Starting Your AI Security Journey With Deep Learning

September 14, 2021
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3 min read

You’ve probably heard how using artificial intelligence (AI) can improve your cybersecurity — and how threat actors are using AI to launch attacks. You know that you need to use the same tools, if not better ones. AI security is about having the right tools, but also about having the right information.

But you aren’t sure where to start your AI security journey, and you aren’t alone. Many business or agency leaders are in the exact same place and have the same questions.

I’ve talked with many companies that are in various stages of the AI journey. And I’ve seen firsthand that, for many, deep learning is a good first step toward using AI-based security.

Is Deep Learning for Security the Same as AI Security?

Yes and no; deep learning is a type of machine learning. And yes, machine learning is a type of artificial intelligence. The goal of AI, whether that’s AI security or otherwise, is to mimic the human brain. When you read or watch information on a topic, you learn more about that subject. Then, you can better make logic-based decisions and predictions.

As a human, there is a limit to the amount, speed and complexity of the information you take in. But AI engines can process much higher volumes, at faster speeds. AI trains itself by consuming data from both structured and unstructured sources and then uses reason to make suggestions. And as quantum computing becomes more common in the coming years, AI will become even more powerful.

How Is Deep Learning Different From Machine Learning?

They are similar, but each type of AI is better suited for different tasks. So, they use different techniques for almost the same purpose. With classical machine learning, the AI algorithm must pre-process unstructured data to organize it into a structured format before using it. Deep learning processes unstructured data (e.g., text and images) just as it is. This makes its processing of unstructured data faster and more accurate.

Deep learning uses two unique methods to learn and reason: propagation and backpropagation. During the former, the deep learning algorithm moves through layers that build on each other. Because of this, the way it processes and predicts data is more accurate. Backpropagation relies on the algorithm to look for errors in predictions. Next, it uses that information to train the model more accurately.

Why Is Deep Learning a Good Option for Cybersecurity?

When using AI for cybersecurity, you must first train the model on the right topics and methods. Many of the sources needed in this field (such as message boards) are unstructured. So, deep learning allows you to collect more information on potential threats, which can then be used to predict potential risk. Without this unstructured data, AI algorithms are likely to be less accurate.

The next step is using what you’ve collected and the insights AI provides to spot potential threats, such as malicious files, suspicious IP addresses or employees performing actions that are potential insider attacks. However, AI cybersecurity is only effective if it predicts the threats right. While overlooking potential threats can be harmful, false positives force people to waste time that could be spent preventing actual threats. Propagation and backpropagation increase the AI’s accuracy  — and keep your data better protected.

How Do I Start Using Deep Learning for AI Security?

Your first step toward using AI security is to select a tool that uses AI to analyze and predict threats. When researching different tools, look for one that uses deep learning as its cornerstone. This will ensure it’s accurate and will help your team spend their energy on the highest risk threats. By using deep learning, you can decrease both your risk and the time spent on detecting threats, which allows your team to focus on work that needs the human touch.

Jennifer Gregory
Cybersecurity Writer

Jennifer Goforth Gregory is a freelance B2B technology content marketing writer specializing in cybersecurity. Other areas of focus include B2B, finance, tec...
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