How MSSPs & Artificial Intelligence Can Mitigate Zero-Day Threats

So, here’s the problem: unknown zero-day threats are just that — unknown. You have no way (besides historical experience) to predict the next vulnerability avenue that will be exploited. You, therefore, don’t know what will need patching or what extra security layer needs injecting. This ultimately leads to a forecast-costing dilemma as you cannot predict the man hours involved.

The other quandary faced when tackling complex targeted zero days is the skills gap. Staffing a security operations center (SOC) with highly skilled cybersecurity professionals comes at a cost and only becomes profitable with economies of scale that a large customer base brings.

Coupled with the shortage of skilled cybersecurity professionals in the open market, how can you get your SOC off the ground? Could artificial intelligence (AI) level the playing field?

Machine Learning Reality Check

Machine learning and behavioral analytics continue to grow and become synonymous with zero-day threat protection. Is this all hype or is it the new reality? The truth is, it is both.

There is a lot of hype, but for good reason: AI works. Big data is needed to see the behaviors and therein the anomalies or outright nefarious activities that human oversight would mostly fail to catch. Delivered as a layered security approach, AI is the only way to truly protect against modern cyber warfare, but not all AI is deterministic and herein lies the hidden cost to your bottom line.

AI-based analysis tools that provide forensics are very powerful, but the horse has bolted by the time they are used. This approach is akin to intrusion detection systems (IDS) versus intrusion prevention systems (IPS). The former are great for retrospective audits, but what is the cleanup cost? This usage of behavioral analysis AI solely for detection is not MSSP-friendly. What you need is automated, real-time breach detection and prevention. Prevention is key.

So, how do you create an effective prevention technology? You need security layers that filter the malware noise, so each can be more efficient at its detection and prevention function than the last. That means signature-based solutions are still necessary. In fact, they are as important as ever as one of the first layers of defense in your arsenal (content filtering comes in at the top spot).

By SonicWall metrics, the ever-growing bombardment of attacks the average network faces stands at 1,200-plus per day (check out the mid-year update to the 2018 SonicWall Cyber Threat Report for more details).

When you do the math, it’s easy to see that with millions of active firewalls, it’s not practical to perform deep analysis on every payload. For the best results, you must efficiently fingerprint and filter everything that has gone before.

Aren’t All Sandboxes Basically the Same?

Only by understanding the behavior of the application and watching what it’s attempting to do, can you uncover malicious intent and criminal action. The best environment to do this is a sandbox, but no SOC manpower in the world could accomplish this with humans at scale. In order to be effective, you must turn to AI.

AI understands the big data coming from behavioral analysis. It can adapt the discovery approach to uncover threats that try to hide and, once determined as malicious, can fingerprint the payload via signature, turning a zero day into a known threat. It is the speed of propagation of this new, known signature to the protection appliances participating in the mesh protection network that drives the efficiencies to discover more threats.

Also, it’s the size of the mesh network catchment area that allows you the largest overall service area of attaches, which helps your AI quickly learn from the largest sample data set.

Luckily, SonicWall has you covered on all these fronts. With more than 1 million sensors deployed across 215 territories and countries, SonicWall has one of the largest global footprint of active firewalls. Plus, the cloud-based, multi-engine SonicWall Capture Advanced Threat Protection (ATP) sandbox service discovers and stops unknown, zero-day attacks, such as ransomware, at the gateway with automated remediation.

Our recent introduction of the patent-pending Real-Time Deep Memory Inspection (RTDMITM) technology, which inspects memory in real time, can detect and prevent chip vulnerability attaches such as Spectre, Meltdown and Foreshadow. It’s included with every Capture ATP activation.

At SonicWall, the mantra of automated, real-time breach detection and prevention is fundamental to our security portfolio. It is how our partners drive predictable operational expenditures in the most challenging security environments. Only via connected solutions, utilizing shared intelligence, can you protect against all cyber threat vectors.


A version of this story originally appeared on MSSP Alert and was republished with permission.

James Whewell
Solution Architect | SonicWall
James is currently the lead solution architect for SonicWall’s enterprise sales team. With more than 20 years in the cybersecurity industry, James has provided solution architecture, product management and security consultancy guidance to many Fortune 500 companies. With a career spanning both sides of the vendor-employee relationship, James has had a passion for access and mobility security since his startup days launching the SSL VPN space.
1 reply

Trackbacks & Pingbacks

  1. […] How MSSPs & Artificial Intelligence Can Mitigate Zero-Day Threats – James Whewell […]

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply