How AI Is Used in Cybersecurity in 2024

Table of Contents

• How AI Is Used in Cybersecurity
• 3 AI Tools for Cybersecurity
• Employer + Certification Trends
• How to Learn AI at Fullstack Academy

As artificial intelligence evolves, it’s being incorporated into powerful tools used for cybersecurity. Two experts in the field from Fullstack Academy walk us through the ways AI is being used in cybersecurity in 2024, and the three AI tools that cyber professionals are now integrating into their processes. For those interested in diving deeper into AI, find out how the six-unit AI & Machine Learning Bootcamp at Fullstack Academy is preparing students for in-demand tech roles!

Meet Our Experts: 

• Kristin Whalen is the Associate Director of Academics and Product at Fullstack Academy. Kristin ensures that both the curriculum and instructors are supported throughout the bootcamp experience. She manages curriculum changes and optimization, receives curriculum feedback, and supports the managers who oversee and coach the instructional team.
• Seraphina Courtney is a Cybersecurity Instructor at Fullstack Academy and AI researcher at the University of New Orleans, delivering daily lectures and encouraging class discussions, as well as offering 1:1 office hours and extra learning time with students on an individual and case-by-case basis.

How AI Is Used in Cybersecurity

Seraphina: In general, artificial intelligence is an aid to cybersecurity analysts in detecting threats and identifying patterns and unusual behavior that come up. AI is really good at processing very large data sets and pulling out things that a human staring at millions of lines of log files or code might not necessarily see. AI can parse through all of that data and bring any patterns to the attention of the security analysts. The analysts can then give it a further review to see if what the AI has found is actually a security anomaly or expected behavior.

Will cybersecurity eventually be “automated away” by these new AI security tools? 

Seraphina: The short answer is no, I don't believe any of these jobs are going to be automated away. Large language models like ChatGPT have really brought AI to the forefront of the social consciousness and amplified this fear of jobs being automated away. Instead, AI will continue to be a tool to assist cybersecurity analysts in their jobs to be more effective at what they're doing.

3 AI Tools for Cybersecurity

Seraphina: While new AI tools are coming out in tech, right now in cybersecurity, AI and machine learning techniques are primarily being incorporated into existing products: 

1. A big example of that is what's called “next generation firewalls” that are being produced by companies like Cisco and Palo Alto. Instead of having a strict set of rules that these firewalls work on, they watch the data that's coming through to look for patterns and recognition, and adapt themselves autonomously to what might be malicious traffic. Essentially, they make up their own rules and alerts based off of that. 
2. AI is also being used in security information and event management systems. A good example of that is Splunk which has an AI component to it that can dig through log files to find these complex patterns that an engineer may overlook if they're having to dig through millions of logs by themselves.
3. AI is now being used in antivirus software, where historically we've used signatures to mark known bad files or malicious files. There are a lot of sophisticated techniques used in trying to identify malicious files. Instead of just working on those signatures, AI is also looking at the code and design patterns of how the software is created and what it's doing to determine if a piece of software is potentially malicious.

At this time, are AI tools being used by all levels of cybersecurity professionals?

Seraphina: The tools we're using now are still the tools we have been using. AI is on the back end of this, presenting data to us in more efficient ways. At a super high level, an entry-level cyber professional doesn't need to know in-depth how AI is working. They need to be aware of AI and its limitations, like knowing that the AI model may be hallucinating and coming up with false positives and not just blindly trusting what an AI has told them.

Employer + Certification Trends

Do you foresee any of the cybersecurity certifications being updated in the next year to meet those new AI demands?

Seraphina: I don’t believe that a CompTIA type of exam is going to specifically require in-depth knowledge of AI systems. As a security analyst, one of your biggest jobs is learning and being able to work with new systems. A security analyst doesn't need to know the ins-and-outs of machine learning and how to write a neural network from scratch or anything like that. What's important for us is to be able to work with these systems that have been created by software developers. In the next year, I don't think CompTIA or anyone's going to be requiring much in the way of extra AI knowledge, but knowing the concepts of what all these tools are doing already can help. We're doing the same tasks, but AI is making it more efficient for us.

In 2024, will employers expect their new cyber and IT hires to know AI tools?

Kristin: As this concept comes to the forefront of social consciousness, I foresee generative AI prompt engineering being a useful tool for any employers, not just in the tech field. Employers are seeing how this can be applied, used, and leveraged as a tool that their employees can use. Exploring or understanding generative AI and prompt engineering can be helpful. It's here for better or worse. It comes in various forms, and I think employers are really looking for people who can work with this toolset and learning tools that they need to do their job successfully, whether those are AI tools or different tools. New hires should be expected to work with systems that utilize AI, but they really need to be comfortable with the idea of continuous learning. This year it’s AI tools but it’ll always be something. Being comfortable with continuous and lifelong learning is the skill that employers are going to continue to look for. 

