Is AI the solution to vulnerability management?
Director of Security Operations in Finance (non-banking), 5,001 - 10,000 employees
The big challenge with vulnerability management—even with the small number we have—is narrowing down which ones are most important. I understand that every vulnerability doesn't have the same level of importance within the environment and we have the rating system, etc. But when you have thousands of machines sitting in the environment, imagine having the ability to say, "This is important, but the reality of the situation is that you need to patch these 47 machines out of your fleet of 15K right now. Because as I look at the environment, these are the ones that either have the most critical data or are on an exploitation path so this needs to be closed." Then I could say to my team, "This is where I really need you to focus." Think about what that does in terms of true risk management in the environment and the level of effort required. I am doing that manually now and there are just not enough hours in the day.Director of IT in Software, 201 - 500 employees
Not the solution but will enhance it drasticallyCIO in Education, 1,001 - 5,000 employees
No, but proper utilization of AI in the space could possibly benefit us all.Chief Information Officer in Manufacturing, 10,001+ employees
I don't think it's the be-all solution for vulnerability management but more another tool in the toolbox to manage and administer your vulnerability strategic platform.Content you might like
Yes – very optimistic!31%
Yes – mildly optimistic.56%
No7%
I’m not sure5%
242 PARTICIPANTS
CTO in Software, 201 - 500 employees
Without a doubt - Technical Debt! It's a ball and chain that creates an ever increasing drag on any organization, stifles innovation, and prevents transformation.ISSO and Director of the IRU in Healthcare and Biotech, 10,001+ employees
I would definitely suggest this based of how you categorize your types of data/systems and information being stored in certain parts of your data center. I think it’s really dependent on the size of your organization and ...read moreStructured Business Data62%
Unstructured Business Data37%
532 PARTICIPANTS
That's what we have done at RiskSense: We took what Google did 20 years ago with apps and authorities from a page rank perspective, and then re-implemented that for cyber today from a vulnerability and threat management perspective. For example, one metric is called Term frequency–Inverse document frequency (tf–idf) and measures how often or how rarely the term occurred.
So using that approach, we went back and looked at all exploit code that's committed to Metasploit and PRCs. Rather than taking the tags at face value, we studied those exploits ourselves and labeled them using Natural Language Processing (NLP). It used to take four days for an analyst to understand an exploit and label it; today we can do it in four seconds. That's a huge win for us. We run the models on a continual basis now and when a new exploit comes, we label it. If we don't get the accuracy then a human looks at it. It's a very typical live example.