Zero-day cyberattacks pose unique challenges for IT organizations, due in large part to their inherent novelty. Verizon’s 2024 Data Breach Investigations Report (DBIR) found that attacks involving vulnerability exploits grew 180% from the previous year, and attackers’ widespread use of AI tools makes it easier for them to carry out more exploits faster. But new solutions are available to help organizations fight back, with deep learning providing a foundation for a preemptive approach to data security that can finally get ahead of zero-day attacks.
Solving the challenges behind zero-day attacks
Because these attacks target vulnerabilities that aren’t publicly known, zero-day exploits are often missed by signature-based threat detection platforms that rely on lists of recognized attack attributes. Once a zero-day attack enters the environment, IT has historically had few tools available to stop or defend against it. The best thing an enterprise can hope for is to receive some early-ish warning that something’s wrong and then try to respond to it as quickly as possible.
Innovative platforms are turning the tables, empowering businesses to stop zero-day attacks with more advanced technology. For example, some solutions feature lightweight agents that can be installed on customer endpoints, so when a user downloads a file or when a file is in transit that could touch that endpoint, the file is quickly scanned for malicious content. If the agent spots anything malicious within the file, it’s blocked before it can execute. The scanning happens so fast that an unsuspecting or inattentive end user doesn’t even have a chance to click on or interact with the file. This switch to preemptive action is a meaningful step forward in blocking zero-day attacks before they can unleash their payloads.
Plugging the holes in patch releases
With traditional tools, providers push routine security patches to update the list of known threats, allowing the software to spot and, hopefully, stop them. However, despite the comprehensive nature of many vendors’ lists, there are still gaps that can reduce the effectiveness of the company’s defensive efforts. One problem is that zero-day exploits can take a long time to identify, and even after a vulnerability is known, there may still be a days- or weeks-long gap before it’s included in a patch.
Patch release schedules often present their own challenges. Frequent patch releases may stress a cybersecurity vendor’s quality assurance process, allowing errors to infiltrate customers’ networks. Those mistakes can disrupt operations and potentially hop from the originating software to other systems in the environment. The more frequently an enterprise receives patches, the greater the chances that something will go wrong. If that little thing snowballs into a big thing, IT will have a new and urgent problem to fix.
Zero-day attacks and the AI difference
Solutions with deep-learning capabilities can address many of the drawbacks of traditional tools. For example, they can bridge the holes that may appear between patches. Rather than relying on frequently updated lists of attack vectors and attributes, which can become outdated almost as soon as they’re released, platforms with advanced AI capabilities leverage alternative methods to stay ahead of zero-day attacks.
Working much like the human brain, these tools incorporate a neural network that can detect previously unknown attack patterns faster. In the case of zero-day attacks, this means the platform can make connections that didn’t exist before, identifying novel cyberattacks or malicious software. Innovative solutions with deep learning capabilities can see a threat it wasn’t trained specifically to identify and, employing pattern recognition that’s more advanced than traditional AI and machine learning, block the attack. Leading tools have an efficacy greater than 99%, giving IT teams more power to stop zero-day attacks than ever before.
Augment detection and response, reduce overall risk
Using advanced platforms alongside traditional tools, it’s now possible to achieve two critical goals: stopping more zero-day attacks and reducing security operations center (SOC) stress. With advanced AI on the frontlines, detection and response solutions no longer need to wade through every potential threat in the search for genuinely malicious items. Existing platforms can be more efficient and accurate. In addition, as more incoming dangers are evaluated and managed automatically, the SOC team can focus on high-priority alerts elevated by the cybersecurity tools rather than wading through the entire flood of concerns.
New solutions with AI and deep learning at their core have a high success rate, but they aren’t perfect. Companies should continue to employ a layered approach to cybersecurity, with deep-learning platforms and traditional detection and response tools working hand in hand to thwart zero-day, ransomware, and other attacks. Together, these layers can block threats at multiple levels, relieve the SOC team of unnecessary alerts, and drastically reduce the organization’s overall risk.
About the Author
Dave Floyd is the Vice President of Cybersecurity Sales and Service for Hughes Network Systems. He works directly with organizations and enterprises across industries to provide tailored cybersecurity solutions that address pain points and build a comprehensive cybersecurity posture for their businesses. Dave can be reached online at linkedin.com/in/davidefloyd/ and at our company website https://www.hughes.com/