Seraphina: I echo everything Kristin said. Especially in cybersecurity and most IT fields, the idea of being a continuous lifelong learner is the driving force behind this type of career. Along those lines, not every security analyst needs to know how to write a neural network from scratch, but they should be comfortable working with tools that are using this technology.

How to Learn AI at Fullstack Academy

Kristin, what do students learn in Fullstack Academy’s AI/ML Bootcamp? 

The AI/Machine Learning Bootcamp consists of six units: 

• Unit 1: Statistics Essentials for Data Science. Having statistical knowledge is essential in this profession, so students begin by learning about probability, sampling techniques, data visualizations, relationships between variables, and more! In this unit, we work on real-world projects, such as analyzing customer satisfaction survey data and analyzing the relationship between stocks. 
• Unit 2: Programming Basics. Python is the primary coding language used in AI and machine learning. It’s a great language for beginners because it's in native English. Students learn everything from conditionals, variables, loops, functions, and threading. By the end of this unit, they're creating a back end script for a shopping app!
• Unit 3: Applied Data Science with Python. This unit is a deep dive into data science processes like data wrangling, data exploration, fundamentals, advanced statistics, hypothesis building, and testing. This unit is great because the concepts are helpful no matter what career you end up in because being able to prepare, analyze, and interpret data using Python can help you in a lot of tech jobs! For a project, students perform data analysis on fictional sales data for a cutting-edge company.
• Unit 4: Machine Learning. Students explore supervised and unsupervised learning with regression models, classification, its applications, and recommender systems. For their project, students create a model to predict employee turnover as well as suggest various retention strategies based on the findings.
• Unit 5: Deep Learning. Students explore artificial and deep neural networks, learn how to use Keras and TensorFlow, evaluate optimization algorithms, implement a neural network using PyTorch, and a lot more! The project for this unit is developing a deep learning model that can predict the probability of a loan applicant defaulting on their home loan.
• Unit 6: Generative AI & Prompt Engineering. In this unit students explore ChatGPT, prompt engineering and more. Students also learn how to effectively and ethically utilize these tools in a variety of business applications.

Does Fullstack Academy cover AI tools and skills in its other bootcamps like Full-Time Coding Bootcamp?

Kristin: Not at this time. However, it is something we have been keeping an eye on as trends evolve in the industry where AI and machine learning are being used in a variety of tech fields. Currently, it is taught exclusively in our AI & Machine Learning Bootcamp. 

What is the teaching style like in the 26-week AI/ML bootcamp? 

Seraphina: A typical day starts with a lecture and class discussion about a daily topic. Students then have in-class labs that they can work on together. Myself and other instructional staff are around to assist and fill in any blanks or questions that kind of come up during that period. There is also some asynchronous material that students are expected to have completed between each class session. 

What types of tech jobs do AI/ML and Cybersecurity bootcamps prepare students for?

Kristin: When developing the AI/ML bootcamp, we based it on preparing students for the following roles:

• Data analyst roles, such as business analysts or data intelligence roles
• Artificial intelligence roles, such as artificial intelligence engineers, data scientists, natural language processing scientists, and general computer scientists
• Machine learning engineers in data scientist roles, such as predictive analytics and computer vision engineers

Serafina: With the Cybersecurity Bootcamp, Fullstack Academy is preparing students for a job in a security operation center (SOC), especially as a SOC analyst. Cybersecurity job titles are still in flux because of the nature of this industry. “Security Analyst” comes up a lot. We're preparing students for foundational security roles. Once you're in a SOC or working in a similar company, you’ll continue to build on what we've taught you and keep moving forward into a specialization.

What is your advice to incoming Fullstack Academy students who are interested in learning AI for their tech field? How can they make the most of their bootcamp experience?

Seraphina: 

• Be excited! Keep that enthusiasm and passion for learning new and interesting things. 
• Don't be afraid to explore and get out there and break things in a controlled manner!
• Try working with these new technologies. 
• Don't be afraid of what you don't know.

Kristin:

• If you're interested in a tech field outside of AI, but you want to incorporate AI tools, you can make the most of your bootcamp by reflecting on lessons and how you can apply AI to each of those for whatever field that you're interested in. 
• Ask good questions like, What problems in this specific field can AI help me solve? or How can AI help me operate more efficiently in this tech field?
• Thinking about ethics is a good thing for our students to consider. What are those implications regarding AI in the context of the field that they want to get into?
• Everything that you're doing during your bootcamp is really preparing you to work with these tools. You just have to be committed to that lifelong learning and continually exploring and trying new tools that are available to you. 

Find out more and read Fullstack Academy reviews on Course Report. This article was produced by the Course Report team in partnership with Fullstack Academy.