Typically, when a developer writes a program, they describe the logic they want the application to implement. However, this has the limitation that developers can only write applications, classifiers, etc., for things that they understand and know how to implement.

Artificial intelligence (AI) systems learn from a collection of training data rather than being told what to do. When presented with a training dataset, the AI system trains a model that enables it to ingest data and provide the desired response, such as whether a file is malware.

AI has broad applicability to many fields, and cybersecurity is one of the most promising. Cyberattacks are a major concern for many organizations, but many struggle to manage them. By automating cybersecurity data analysis and decision-making, AI can rapidly enhance an organization’s ability to identify and respond to them.

How AI is Used in Cybersecurity

AI is extremely effective at analyzing large volumes of data and making decisions based on them. These capabilities can be applied to cybersecurity in various ways, including the following:

  • Threat Detection and Prevention: AI can process security logs, threat intelligence, and other security data. Based on this information, the system can alert human operators to potential threats or independently take action to contain or remediate a detected cyberattack.
  • Malware Detection and Prevention: Zero-day and novel malware variants are difficult to detect because no known signatures exist. AI could monitor the behavior of programs on a system for suspicious or anomalous actions, such as the large-scale file encryption performed by ransomware.
  • User Behavior Analysis: AI could also monitor the behavior of human users of an organization’s network, computers, and applications. Deviations from normal behavior may indicate a compromised account or a malicious insider taking action to harm the company.
  • Network Security Monitoring: AI can be used for network traffic analysis and security monitoring. AI can identify potential data breaches, ransomware infections, and other security incidents by analyzing traffic flows and packet data.
  • Fraud Detection: In addition to identifying potentially anomalous or suspicious behavior by an organization’s users, AI can also be applied to identifying potential fraud.
  • Red Team Operations: In a red team engagement, AI could automate various parts of the testing process. For example, AI could automate reconnaissance and develop attack plans, malware, scripts, or phishing emails to exploit identified vulnerabilities.
  • Blue Team Operations: As discussed above, AI has various applications in defensive cybersecurity. These are equally applicable in simulated engagements, enabling blue team members to more rapidly identify and respond to the red team’s actions.

Advantages of AI in Cybersecurity

AI has numerous use cases and can also provide significant benefits compared to human-centric approaches. Some of the advantages of AI in cybersecurity include the following:

  • Faster Threat Detection and Response: Artificial intelligence has the ability to analyze data and make decisions at machine speed. This enables faster threat detection and response than processes dependent on human users.
  • Improved Accuracy and Effectiveness: AI could process more data than a human and doesn’t get tired or bored. This can improve the accuracy and effectiveness of threat detection and response because AI can make better decisions based on more data and greater context.
  • Scalability: The capabilities of AI-based cybersecurity solutions are largely limited by their computational resources. It’s often cheaper and easier to scale by purchasing more computing power than hiring additional skilled personnel.
  • Large-Scale Data Analysis: AI is designed to process massive amounts of data and make decisions based on this information. This capability can be invaluable for cybersecurity because it provides the ability to fight the alert overload many security teams face.

Challenges of AI in Cybersecurity

AI offers significant potential benefits for cybersecurity operations. However, it also faces challenges, such as the following:

  • The complexity of Implementing AI Systems: The strength of an AI model and system depends on the volume and quality of its training data. To develop an effective AI-based cybersecurity solution from scratch, an organization needs access to a large volume of training data and significant processing power to train the model and operate the final solution.
  • Potential for False Positives and False Negatives: Like any cybersecurity solution, AI has the potential for false positive and false negative detections. If the AI system is used as the only line of defense and automatically takes action based on detected threats, it could miss attacks and inappropriately block legitimate activity.
  • Ethical Concerns: An AI model and system can have intrinsic biases based on its training set. For example, facial recognition systems commonly perform best for white males and can exhibit extremely poor detection for other demographics. This introduces ethical issues for the use of AI in cybersecurity and related fields if its decisions are potentially discriminatory.

In many cases, these challenges depend on the quality and quantity of training data used. A data trained on better data will make more accurate decisions and hopefully carry fewer ethical concerns.

Future of AI in Cybersecurity

Today, AI is in its relative infancy. While the field has existed for decades, major advances — such as the development of tools such as ChatGPT — have only occurred within the past few years. This rapid growth hints that the AI of the future may look very different from the tools in use today.

AI is likely to change the face of cybersecurity operations dramatically. The future security operations center (SOC) will likely feature partnerships between human analysts and AI assistants. This will dramatically improve security operations' speed, efficiency, and effectiveness as rote manual tasks will be automated, and humans will only be involved in making critical decisions and provide instructions to automated systems.


The emergence of usable AI in recent years has already had dramatic impacts on the cybersecurity industry. Tools like ChatGPT make writing malware or effective spear phishing emails much easier. On the other hand, AI is increasingly integrated into cybersecurity tools, improving the speed and accuracy of threat detection and response.

AI will almost certainly become a central part of the SOC of the future. However, it’s important to ensure that this AI is well-trained and to address the potential for attacks specifically targeting these AI systems.