Why Fuzz Testing Is Crucial In AI And Machine Learning

Software testing

Today’s software development requires a high level of sensitivity, especially when it comes to writing safe codes. Cyberattacks are becoming more sophisticated, making it almost impossible to predict a hacker’s next step.

As a result, your software development team may need to continually consider all the potential attack modes to be used against your business. Including fuzz testing into your cybersecurity techniques may enable you to defend yourself against these attacks.

What Is Fuzz Testing

Fuzzing, also known as fuzz testing, is an automated software testing technique that involves feeding a computer program with random or invalid data. By intentionally feeding data to cause a program to crash, testers will be able to identify its defects or loops. This will allow them to determine the potential causes for its failure to manage the data.

From there, they can come up with rectifications for the problem and prevent the program from experiencing the same issue. For more information, you may check out this fuzz testing guide.

AI And Machine Learning

To understand the importance of fuzz testing in Artificial Intelligence (AI) and machine learning, you must first gain a basic understanding of both terms.

Remember that machine learning is under the umbrella of AI. This means that all machine learning is artificial intelligence. However, it’s important that you know that not all AI is machine learning.

Artificial Intelligence

Artificial Intelligence, in simplest words, are machines that humans programmed to be intelligent and perform different tasks that humans do. These tasks might include learning a new language, problem-solving, and even cooking.

AI has two different categories: Narrow AI and General AI. Narrow AI, which is also known as Weak AI, is the only type of AI that humans have successfully brought into life to date. Examples are devices that can perform tasks such as voice and facial recognition, drive a car, or even search the internet.

Although Narrow AI might seem intelligent, this type of AI can only perform specific tasks. It doesn’t replicate human intelligence but only simulates human behavior using a limited set of parameters and contexts.

General AI, or Strong AI, on the other hand, is the type of AI that can replicate human cognition and behavior. Unfortunately, scientists and AI researchers haven’t attained General AI yet. They’d have to figure out a way to make machines conscious and configure them with a complete range of cognitive abilities to succeed. They may need to level up machines’ experiential learning and improve their efficiency in performing a single task. They may also need to apply experiential knowledge to these machines so they can perform different tasks and solve various problems.

Machine Learning

Machine learning (ML) is simply a way of achieving AI. It’s a branch of artificial intelligence that enables computers to learn and develop independently without being specifically programmed. It mainly focuses on developing computer programs that can access data and learn on their own. It offers a variety of applications in different sectors that you might’ve already been using.

Machine learning is also known as a Narrow AI. Examples of machine learning are the digital voice assistants of those big search engine companies. These companies use advanced machine learning technologies to function. Its learning process starts with data, such as direct experience, and examples, or instruction, to search for patterns and make informed decisions in the future.

The primary goal for ML is for computers to learn without the need for human interference and to adapt their behavior accordingly. Thus, the more you interact with machine learning, the more data it can gather. This can help it learn to provide more accurate answers and perform the task efficiently.

Now that you already have an idea of what fuzz testing, Artificial Intelligence (AI), and Machine Learning are, continue reading below as this article talks about the importance of fuzz testing to AI.

Fuzz Testing In Artificial Intelligence And Machine Learning

Fuzz testing in AI and ML

Advanced deep learning technologies and a vast increase in investment have driven AI to bring new waves of technology and business models. As AI becomes more prevalent in computer systems, so does the need for high-level protection increases at all levels.

Since artificial intelligence requires programming computers or machines, they also need consistent fuzz testing to ensure that the answers they provide are accurate. AIs need to be tested before developers introduce them to the public, primarily for the reason they play in aiding humans in their everyday lives. Imagine a self-driving car not properly executing commands from its owner because of overwhelming data or bugs, which could’ve been avoided with fuzz testing. If a self-driving car’s system crash, its owners’ lives will be at risk.

AI’s security must also be a priority to secure the safety of information and privacy of users. The protection of businesses’ assets is dependent on the security of AI systems, their data, and communication. Cyberhackers might quickly attack and damage an AI’s program, especially if fuzz testing isn’t regularly performed.

The Power Of AI And Fuzz Testing (Fuzzing) Combined

Machine learning and related techniques are used in AI fuzzing (Artificial Intelligence and Fuzz testing) to identify bugs in an application or device. Fuzzing has been around for a while, but it isn’t easy to implement and hasn’t caught on with businesses. Thus, the addition of AI promises to make the tool more user-friendly and adaptable.

Before launching software or updates, companies can use fuzzing to fix any exploitable holes. Artificial intelligence has made the practice more available to software developers as it was previously complicated and frequently used in bug-hunting.

However, hackers can also use fuzzing to find vulnerabilities and narrow in on attack targets, especially if machine learning is used. That’s why experts highly recommend using AI fuzzing for companies to check their software. They also propose other techniques to find and patch vulnerabilities before cyberhackers can find them.

Final Words

Online security should be a top priority, especially for companies using the internet and its tools in their operations. Although AI and Machine learning seems impressive and powerful, they also need to be protected from cyberhackers and all possible bugs. If you own a company that operates primarily on computer programs, it’s a must to integrate fuzz testing as part of your cybersecurity measure to prevent hackers from seeing you as a target.