AI & Cybersecurity - Part 2: Threat Detection, Prevention, and Remediation

Posted by Alexis Vander Wilt
Alexis Vander Wilt

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Introduction

Welcome back and thanks for tuning in! This is part 2 of this series on AI & Cybersecurity. The focus of this article is the use of AI in its applied form for Threat Detection, Prevention, and Remediation.

Threat Detection

Hackers are designing malware that is more advanced than ever before. They are learning how to disguise their attacks to make them less detectable. As the attacks grow in complexity, the tools used to prevent them must grow too. Artificial Intelligence and Machine learning are vital in protecting businesses from cyber attacks. In fact, according to a survey of 850 senior executives from a variety of industries, “61% of enterprises say they cannot detect breach attempts today without the use of AI technologies”. Organizations of any size need to leverage these tools in order to protect themselves.

 

How does it work?

 

First, Learn

Using data from millions of sources, machine learning models can improve their ability to ‘understand’ cybersecurity threats and risks. Data from places like blogs, news stories, etc. is processed through machine learning algorithms and deep learning procedures to help the model create connections between items.

Then, Reason

After a model has ‘learned’ the data, it can create connections between threats like suspicious IP addresses or files. AI-driven threat analysis happens in minutes, which allows for a faster response time, which could save the company money in the long run.

Finally, Augment

The last step for using AI to prevent cybersecurity attacks is Augmentation, which is the process of using AI to analyze risks and generate a report. These reports can be used to make critical decisions, so having them quickly is imperative to eliminating a threat. It also saves cybersecurity professionals the time that the analysis would have traditionally spent compiling and analyzing this information by hand.

 

Why should we use it?

AI as a solution to these malicious cyber attacks is smarter, faster, and more cost-effective. AI was designed to be versatile and able to evolve over time, which makes it perfect for use in the cybersecurity field where things are constantly changing and improving. Machine Learning allows computers to learn from data and make connections that humans might have missed. It is far more adaptable than previous traditional technologies.

According to the previously mentioned survey by Capgemini, “56% of senior execs say their cybersecurity analysts are overwhelmed and close to a quarter (23%) are not able to successfully investigate all identified incidents”. One of the biggest issues that cybersecurity professionals face is ‘Alert Fatigue’ from the immense volume, variety, and velocity of data that is being thrown at them.

Using AI to detect malicious attacks promises to help reduce the amount of false positives that cybersecurity professionals have to investigate, which would speed up their ability to find real issues and fix them. The same survey mentions that “64% of the 850 senior executives that were interviewed said that AI lowers the cost to detect and respond to breaches and reduces the overall time taken to detect threats and breaches up to 12%”. So, not only is AI faster, it can also more cost-effective and connect disparate data better than using human resources or more traditional technologies.

 

Threat Prevention

AI is also being used to prevent threats before they happen. AI and Machine Learning algorithms can be taught to recognize imperfections in a system and create alerts to draw attention to the problem.

Malware

One of the best examples of threat prevention in practice is a software called Intercept X by Sophos. They have a software that uses AI to detect malware and ransomware and prevents it from being executed.

Phishing

Phishing is one of the most common cyber attacks. AI can be used to detect phishing emails and react to them in order to prevent any damage to the system. These reactions take much less time than it would take a human to do the same thing.

Managing Vulnerabilities

AI can be taught to proactively analyze a system for any known weaknesses and create alerts if it finds an imperfection in the system that could be exploited by a malicious attacker. Some of these models even incorporate chats found on the dark web to alert cybersecurity professionals of a potential attack.

Threat Remediation

AI is great on its own and it is being used in incredible ways to save cybersecurity professionals time, but right now a lot of the threat remediation has to be done by hand. AI is great at recognizing and preventing attacks, but if an attack does happen, AI is not quite advanced enough to resolve it by itself. Currently, AI and humans work together to protect our cybersecurity, however, not all hope is lost AI is in development that claims to ‘self-heal’ and take intelligent action to react to threats quickly on nights and weekends when cybersecurity professionals are away from the office.

 

Summary

The use of AI in cybersecurity is becoming a necessity. The complexity of new attacks requires a much faster detection and resolution rate than humans are capable of handling. Using AI is smarter, faster, more cost-effective, and allows cybersecurity professionals to be more efficient with their time than previous technologies did.

Please stay tuned for the follow up articles as we dive further into AI and its impact on cyber security.

https://blogs.query.ai/ai-cybersecurity-part-3-adversarial-ai

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Resources

https://www.capgemini.com/research/reinventing-cybersecurity-with-artificial-intelligence/

https://www.ibm.com/security/artificial-intelligence

https://www.wired.com/wiredinsider/2019/10/how-ai-battles-security-threats-without-humans/

 

Thanks for reading!

Alexis Vander Wilt

Written by Alexis Vander Wilt

I am a senior Computer Science and Mathematics student, with a passion for understanding Data Analysis and its impacts. I work as part of the team at Query.AI where we are using Natural Language Processing to allow users to “talk to your data” reducing the security learning curve and working to make security more accessible to all.