10 Examples of AI in Cyber Security (Latest Research)

Examples of AI in Cyber Security

Let’s look at ten examples of AI in cyber security and discover why AI is a game-changing technology for the security industry. You will see how AI can be used across multiple cyber security domains, what makes it a good solution to existing problems, and the current tools using this technology.

This comprehensive roundup of AI use cases will help you understand the power of AI and why you need to adopt it into your workflow, tools, and daily life. This revolutionary technology will empower you to become a more efficient and effective cyber security professional. 

Before diving into the ten examples, let’s explore why we need AI in cyber security. 

10 Examples of AI in Cyber Security (Latest Research)

Why Do We Need to Adapt AI Into Cyber Security?

The AI revolution is happening. The technology is here to stay, and you must get on board. 

Attackers are rapidly adopting this technology to enhance their capabilities, perform more sophisticated cyber attacks, and develop advanced malware that can evade detection and exploit the latest vulnerabilities. Defenders must keep up with this rapid adoption by incorporating AI into their tools, technologies, and processes to respond to these emerging threats. 

As this epic battle continues and more AI tools emerge, you need to stay up-to-date with the release of new AI tools and understand how to use the advances in AI to your advantage. Here are some of AI's key benefits to get you up to speed on why you need to adapt AI into your daily cyber security work.

Benefits of AI for cyber security:

Provides immediate access to a vast knowledge base that you can utilize.
Built around an intuitive interface that streamlines the enrichment of information and data searching. 
Capable of augmenting workflows and making processes more efficient.
It can act as a copilot to make executing a series of complex tasks easy. 
Capable of automating the protection of computer systems and networks.

Let’s see how some of these benefits impact various cyber domains.

1. Threat Detection and Prevention

Detection and prevention are critical components of all cyber security strategies. They are used for threat identification purposes by targeting endpoint devices, networks, and data. AI can support both.

How Can AI Be Used for Threat Detection and Prevention?

AI can analyze large quantities of data at scale and detect anomalies or suspicious behavior indicative of malicious activity. Behavior analysis is a detection technique that modern tools and human operators can perform. AI revolutionizes this detection technique with data processing and machine learning capabilities. It can adapt to emerging threats and new attack techniques that hackers constantly develop to bypass security tools and remain undetected while reducing false positives.

Why Is AI a Good Solution?

AI can detect anomalous behavior across millions of data points, immediately ingest the latest threat intelligence about emerging threats, and react to malicious activity through real-time monitoring. The power of AI when it comes to analyzing vast quantities of data and responding immediately to threats across large IT environments is something you cannot replicate with human operators.

Current Tools

Several tools currently use AI to empower and automate threat detection and prevention. Two of the most impressive are Microsoft Security Copilot and Tessian’s Complete Cloud Email Security platform.

Microsoft Security Copilot is an in-one virtual assistant that uses the power of AI to augment your workflow, detect and respond to threats, and enhance your security posture. Meanwhile, Complete Cloud Email Security is an AI-based approach to detecting and preventing phishing attacks and business email takeovers and protecting sensitive data on email.

Aside from these two, other AI tools for detection and prevention are Cylance, Cybereason, and McAfee MVISION. Going forward, more companies will likely invest resources in pairing AI with their existing detection and prevention tools.

2. Automated Incident Response

Incident response involves cleaning up after a cyber attack. A team of dedicated professionals will contain the incident, eradicate any lurking threats, and restore affected systems. This time-intensive process requires a specialist skillset and a series of coordinated procedures to ensure all systems are safe. AI can automate this, saving time and money.

How to Use AI for Automated Incident Response?

AI can perform complex task execution, augment analysts' workflow, and instantly enrich investigation information with its access to vast data stores. This makes incident response a lot easier. Analysts can streamline their investigation by knowing what indicators to look for, how certain incident response actions can be automated, and how business data can be made ready to restore the business to an operational state.

Why Is AI a Good Solution?

The complexities around incident response mainly revolve around coordinating teams, executing complex tasks, and ensuring a threat has been eradicated. AI can help with these issues and save precious time during security incidents through its task execution and automation capabilities. It can automate complex threat containment tasks like containing endpoints or locking down data stores instantly without the need for disparate teams to coordinate.

Current Tools

Currently, Microsoft Security Copilot is the industry leader in using AI for automated incident response. However, a tool that is gaining popularity is Darktrace. This cyber security tool is built on an AI-driven continuous feedback loop that takes in AI inputs and produces AI outcomes to protect corporate data from sophisticated cyber attacks.

