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Using behavioral AI as a cybersecurity tool

5 min read

Cybersecurity has seen the rise of complex and dangerous threats, such as phishing and ransomware. Nevertheless, behavioral AI has emerged as a powerful ally in the fight against these threats, using behavioral analysis of digital entities to identify and counter risks. Nevertheless, the synergy between humans and behavioral AI remains vital for effectively detecting and removing threats.

Cyber ​​threats have become more subtle, sophisticated and dangerous, but cybersecurity has a weapon in the form of artificial intelligence (AI).artificial intelligence (AI). a>

Modern cyber threats manifest as phishing, ransomware, Denial-of-Service (DoS), malware and spyware – and they are deceptive and effective.

As the name suggests, Behavioral AI analyzes the behavior of objects such as a system, files, emails or attachments to identify and flag or remove threats.

For example, Behavioral AI can identify and highlight pattern anomalies when an inactive account at a financial institution suddenly becomes hyperactive and receives multiple high-value transactions.

Such events can not only bypass standard antivirus solutions, but can also be dangerous.

However, the role of people in removing threats remains just as important. In many cases, Behavioral AI cannot succeed without the cooperation of people.

Wat is Behavioral AI?

A computer system has several entities, including the user, endpoint devices such as smartphones or laptops, cloud services, files and data, and network traffic, to name a few.

All entities can be compromised at different times, which can seriously compromise the computer system or institution.

A common scenario that many of us may have experienced is Google blocking access to a website because the system identifies unusual traffic. While Google often confuses a normal situation with an abnormality, this is an example of its AI systems in action.

The AI ​​systems analyze traffic and flag any event they think is an anomaly. This is Behavioral AI in action, where AI techniques are applied to analyze and understand the behavior of different entities in a computer system.

Behavioral AI deals with behavior modeling, anomaly detection, user and entity behavior analysis, threat and phishing detection, automated responses, and more. It is an advanced form of countering cyber threats, similar to how people recognize deviations or changes in the behavior of people they know well.

The Role of Behavioral AI in Combating Threats

Behavioral AI stands out from the conventional approach to cybersecurity in dealing with threats. While the traditional approach addresses known threats, Behavioral AI is able to address both known and unknown threats in real time.

Behavioral AI undergoes training on massive streams of cyber threat data, allowing it to continually learn about the ongoing evolution of threat types. When it identifies a threat, it immediately raises an alarm or automatically removes the threat through an automated system.

The automated threat removal and faster identification are another distinction between the traditional approach and the Behavioral AI approach.

The traditional approach involves first identifying the threat, then raising the alarm, and finally manually removing the threat, which is a time-consuming process.

The role of Behavioral AI can be summarized as follows:

  • Identifying malware: Behavioral AI identifies malware in both labeled and unlabeled data. Labeled data serves as the basis for detecting suspicious data, while Behavioral AI learns independently from unlabeled data.
  • Detecting phishing attempts: Phishing attempts are becoming increasingly sophisticated and subtle. Even emails with malicious content that appear almost identical to legitimate emails can be recognized by AI. This ability was created by the AI’s learning process regarding such content.
  • Providing network security: Given the large amounts of traffic that computer systems receive, advanced threats can disguise themselves as regular traffic. However, Behavioral AI is able to identify such threats because of the continuous learning process it has undergone.

Case study: AI in action

A Fortune 500 telecom company has introduced AI to classify encrypted data flowing through their network into application categories.

The main challenges the company faced were as follows:

  • Manual labeling of traffic data was too slow and required expensive resources.
  • Analyzing network traffic was based on a static set of rules, making the system vulnerable to suspicious traffic that did not match the rules.
  • The existing system had difficulty managing changing data distributions, such as responding to alarms or tickets for network issues.
  • The company had to use multiple tools to secure its computer system, which was expensive and difficult to manage.

After implementing AI, results improved significantly:

  • Before the AI, the system could produce an initial subset of 2,000 labeled ground-truth examples, but after the AI ​​it produced an additional 198,000 programmatically labeled examples.
  • The AI ​​model was 26.2% more efficient than its predecessor.
  • AI was 77.3% more accurate than the rules-based approach of the previous system used by the company.


AI has redefined cybersecurity management and many case studies have demonstrated its usefulness. However, AI is not a foolproof solution, at least not yet.

It comes with limitations that raise questions about its effectiveness, including:

  • AI is an evolving technology and still struggles to provide precise solutions to cyber threats. Although AI is being used to counter cyber threats, questions are being asked about its output and reliability in countering threats.
  • AI is not yet robust enough for the series of complex actions required to recover from attacks. One of the reasons for this is the lack of precision and accuracy, which makes it not reliable enough for the technicians.
  • Criminals in the cyberspace are also using AI, making threats more sophisticated and powerful.


We must remember that AI is still a developing technology.

The limitations are real and organizations are faced with the question of how much to rely on AI. Still, there is proven benefit in deploying AI as part of an arsenal of cybersecurity tactics.

The best way forward is to not get carried away by the hype, objectively assess the capabilities of AI versus traditional systems, and find a combination of the two that suits you or your organization.

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All content in this article is for informational purposes only and in no way serves as investment advice. Investing in cryptocurrencies, commodities and stocks is very risky and can lead to capital losses.