The Rise of AI: A Transformational Force
AI, AI, AI… It’s the buzzword of the year. Undoubtedly, AI will transform enterprise tech and cybersecurity, much like it’s impacting various aspects of our lives. However, with any disruptive technology poised to change the world, the biggest challenge lies in harnessing it effectively. I see this phase as analogous to the early days of the internet in the 1990s when companies struggled with applying information technology to meet real demands and solve genuine problems. Back then, just having a website was considered innovative and competitive, leading to the infamous ‘dot-com bubble’ where Internet-related companies’ stock prices soared, only to later crash as many of these firms proved unsustainable.
Learning from the Dot-Com Bubble: Applying AI Thoughtfully
Modern tech companies face a similar challenge with AI: how to avoid falling into the trap of implementing AI for the sake of it? This challenge becomes more daunting given the pressure on IT teams from CEOs to incorporate generative AI. To circumvent this trap, it’s crucial to gain a clear understanding of how AI can genuinely benefit your organization and the specific goals you aim to achieve. In this blog post, I intend to share my vision of how AI can add value to patch management, a critical element of cybersecurity hygiene, helping you navigate the realm of AI in enterprise tech and cybersecurity more effectively.
The Business Case for AI in Patch Management
As in other areas, when applied to patch management, AI has the power to automate manual tasks and analyze vast volumes of data, drastically reducing the time required to achieve results. Effectively applying AI to patch management involves aligning its specific areas you plan to automate with existing critical processes to ensure tangible value.
When it comes to patch management, its anticipated results would be improved security, of course, as it stands for addressing vulnerabilities before hackers exploit them. Sounds simple, but, as the number of vulnerabilities surges year over year, and known vulnerabilities continue being among the top reasons for data breaches, it becomes obvious that a radically new approach is required to change this situation and help organizations prioritize and address vulnerabilities in a timely manner. The numbers are showing that it will be simply impossible to do without AI:
- Statista reports a record-breaking 22,514 CVEs in 2022.
- 75 percent of attacks in 2020 targeted vulnerabilities that were at least two years old.
- The Mean Time to Remediate vulnerabilities (MTTR) has increased from 60 to 65 days between 2022 and 2023.
It’s important to note that in the modern cybersecurity landscape, attackers also employ AI. For example, cyber criminals use Chat GPT to generate unique scripts to exploit vulnerable systems. Ignoring AI could mean falling behind in this arms race, and time is running out.
Navigating the Complexity of Vulnerability Prioritization and Remediation
So, which stages of the patch management process are the most time-consuming and labor-intensive, making them crucial targets for AI to expedite vulnerability remediation and reduce MTTR?
Number one is vulnerability prioritization and number two mitigating vulnerabilities for which patches are unavailable. Other stages, like vulnerability discovery and patch deployment, are already automated. Patch prioritization, however, remains challenging to automate due to its multifaceted nature and the need for a nuanced understanding of an organization’s unique context. Complexity arises from various factors, including vulnerability severity, the presence of active exploits, and system criticality.
Automating mitigation of vulnerabilities without available patches is a complex task, too, as it implies non-standard approach. When patches are unavailable, there are no predefined actions that can be easily automated. Unlike vulnerabilities with patches, which automated systems can address through established procedures, vulnerabilities without patches require unique, case-specific compensating controls. Particularly for large enterprises with thousands of endpoints and various vulnerabilities, consistently designing these compensating controls can be difficult.
AI Bridging the Gap in Patch Management
AI has the potential to bridge the existing gap in patch management between vulnerability discovery and remediation. When combined with a vulnerability discovery engine within a patch management solution, a trained generative AI model can prioritize vulnerabilities, suggest, generate and apply case-specific compensating controls, helping IT teams secure their environment and empowering them to bridge the skills gap. This challenge would otherwise be impossible to overcome due to the complexity of modern IT environments and the resource constraints organizations face.
Furthermore, AI can go beyond patching by helping organizations address vulnerabilities even when patches are unavailable or when they need to mitigate security risks while preparing for patch deployment.
This is our vision of applying AI to patch management, which we believe empowers organizations to significantly reduce their MTTR for vulnerabilities and stay ahead of cyber threats.
Exploring the Future of Patch Management with AI
I’m truly excited about the upcoming Action1 AI features. I invite you to sign up for the waiting list and be the first to know when it goes live. Join us in our journey to re-invent patch management with AI and enhance your organization’s cybersecurity posture.
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