3. Vulnerability Scanning and Patch Management

To maintain a secure environment, you must run secure software. This is where vulnerability scanning and patch management come in. These two components of cyber security work together to identify, assess, prioritize, and remediate vulnerabilities. Traditionally, this time-consuming process can lead to vulnerable software lingering in corporate networks for years. AI can help solve this problem.

How Can AI Be Used for Vulnerability Scanning and Patch Management?

AI can automate vulnerability scanning through complex task execution, prioritize vulnerabilities to patch using its access to vast data stores and advanced data analysis capabilities, and even use predictive analysis to identify vulnerabilities that are likely to be exploited in the future. For instance, AI can proactively identify critical software components likely to be targeted and patch these immediately if a vulnerability is disclosed while considering software dependencies so business operations are not impacted.

Why Is AI a Good Solution?

AI’s task execution and data analysis capabilities are ideal for vulnerability scanning and patch management tools. Its ability to analyze the entire corporate IT environment for vulnerable software, predict if the software is likely to be targeted, and then automate the deployment of patches or mitigations means attackers have less time to exploit vulnerable software. This reduces a company’s attack surface and significantly improves its security posture.

Current Tools

The two industry-leading tools that are leveraging AI for patch management are Tenable’s Exposure AI and IBM’s Guardium. Exposure AI is an attack surface management tool that aims to empower security teams to make faster decisions using the power of generative AI. The tool gives teams greater context into their attack surface, revealing insights into potential vulnerabilities, threats, and misconfigurations.

In contrast, Guardium is a data security software that uncovers vulnerabilities to protect data on-premise and in the cloud. It uses AI to adapt to complex data landscapes and changing threat environments so it can provide security teams visibility of what is relevant to them, be it for compliance or data protection.

4. Threat Hunting

Threat hunting proactively defends you from cyber threats. It involves searching for signs of malicious activity not detected by traditional security tools to uncover advanced threats. It is a valuable addition to your cyber security defense to combat new and emerging threats, and AI can be used to enhance your threat hunting capabilities.

How Can AI Be Used for Threat Hunting?

Threat hunters can leverage the power of AI to analyze large and diverse data sets for anomalies to investigate, identify patterns associated with malicious behavior across the entire IT environment, and utilize historical data to predict potential future threats. They can also use AI’s task execution and automation capabilities to automate the initial triage and processing of hunting queries based on contextual data.

Why Is AI a Good Solution?

AI’s ability to analyze big data makes it ideal for threat hunting. Today, large quantities of data are collected from endpoint devices, networks, and servers. The scale of data collected in enterprise IT environments makes it difficult for threat hunters to identify anomalous or suspicious behavior, but AI can change this. It can efficiently sift through data to identify potential threats, predict future ones, and automate investigative tasks.

Current Tools

Tools currently leveraging AI for threat hunting include SentinelOne’s Singularity platform and IBM’s QRadar Security and Event Management (SIEM) tool. Singularity is an AI-driven platform that combines every security component of your enterprise ecosystem to provide maximum context and data enrichment. This enables you to have enterprise-wide visibility and efficiently hunt for emerging threats in real-time.

On the other hand, QRadar is a longstanding SIEM solution that has been greatly extended recently with new modules, such as advisor, data store, and user behavior analytics. These have allowed analysts to harness AI’s data analysis capabilities to uncover hidden patterns and connections in their network security data.

5. Malware Analysis and Reverse Engineering

Attackers use malware to exploit vulnerabilities, gain and maintain access to compromised machines, and extort victims for large sums of money. To defend against malware, cyber security analysts need to analyze and reverse engineer it to find ways of protecting against it.

How Can AI Be Used for Malware Analysis and Reverse Engineering?

AI can greatly improve the efficiency of both malware analysis and reverse engineering. It can be used to efficiently analyze large volumes of malware samples, identify patterns, and output meaningful insights defenders can use to bolster their security. 

Why Is AI a Good Solution?

AI’s ability to perform data analysis and automation at scale makes it ideal for malware analysis and reverse engineering. Unlocking the capability to analyze or reverse engineer malware at scale can significantly improve detection accuracy and enhance an organization’s ability to uncover new threats.

Current Tools

Anti-virus and malware detection tools like Malwarebytes and Kaspersky’s Endpoint Security use AI and machine learning to accurately identify malware, detect malicious software behavior, and autonomously learn new evasion techniques. Meanwhile, plugins like BinNet AI integrate AI with existing reverse engineering platforms so analysts can better understand binary machine code semantically and syntactically. 

