In the last decade, a considerable amount of time and money has been invested in the advancement of Artificial Intelligence (AI), and these efforts have yielded real, observable results. A good example of this is AI’s recent utilization in the discovery, production, and distribution of Covid-19 vaccines.
Perhaps nowhere is AI becoming more important than in cybersecurity. In fact, most modern antivirus software solutions use some form of AI and machine learning. Integrating AI into the software security suite should yield more agile cybersecurity where constant definition updates are either rendered obsolete or more seamless and flexible.
In this guide, we’ll explore how AI - particularly Zero-Shot AI – can make commercial and mass cybersecurity more robust and agile.
What is zero-shot AI?
AI requires machines to behave and learn similarly to how human beings do. The most commonly studied type of AI is artificial narrow intelligence (ANI). It involves machine learning, where AI is specialized in a single field or problem, such as cybersecurity.
But to advance AI further, we need to find a way to make machines learn and discover new information on their own. This is where zero-shot learning comes in. Machine learning trains a model presented with new information to categorize it into a class based on previous information. In zero-shot learning, the model is expected to classify with as little information as possible.
For instance, let’s say a machine is presented with a brief phylogeny (an evolutionary chart) of the canid species and the avian species. It’s presented with a new animal sample, such as a golden retriever. Despite not having a sample of that breed in its database, it should be able to classify it as a domestic dog.
While this is all very fascinating, how can Zero-Shot AI be used in cybersecurity? And how can it benefit you or your company?
Zero-shot AI and enterprise cybersecurity
Surprisingly, the number of reported data breach incidents has dipped in the last three years. Yet, despite less frequent reports of data breaches, cybercriminals always manage to diversify their hacks and their targets. Of course, these automatic attacks are also facilitated through the power of artificial intelligence. To combat this, network and software security experts have to fire with fire and by implementing more advanced AI-based defenses.
Enterprises are the most vulnerable to these types of attacks, primarily because of the large financial data that businesses have to handle. Business owners must have the best and latest security solutions – from the network that connects all work devices to the software that employees use.
For instance, it’s important to use secure invoicing software that comes with critical security measures such as PCI compliance and secure cloud storage. However, this is only a part of the equation. To contend with the speed at which hackers advance, we need more adaptable and agile solutions.
For many businesses, managing and securing data is one of their greatest challenges. Not only does zero-shot AI have the ability to parse through large sets of basic and telemetry data, but it also has the power to interpret it and use it to strengthen enterprise security.
Zero-shot AI can function as an almost omnipresent figure that’s constantly analyzing logs, network traffic, and user actions to scan for irregularities. A study conducted by IBM found that human error was the contributing cause of 95% of all data breaches.
Fortunately, zero-shot AI requires very little human interaction to initiate learning or oversite. A zero-shot AI solution can keep a track record of all employee actions and analyze which behaviors may increase the risk of cyberattacks. This decreases the risk of insider threats and outsider threats alike.
For example, one of the biggest data leaks of the last ten years came in March 2020, where the records of 10.88 billion user records were leaked. The leaked information contained passwords, email addresses, chat transcripts, and more.
A zero-shot AI could prevent data leaks like this. For instance, while monitoring your server or network traffic, the AI could pick up on large segments of data being copied or moved to a foreign server. The AI can then report this anomaly to your network administrator so an analyst can then take action and adjust security protocols to prevent similar attacks in the future. In this way, zero-shot learning is a way to make network vulnerability assessments more efficient and continuous.
A multi-layered approach
Not every business has the means to implement advanced security measures like zero-shot learning all in one go. In the meantime, it is important to take other steps to secure your business.
First, we recommend using a VPN. As online cybersecurity expert Ludovic Rembert from Privacy Canada notes, VPNs are an essential tool to use for encrypting your online communications.
“A Virtual Private Network (VPN) may sound complicated, but the idea is pretty simple,” says Rembert. “A VPN is a service that creates a virtual tunnel of encrypted data flowing between the user (that’s you) and the server (that’s the internet). The bottom line is a VPN hides your information from spies, hackers, snoops, and anyone else who might want to steal and monetize your information.”
In addition to a VPN, you should also consider implementing:
- Multi-factor authentication
- Zero-trust network access (ZTNA)
- Container security
- Regular network vulnerability assessments
- Regular external threat hunts on your network
While some sources have suggested that cybercrime is on a decline thanks to more improved cybersecurity, it’s important to remember that a lot of cyber crimes go unreported or even undiscovered. Enterprises are still plagued by advanced persistent threats, and you can’t afford to be complacent. Implementing a zero-shot AI approach can help spot nuanced intrusions that escape traditional human detection, and the adoption of such tech isn’t as far into the future as it may seem.