Integrating Generative AI with XDR


Generative AI has been a trending term or topic within the tech industry, requiring top personalities like Elon Musk to speak about the technology. It is quite different from the previous versions of artificial intelligence and has a wide range of use cases, including the cybersecurity sector.

Now, cybersecurity personnel can leverage the advanced efficiency and functionality of generative AI to help fight and overcome ever-increasing cybersecurity attacks. There have been cases where GenAI was used in the analysis of intelligence feeds to provide enhanced threat intelligence. The same can happen with XDR solutions. Thus, in this article, we will explore how generative AI works and how it can be integrated with XDR solutions.

What’s Generative AI

Before we dive into the benefits of AI-driven XDR, let’s first look into what generative AI is and how it works. Generative AI is simply the combination of machine learning and artificial intelligence that helps generate new information in a way that has not been done in the past. Previously, legacy machine learning models were used in business and industry to make predictions based on querying databases. However, one of the downsides they had was that they were not able to create new data or information on their own.

Enter generative AI, which combines large language models (LLMs), machine learning, and artificial intelligence to generate new data. It can be said that the technology behind generative AI is not new, but the development of user-friendly interfaces has given it a lot of popularity. Google Gemini, ChatGPT, and many other generative AI solutions have forever changed the application and perception of this technology. One of the latest applications with generative AI is that it can be integrated into cybersecurity solutions. A good example is the integration of generative AI in the cybersecurity solutions offered by Stellar Cyber.

Integrating Generative AI with XDR

Extended detection and response, often abbreviated as XDR, is a security solution that helps provide individuals and organizations with an all-encompassing approach to cybersecurity. It usually combines artificial intelligence and automation to ensure the prevention and proper response to cyber threats. Integrating generative AI with XDR is mainly to fast-track the detection, response, and mitigation of cyber attacks.

Furthermore, combining generative AI with XDR helps to provide deeper insight into the details of a cybersecurity incident that was not initially understood by the security team. For instance, assuming a user was trying to download a malicious script, a generative AI model could tell from a Suspicious Activity Monitoring (SAM) that the user also tried to turn off the firewall services at the endpoint.

Integrating GenAI in an XDR solution not only provides more insights into cyberattacks but also gives recommendations on how to prevent and mitigate attacks. These GenAI models can use data from supporting playbooks to provide suggestions that will help resolve or investigate an incident.

How to Pick a Generative AI Model for an XDR Solution

There are some factors to consider while trying to integrate a GenAI with an XDR solution. It is very important to point out that GenAI or any other AI solution handles a lot of data, including customer data and certain things must be considered to avoid entering into a legal debacle.

One of the major things an organization should consider is the versatility of generative AI. Since many organizations have different cybersecurity needs, generative AI should be versatile enough to provide solutions or recommendations in any situation. Immediate implementation is also another thing to consider, as speed is very important in cybersecurity. A GenAI model should provide information or data as fast as possible to enable the security operations center (SOC) to take action.

A GenAI model should also allow an organization to build a model-agnostic design. This means the generative AI model should provide them the opportunity to integrate additional AI sources in the future. As said before, data privacy is also another thing to look at. Ensure that the GenAI model is governed by compliance certifications such as SOC2, GDPR, and others.

Challenges of Integrating Generative AI with XDR

Below, we will discuss some of the challenges an individual or organization will face when integrating GenAI into their XDR solution.

Training Challenges

GenAI models have to be trained before they can provide the data or information required. However, training generative AI models within the cybersecurity context requires significant computational resources. Not only is it resource-intensive, but it also takes a lot of time and often requires technical expertise, which can be a challenge for some organizations. This is why using XDR solutions like Stellar Cyber is necessary, as they already have GenAI integrations, saving organizations the stress of doing so themselves.

Infrastructure and Hardware Considerations

As mentioned before, running generative AI models can be resource-intensive. This is because it often requires substantial hardware resources, such as high-end GPUs and TPUs, to carry out intensive computations. The implication of this is that any organization planning to integrate GenAI with XDR should prepare for upfront costs relating to hardware and infrastructure.

Data Leakages and Privacy

Data leakages and privacy are other issues an organization will likely have by integrating XDR with GenAI. Generative AI models often handle sensitive data during their training and operations, and these are sometimes customer’s data. One of the major implications of customer data leakage due to GenAI is that it could lead to massive legal issues. Thus, an organization needs sophisticated security measures to prevent invasion of data privacy or unauthorized access. Furthermore, any GenAI model an organization is choosing must comply with specific regulatory certifications such as SOC2 and GDPR.

Issues with Prompt Engineering and Context

Context while prompting a GenAI model is another hurdle an organization will face when integrating with XDR solutions. For instance, asking ChatGPT to “explain legacy SIEM” and “explain next-gen SIEM” will yield different results. Thus, giving the GenAI model the proper context in every cybersecurity situation can be challenging.


Generative AI is a new version of artificial intelligence that combines with machine learning to provide or generate new information about a subject. Over time, it has been discovered that GenAI can be used within the cybersecurity sector, integrating it into solutions like XDR. Combining the two helps organizations get deeper insight into the details of a cybersecurity incident. Nevertheless, there might be challenges, including the risks of data breaches, training challenges, hardware considerations, and many more.

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