6. Penetration Testing and Ethical Hacking

Penetration testing involves identifying and assessing weaknesses in computer systems, networks, and applications by simulating a cyber attack against an organization’s infrastructure. It is often time-intensive when testing large IT environments or complex software applications and requires much specialist knowledge.

How Can AI Be Used for Penetration Testing and Ethical Hacking?

Incorporating AI into the penetration testing process can drastically improve efficiency and scalability. It allows you to test more systems, discover if a vulnerability is exploitable faster, and bridge the knowledge gap when testing systems or applications you are unfamiliar with. 

Why Is AI a Good Solution?

AI’s ability to perform task execution at scale is ideal for testing large IT environments with hundreds or even thousands of machines. Additionally, with AI’s instant access to a vast knowledge base, you can easily identify the severity of any weaknesses you discover and quickly research unfamiliar technologies.  

Current Tools

Outside of vulnerability scanners like Tenable’s Exposure AI, few tools use AI for penetration testing. That said, you can use AI to help you learn about hacking techniques and you will likely see Burp Suite extensions use AI in the future as AI models become more accessible.

7. Risk Assessment

Risk assessment involves systematically evaluating potential risks and vulnerabilities in your environment. You assess risks, understand their impact, prioritize them, and make informed decisions about managing or mitigating them. This requires technical knowledge about exploiting tools and technologies and a deep understanding of your organization’s business processes. AI can address both of these concerns.

How Can AI Be Used for Risk Assessment?

AI can provide you with detailed technical knowledge on any tool or technology almost instantly with access to vast data stores and inform you about the latest vulnerabilities that might impact them. It can also provide you with extended visibility of your environment and highlight the dependencies of your business processes, allowing you to manage risk effectively.

Why Is AI a Good Solution?

AI’s instant access to large amounts of data, ability to enrich information with the latest threat intelligence, and data analysis capabilities make it an ideal solution for risk assessments. Security professionals can efficiently gather, analyze, and discover existing and emerging risks throughout their IT ecosystem and quickly understand the business impact.

Current Tools

Tools, such as Tenable’s Exposure AI and IBM’s Guardium, exist to identify risks exposed by an organization’s attack surface. However, other tools that can be used to perform risk assessments are User and Entity Behavior Analytical (UEBA) tools like IBM’s QRadar, Splunk’s Enterprise Security, and Forcepoint’s Behaviour Analytics. These tools use AI and machine learning to detect threats and insider risks. 

8. Data Loss Prevention

Data Loss Prevention (DLP) focuses on identifying, monitoring, and protecting sensitive information from unauthorized access or exfiltration. This includes protecting Personally Identifiable Information (PII), financial data, and intellectual property using a mixture of tools, procedures, and policies. 

How Can AI Be Used for Data Loss Prevention?

AI can enhance an organization’s ability to organize and protect its sensitive information in several ways:

  • It can use Natural Language Processing (NLP) to analyze documents, emails, and other data to identify and classify sensitive information accurately.
  • It can use UEBA to identify anomalies in data access, indicating unauthorized access or data exfiltration.
  • It can even recognize sensitive information within images or non-text objects.

Why Is AI a Good Solution?

AI’s data analysis capabilities make it a great solution for DLP. Discovering, classifying, and monitoring large data sets is a difficult endeavor. AI’s ability to automate these processes and continuously learn and adapt to emerging risks and changing attack vectors empowers organizations to manage their sensitive data effectively.

Current Tools

The most prominent tool using AI for DLP is Zscaler Data Protection. This tool is designed to protect your data wherever it might be; in the cloud, on endpoints, emails, embedded in automated workloads, and more. It uses AI to classify data at scale, providing visibility into where your sensitive data is located and its risk of exposure.

9. Identity and Access Management

Identify and Access Management (IAM) focuses on providing the right employees with the appropriate access to an organization’s IT resources. It includes user authentication, authorization, and active monitoring to protect against data breaches and meet compliance standards.

How Can AI Be Used for Identity and Access Management?

AI can bring automation, adaptability, and intelligence to identify management processes through advanced data analysis capabilities. The technology can use behavioral biometrics to analyze how users interact with applications and recognize when behavior deviates from normal. It can even dynamically adjust authentication requirements, such as prompting for MFA when anomalies are detected.

Why Is AI a Good Solution?

AI’s access to vast data stores and advanced data analysis capabilities make it ideal for identity and access management. Rather than having a human continuously monitor access to company resources, AI can do this much more efficiently and automatically respond to threats. It can recognize usage patterns, detect anomalies, and provide adaptive authentication to protect data. 

