Artificial Intelligence in Cybersecurity: The Ultimate Manual

Protecting an enterprise IT environment is a tall task. Trusting an organization’s cybersecurity defense to human intervention alone likely won’t be enough to combat sophisticated attacks.

Fortunately, the best cybersecurity tools leverage artificial intelligence (AI) to help companies of all sizes across every industry. 

What is Artificial Intelligence in Cybersecurity Anyway?

Artificial intelligence and machine learning have become crucial technologies for information security. AI has the ability to detect threats, improve response times, identify patterns, and analyze millions of data points.

In addition to threat detection, AI can automatically respond to threats and stop breaches before they become a problem.

According to a recent report from TechRepublic, the average mid-sized organization gets more than 200,000 cyber alerts per day. Even large cybersecurity teams can’t possibly respond to such a high volume of potential threats.

But AI can process these threats with ease. It alleviates the burdens of IT security teams for the vast majority of threat prevention steps and assists with detection and response.

How Artificial Intelligence In Cybersecurity Works

Every AI cybersecurity solution is a bit different. But at their core, all AI tools get smarter over time. 

Artificial intelligence leverages deep learning and machine learning to assess the behavior of a network and IT environment over time. It recognizes patterns of good behavior, allowing the system to detect anomalies that could be perceived as threats. 

AI can even detect unknown threats that have traits similar to known malware, spam, DDoS attacks, ransomware, phishing, and more. 

As cybersecurity tools become more advanced, hackers and attackers use more sophisticated attack methods to breach networks. Artificial intelligence in cybersecurity has become a must-have feature to prevent modern attacks.

One of the biggest benefits of AI in cybersecurity is its ability to analyze massive amounts of data. It’s nearly impossible for an IT security team to manually review all traffic. But AI can detect malicious behavior masked as normal IT traffic. 

While AI does most of the work on its own, it can also assist with human-related tasks. The research and data conducted by AI can be helpful for IT teams to improve vulnerability management strategies. AI can help detect weak points in a network or system, which makes it easier for organizations to identify areas that need improvement. 

Common Use Cases For AI in Cybersecurity

There are lots of different ways that organizations are harnessing the power of AI for IT security. But these are some of the most common:

  • Threat Detection — AI can detect malicious activity faster than traditional software and humans. Artificial intelligence uses pattern recognition, data analysis, and other principles to stop malware and attacks before they infiltrate a system. In addition to getting smarter by assessing your own network traffic, AI learns from outside cyber threats as well. 
  • Bot Defense — Bots make up more than half of Internet traffic, and many of these bots are dangerous. AI uses advanced algorithms to detect the difference between good bots and bad bots. So AI can allow search engine crawlers but deny account takeover bots.
  • Risk Assessment — AI can help security teams analyze an inventory of all assets, users, and devices on the network with different access levels. Based on this analysis, AI can generate reports showing which parts of your network are most likely to be compromised. Then your team can allocate resources accordingly or adjust access levels to high-value assets.
  • Endpoint Security — Basic antivirus software and VPNs can only stop some malicious threats. This technology typically uses signatures to protect endpoints based on known threats and signature matching. But if new signatures haven’t been updated by the software vendor, malware and viruses can sneak through. AI prevents this by flagging anything that’s an anomaly to protect against new threats.

Problems With AI in Cybersecurity

Like anything else, artificial intelligence in cybersecurity isn’t perfect. There are some drawbacks that you need to be aware of as you’re using AI to secure your organization.

The financial investment required to build and maintain an enterprise AI security system from scratch is significant. Most companies are better off using a pre-built AI solution from a cybersecurity software vendor. 

False positives at the beginning can cause problems for the daily operations of an organization. Some AI systems might flag normal behavior as something suspicious, which could make it difficult for employees to access files or perform tasks. False positives typically lessen over time as the AI gets smarter and understands your network better.

AI has also made it possible to collect and process an unfathomable amount of data. If third-party organizations also have access to this data, you need to make sure all of the data transfer and storage is compliant with mandates like GDPR, CCPA, PCI DSS, and more.

It’s also worth noting that hackers and malicious users can also leverage the power of AI to launch large-scale attacks. In the wrong hands, AI can help users with malicious intent exploit vulnerabilities in network security systems.

Example #1: Tessian

Tessian is a UK-based security vendor that specializes in enterprise email security. This use-case-specific solution uses AI in many different ways for its product offerings.

The software is used for phishing protection, account takeover prevention, ransomware prevention, compliance, data loss prevention, and more.

Tessian uses behavioral intelligence to analyze over a year’s worth of enterprise historical data. This includes corporate emails, data on the company network, and data within the Tessian Global Threat Network. It uses this data to determine traits associated with inbound email attacks. 

The AI can help identify social engineering, account takeovers, whaling attacks, spear phishing, impersonation attacks, and more. All of this is accomplished through analyzing user behavior, email content, and other analytics. As a result, Tessian can stop email threats in real-time.

