The Benefits of AI in Combating Cybersecurity Threats

Artificial Intelligence (AI) techniques can be used to learn how to remove noise or unwanted data and to enable security experts to understand the cyber environment to detect abnormal activity. AI can also benefit cybersecurity with automated techniques to generate whenever cyber threats are detected, offloading ongoing work from SOC Operators / Analysts. Artificial Intelligence can analyze massive amounts of data and allow the development of existing systems and software in an appropriate way to reduce cyber-attacks. While artificial intelligence can improve security, the same technology can give cyber criminals access to systems with no human intervention. AI can also have the power to automatically adapt to changes in threats in real-time and mark problems as they occur. Finally, with all of the time savings AI brings to the table, you can be sure those would be reflected in your ROI.

The list below explains the good news about AI’s impact on cybersecurity.

Vulnerability Management

Organizations are struggling to manage and prioritize the large number of new vulnerabilities they come upon daily. Conventional vulnerability management techniques respond to incidents only after hackers have already exploited the vulnerability.

AI and machine learning techniques can improve the vulnerability management capabilities of vulnerability databases. Additionally, tools like user and event behavior analytics (UEBA), when powered by AI, can analyze user behavior on servers and endpoints, and then detect anomalies that might indicate an unknown attack. This can help protect organizations even before vulnerabilities are officially reported and patched.

Threat Hunting

Conventional security tools use signatures or attack indicators to identify threats. This technique can easily identify previously discovered threats. However, signature-based tools cannot detect threats that have not been discovered yet. In fact, they can identify only about 90 percent of threats.

AI can increase the detection rate of traditional techniques up to 95 percent. The problem is that you can get multiple false positives. The ideal option would be a combination of AI and traditional methods. This merger between the conventional and innovative can increase detection rates by up to 100 percent, thus minimizing false positives.

AI can also improve threat hunting by integrating behavior analysis. For instance, you can develop profiles of every application inside your organization’s network by analyzing data from endpoints.

Network Security

Conventional network security techniques focus on two main aspects: creating security policies and understanding the network environment. Here are some aspects to consider:

  • Policies – Security policies can help distinguish between legitimate and malicious network connections. Policies can also enforce a zero-trust model. However, creating and maintaining policies for many networks can be challenging.
  • Environment– most organizations don’t have precise naming conventions for applications and workloads. As a result, security teams must spend a lot of time determining what set of workloads belong to a given application.AI can enhance network security by learning the patterns of network traffic and recommending both security policies and functional workload grouping.

Data Centers

AI can monitor and optimize critical data center processes like power consumption, backup power, internal temperatures, bandwidth usage, and cooling filters. AI provides insights into what values can improve the security and effectiveness of data center infrastructure.

You can use AI to reduce maintenance costs. AI can prompt alerts that let you know when you have to attend to hardware failures. AI-based alerts enable you to fix your equipment before further damage occurs.

Advantages of AI in Cybersecurity

AI presents many advantages and applications in a variety of areas, cybersecurity being one of them. With fast-evolving cyberattacks and rapid multiplication of devices happening today, AI and machine learning can help to keep abreast with cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques. Here are a few of the advantages of using AI in cybersecurity:

1. Detecting New Threats

AI can be used to spot cyber threats and possibly malicious activities. Traditional software systems simply cannot keep pace with the sheer number of new malware created every week, so this is an area AI can really help with.

By using sophisticated algorithms, AI systems are being trained to detect malware, run pattern recognition, and detect even the minutest behaviors of malware or ransomware attacks before it enters the system.

AI allows for superior predictive intelligence with natural language processing which curates data on its own by scraping through articles, news, and studies on cyber threats.

This can give intelligence of new anomalies, cyberattacks, and prevention strategies. After all, cybercriminals follow trends too so what is popular with them changes constantly.

AI-based cybersecurity systems can provide the latest knowledge of global as well as industry-specific dangers to better formulate vital prioritization decisions based not merely on what could be used to attack your systems but based on what is most likely to be used to attack your systems.

2. Battling Bots

Bots make up a huge chunk of internet traffic today, and they can be dangerous. From account takeovers with stolen credentials to bogus account creation and data fraud, bots can be a real menace.

You cannot tackle automated threats with manual responses alone. AI and machine learning help build a thorough understanding of website traffic and distinguish between good bots (like search engine crawlers), bad bots, and humans.

AI enables us to analyze a vast amount of data and allows cybersecurity teams to adapt their strategy to a continually altering landscape.

3. Breach Risk Prediction

AI systems help determine the IT asset inventory which is an accurate and detailed record of all devices, users, and applications with different levels of access to various systems.

Now, considering the asset inventory and threat exposure (as discussed above), AI-based systems can predict how and where you are most likely to be compromised so that you can plan and allocate resources towards areas of most vulnerabilities.

Prescriptive insights from AI-based analysis enable you to configure and improve controls and processes to reinforce your cyber resilience.

4. Better Endpoint Protection

The number of devices used for working remotely is fast increasing, and AI has a crucial role to play in securing all those endpoints.

Sure, antivirus solutions and VPNs can help against remote malware and ransomware attacks, but they often work based on signatures. This means that to stay protected against the latest threats, it becomes necessary to keep up with signature definitions.

This can be a concern if virus definitions lag, either because of a failure to update the antivirus solution or a lack of awareness from the software vendor. So, if a new type of malware attack occurs, signature protection may not be able to protect against it.


Our Cyber Protection is enabled by artificial intelligence to cover three main areas:

  1. Preemptive Protection for Asset Management, Vulnerability Assessment, Advanced Malware, Insider Threats, and more.
  2. Detection & Response for Intrusion Detection (IDS), UEBA & Anomaly Detection, HoneyPot Deception, Lateral Movement Detection, to name a few.
  3. Ongoing Monitoring for File Integrity, Communication for suspicious /malicious/blacklisted domains with SIEM & Orchestration capabilities.

Cybowall’s Autonomous Machine Learning Attack Hunter

Cybowall’s secret sauce is our AI Attack Hunter – an autonomous Machine Learning and multi-step attack hunting engine. It works is by collecting IOCs and alerts across multiple security layers – endpoint, server, cloud, and network, and cluster them into a superset of super-classes of IOCs – GIOC.

That enables the Attack Hunter to:

  1. Filter and correlate massive amounts of alerts into a few designated cyber-attacks.
  2. Detect threats accurately and faster.
  3. Reduce dramatically the number of false positives while increasing the true positive.
  4. Detect and learn new attacks by using consistent datasets that are better suited for machine learning AI algorithms.

To learn more and to book a demo click here.