Current Tools

A tool that uses AI for IAM is IBM’s Verify. This tool is built for hybrid and multi-cloud enterprises that must comply with various regulatory standards, such as HIPPA, PCI DSS, and ISO 27001. It uses AI to assess current risks, discover existing access controls, and provide guidance on reducing this risk and meeting compliance standards.

10. Cyber Security Training and Awareness

Cyber security training and awareness are usually provided through online classes, video lectures, and interactive games designed to empower employees to actively protect the organization from cyber threats. AI can enhance these training programs.

How Can AI Be Used for Cyber Security Training and Awareness?

AI allows cyber security training and awareness to be tailored to individual employees. It can analyze a learner’s strengths, weaknesses, and preferences to create a personalized learning path and generate dynamic content relevant to their needs. The AI can even answer a learner’s questions and clarify concepts.

Why Is AI a Good Solution?

AI has access to a vast knowledge library, can assess a learner’s needs through data analysis quickly, and has the power to respond to a learner’s questions in real time. These capabilities make AI an ideal tool for teaching about cyber security and empowering employees to defend themselves from cyber threats. 

Current Tools

The most popular AI tool for learning about cybersecurity is OpenAI’s ChatGPT. This generative AI chatbot takes user questions, processes them, and provides detailed answers using the latest information. It allows you to have a human-like conversation and learn about whatever cyber security topic. You can use custom chatbots called Security GPTs (Generative Pre-Trained Transformers) to focus your learning on specific topics.

Issues, Responsibilities, and Ethical Concerns Using AI in Cyber Security

This article has showcased various uses for AI across many cyber domains. You have seen how AI can be used to attack and defend systems. It is up to you, the human intelligence, to consider AI's potential security, privacy, and ethical implications and use this powerful technology responsibly.  

To help you get started, here are some of the main issues and concerns related to AI:

  • Malicious use: AI-powered systems can be used by hackers and cybercriminals to carry out cyber attacks, scam unsuspecting victims, and develop malware. Many AI tools already exist that can be used to automate and enhance social engineering attacks.
  • Data privacy and user consent: AI is trained on massive datasets using large language models (LLMs). This data needs to come from somewhere, and it is possible that sensitive information can be ingested into this training data, leading to unauthorized access or misuse of that data. This has already led to legal proceedings over copyright infringements
  • Bias: AI can generate biased outcomes if trained on biased or incomplete data. These biases can have unfair, prejudiced, or discriminatory ramifications in applications like predictive policing and facial recognition.
  • Accountability: Who is responsible if an AI system makes a decision that leads to a bad outcome? Many AI systems operate as “black boxes” where no one knows the true reasoning behind their decisions. This makes holding someone accountable or explaining why a decision was made difficult.
  • Regulation: At the time of writing, there are no legal regulations about the development or use of AI. This means companies developing AI tools have no legal requirements to do so ethically or responsibly. This is likely to change in the future with regulations like the EU AI Act, but keeping up with advancements in AI technology will be very challenging for regulators.
  • Security: AI systems can be attacked. NIST has warned that attacks can manipulate training data, exploit vulnerabilities in AI-powered systems, and exfiltrate sensitive personal information from the data. The rapid development of AI is only escalating these existing security issues. Serious research needs to be done to secure this emerging technology.

To address these concerns, it is vital to follow responsible AI practices are followed. This includes robust security measures, transparent and ethical AI development, ongoing monitoring, and human oversight in critical decision-making processes. The implementation of these practices falls on individuals, organizations, and governing bodies. 


AI is a game-changing technology that is revolutionizing how we do cyber security. You have seen ten examples of how AI is being used across multiple cyber security domains and how it solves many of the current problems in cyber. 

You also discovered some of the existing AI-powered tools used throughout the industry to secure systems, empower analysts, and combat emerging cyber security threats. If you want to learn more about AI, consider joining our Accelerator program. You will get access to the latest cyber security courses in AI, a career roadmap, mentors who can help you on your journey, and more! 

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  • Adam Goss

    Adam is a seasoned cyber security professional with extensive experience in cyber threat intelligence and threat hunting. He enjoys learning new tools and technologies, and holds numerous industry qualifications on both the red and blue sides. Adam aims to share the unique insights he has gained from his experiences through his blog articles. You can find Adam on LinkedIn or check out his other projects on LinkTree.

  • Art says:

    I listened and followed the article about 10 examples of AI in cybersecurity and increased my interest. I was a student and member in the past on station X. I’ve been reading books of AI in general but this cybersecurity article about AI sounded good especially with the audio.

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