Ivanti: Machine Learning, Generative AI & Agentic AI in Security

Name: Ivanti: Machine Learning, Generative AI & Agentic AI in Security
Uploaded: 2026-10-09T06:50:29-04:00
Duration: 3 min 22 s
Description: Discover how AI transforms cybersecurity! In this video, Chris Goettl, VP Product Management at Ivanti explores three critical types of artificial intelligence reshaping security operations: machine learning, generative AI and agentic AI. Machine learn...

Ivanti

10/09/2026

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Transcript

solutions today. We're going to talk about three of those and break them down for you. The first of those is machine learning or ML. This can often be used to learn patterns from large data sets and to make predictions or help classify information. You will definitely be seeing these used in a lot of vulnerability management platforms like Ivanti's risk-based vulnerability management solution, where we are using machine learning algorithms to help classify and better prioritize risks. So with that information, I can take a vulnerability, I can learn more about it, like if it's actively being exploited and if it's tied to ransomware or other different types of campaigns. With that information, we can classify a risk-adjusted score to give you a better idea of what the impact of that vulnerability is. Other examples might be network security solutions, where secure access is approved or denied based on things like user behaviors. A user traveling between two different geographic locations in the same day could be suspicious behavior and might be something that needs to be flagged and checked before being allowed. The second AI type that we're going to talk about is generative AI. Again, many solutions are using generative AI today. Many of you are using this to do fun things like generating text or creating new content like images or videos. In security and IT solutions, this is often being used as a generative interaction or a prompt-based interaction that's helping users be more efficient. I can ask a question, get follow-up guidance, and take steps much more quickly and efficiently than a human-driven experience alone. The third AI type that we're going to talk about is agentic AI. This is where we start to get into a framework of multiple AI agents working together. Each of these will have a specialized function. Now, in the cybersecurity world, there are several examples of this already going on as well. This could be anything from autonomous incident response, where behaviors are detected and then additional steps are taken or automated, things like isolating a device. They could be adaptive threat hunting, going out and looking for more subtle, persistent behaviors that are occurring within your environment and potentially opening those up as potential threats that a human could go and then start to take action on. Or things like automated vulnerability management. If we detect a vulnerability or an exposure, additional autonomous actions might be able to be taken, like running a script to change configuration to make that scenario be compliant with our baselines. Or being able to apply an update or change behavior on that device to mitigate risks in that situation. Now, there's obviously going to be many, many more examples over time as new AI technologies are introduced into solutions in the market. But this gives you an idea of some of the common types you're going to run into and how they can help improve your experiences in your cybersecurity solutions. Microsoft Mechanics www.microsoft.com www.microsoft.com

TL;DR

Machine learning in cybersecurity analyzes massive datasets to detect threats, classify vulnerabilities by risk context, and identify suspicious user behaviors like impossible travel patterns.
Generative AI enables prompt-based interactions that help security practitioners work more efficiently by providing instant guidance and automating routine tasks.
Agentic AI deploys multiple specialized agents working together to autonomously execute security workflows including incident response, threat hunting, and vulnerability remediation.

Summary

This educational overview examines three distinct AI technologies transforming cybersecurity operations: machine learning, generative AI, and agentic AI. Machine learning analyzes large datasets to detect patterns and classify threats, powering risk-based vulnerability management solutions that prioritize vulnerabilities based on active exploitation and ransomware associations. It also enables user and entity behavior analytics (UEBA) to identify suspicious activities like impossible travel scenarios. Generative AI streamlines security workflows through prompt-based interactions, helping practitioners work more efficiently by providing instant guidance and follow-up steps. Agentic AI represents the most advanced application, deploying multiple specialized AI agents working together to execute autonomous security workflows—from incident response and device isolation to adaptive threat hunting and automated vulnerability remediation through configuration changes or patch deployment. These technologies collectively enable security teams to move from reactive to proactive defense postures while reducing manual workload.

Chapters

0:00 - Introduction to AI in Cybersecurity
0:10 - Machine Learning Applications
1:16 - Generative AI Use Cases
1:45 - Agentic AI Frameworks

Key Quotes

0:25 "You will definitely be seeing these used in a lot of vulnerability management platforms like Ivanti's risk-based vulnerability management solution, where we are using machine learning algorithms to help classify and better prioritize risks."
0:41 "I can take a vulnerability, I can learn more about it, like if it's actively being exploited and if it's tied to ransomware or other different types of campaigns."
1:49 "This is where we start to get into a framework of multiple AI agents working together. Each of these will have a specialized function."

FAQ

How does machine learning improve vulnerability management?

Machine learning algorithms analyze vulnerability data to determine if threats are actively being exploited or tied to ransomware campaigns, then assign risk-adjusted scores that help security teams prioritize remediation efforts based on actual threat context rather than just severity ratings.

What makes agentic AI different from other AI types in security?

Agentic AI uses multiple specialized AI agents working together in a framework to autonomously execute multi-step security workflows—such as detecting a threat, isolating a device, and initiating remediation—without requiring human intervention at each stage.

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