm the information.
It is ideal to use this technology in places like airports, athletic events, and schools because it is not intended to totally replace the human component of danger assessments.
Final Thoughts
It is clear that AI applications in cybersecurity have huge advantages and they are being used more and more by businesses and government organizations.
Today, AI cybersecurity is a huge part of organizational practices and protection, and it will only become more advanced in the future.
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