Example #2: Sophos Endpoint Protection

Sophos is a well-recognized name in the world of IT security. They offer a wide range of solutions for different use cases, industries, and business types.

The endpoint security tools from Sophos use AI and deep learning to stop both known and unknown threats—without having to rely on signatures. 

When you enable the deep learning features of Sophos AI, you turn your endpoint protection approach from reactive to proactive. This software enhances malware detection and anti-ransomware to keep endpoints safe.

The AI works to not only detect potential threats but also neutralize those threats before they become a bigger problem. Sophos AI is highly scalable, and it can automatically find the best combination of inputs required to detect even the most sophisticated attacks. 

How to Get Started With AI in Cybersecurity

Using artificial intelligence to improve an organization’s cybersecurity standards will look a bit different for everyone. With that said, the steps below outline the general process you can follow to get started:

Step 1: Audit Your IT Infrastructure

You need to assess your current situation before you start shopping around for solutions, installing software, or implementing new policies. This will make it much easier for you to identify your needs and ultimately find an AI tool that supports them.

Depending on the size of your organization, this step can be a lengthy process. 

Start with some simple, quick wins to build momentum, and continue from there. If you have existing systems in place for IT security, endpoint protection, or network monitoring, those tools can help provide you with some valuable insights.

You’ll eventually want a list of devices, software, applications, and servers on your network. From there, you can take things one step further and look at your users and access control policies. 

Step 2: Review Your Data Platforms

As previously mentioned, data is a huge part of artificial intelligence. So you need to understand where your existing data points are coming from and how that data will be integrated with an AI cybersecurity solution. 

For midsize organizations and enterprises, there will likely be lots of data coming from multiple sources. In some cases, it might make sense to create a unified platform for all of the data. This would give your AI system a single source of truth to work with.

With that in mind, many of the best cybersecurity tools out there are flexible enough to accommodate data from multiple sources. Unifying the data might make more sense for you internally, but it’s not necessarily a requirement to leverage AI.

As you’re going through this process, you’ll want to identify your highest-value assets. Piggybacking off of the work done in step one should start to give you some insight into your vulnerabilities.

Step 3: Compare Security Vendors

Creating your own AI platform and algorithm is possible. But it’s not the best or fastest path to success for most businesses. You’re much better off relying on existing IT security solutions with built-in AI. 

The companies mentioned earlier in this guide as examples are viable options to consider. But you can conduct your own research to see which vendors truly accommodate your needs.

You’ll ultimately be compiling a shortlist of your top contenders. From there, you’ll need to schedule calls and demos to get a better understanding of the products. In addition to your CTO or CSO, you may want to include other IT security staff in the decision-making process.

C-level executives are obviously focusing on big-picture initiatives. But they might not be interacting with tools the same way as your staff working on the day-to-day operations. 

As you’re comparing vendors, you can narrow your options based on factors such as:

  • Budget
  • Implementation process
  • Industry-specific solutions
  • Use-case-specific needs
  • Employee size
  • Number of endpoints on your network
  • Level of customization
  • Compatibility

In some cases, you’ll be able to get a free audit or something like that from a security vendor. This gives them a bit more information about your organization and network. 

Compatibility is really important here, as not every IT security solution will play nice with servers or existing systems. If you need to make small tweaks here and there, it shouldn’t be a dealbreaker. But you shouldn’t have to reconfigure your entire network.

You’ll also want to consider the level of support you’re getting from the vendor outside of the AI and security tools. For example, some vendors offer managed detection and response—meaning they will handle threats. If you already have the in-house resources for response and remediation, you may not need that extra level of support from your vendor.

Step 4: Deployment and Training

Once you’ve settled on a tool, it’s time to deploy it on your IT infrastructure. This could be a cloud deployment, on-premises deployment, or potentially a hybrid deployment. 

Again, this is all based on your organization and its specific needs. 

Large-scale deployments typically take some time. You might not be able to apply the system to your entire IT infrastructure at once, and that’s ok. 

While AI can handle a ton of work on its own, it doesn’t run completely without human interaction. You still need to train your staff to make sure the AI is doing what you want it to do. Your staff must also understand how to make sense of the reports and alerts.

For example, let’s say your AI tool detects a potential threat and alerts your security team. Now what? 

Effective training could take several months based on the scale of the deployment. Many enterprise security vendors will walk you through this process, but it’s not a guarantee. 

Step 5: Risk Assessment

Continuous improvement is a key part of using AI for cybersecurity. As the technology gets smarter over time, you need to harness the outputs and understand what everything means for your network.

Where are your weak points? What types of threats have penetrated your system? What systems, applications, servers, users, or data sets are being targeted the most?

Artificial intelligence in cyber security is not a “set it and forget it” initiative. In addition to reporting, you can also use AI tools to help you with other aspects of risk assessment.

AI-based penetration testing has been a popular use case for risk assessment. It can be a human replacement, or at least a human-assisted alternative, to traditional pen tests. 

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Bryan Wise
Bryan Wise,
CIO of GitLab

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