The Rise of AI in Cybersecurity: How to Keep Your Organization Secure in the Digital Age

Introduction

As cyber threats continue to evolve and become increasingly sophisticated, organizations are turning to the power of artificial intelligence (AI) to stay ahead of the game. From machine learning algorithms that detect and prevent cyber-attacks, to AI-powered defensive strategies that protect sensitive data, the role of AI in cybersecurity is becoming increasingly vital.

One of the key benefits of AI in cybersecurity is its ability to minimize security breaches by augmenting current security teams, processes, and tools. Organizations do not have to rip apart their existing security infrastructure and replace it with AI-powered tools. Instead, AI can work in harmony with existing security teams and processes, providing a more comprehensive and effective defence against cyber threats.

In this article, we will delve into the three main domains where AI is revolutionizing the field of cybersecurity - defensive techniques, offensive techniques and AI-based systems. We will explore the various applications of AI in cybersecurity, such as threat detection and prevention, and examine how AI systems themselves can be vulnerable to cyber-attacks. By the end of this article, you will have a comprehensive understanding of the role of AI in strengthening cybersecurity efforts and its potential vulnerabilities.

Enhancing cyber defence strategies with AI

Artificial Intelligence can be utilized throughout the entire security process, from prevention to detection and response. Specifically, many of the AI techniques being applied in defensive security today are concentrated on identifying potential cyber-attacks, working primarily in the detection stage. To comprehend how AI improves the efficiency of these instruments, let's take a look at the example of a classic security detection mechanism, such as a spam filter.

A spam filter is a tool that aids in identifying whether an email is spam or not. The traditional structure for building a spam filter involves analysing the email's content, sender, and other characteristics.

In the traditional programming approach (Figure 1), a software engineer begins by analysing the problem and identifying the differences between legitimate and spam emails. Then, they create a set of static rules based on these differences. For example, an email written in more than two languages has a high probability of being spam, or if an email contains a specific list of words, it is likely to be spam.

The software engineer identifies all possible inputs and conditions that the system would be exposed to and creates a program that can handle those specific inputs and conditions. The expert then tests the solution and analyses any errors. If necessary, they would have to return to the original problem and understand where the differences between a good email and spam were incorrectly identified.

This traditional programming approach in cybersecurity has its limitations. One major constraint is that the set of rules created depends heavily on the ability of the expert to foresee the possible combinations and their ranges.

Additionally, these static rules are vulnerable to changes in the tactics used by attackers. If the program receives an input that it is not designed for, it will fail to handle that situation, leading to a tedious and complex process of constantly reviewing and updating the rules.

To address these limitations, many companies are turning to AI and machine learning (ML) to enhance their cybersecurity efforts. One way that ML is being used is to replace the static set of rules defined in traditional programming approaches.

Instead of manually creating a set of rules, the data science team first identifies training data, such as source code, log files, past emails, or program execution context. This data is then fed into a ML model that learns from the input data the patterns and features to classify new incoming emails as spam or not spam.

This approach separates the task of creating rules for classifying emails from the expert and, instead, infers the rules based on data. Then, if attackers change their methods, the algorithm can be retrained with new data, making the process faster and easily tailored for specific industries.

While there are pre-built solutions available in the market, they often have a one-size-fits-all approach and may not be tailored to the specific needs of a particular industry. By using ML, companies can create a customized solution that adapts to the unique characteristics of their industry.

The role of AI in advancing offensive security strategies

In the previous section, we examined how implementing AI can improve the cybersecurity of an organization. Now, let's examine the other side of the coin: the ways in which AI and ML are enabling cybercriminals to carry out security attacks with greater speed, scope, and stealth. The same technologies and techniques you rely on to protect your organization are just as easily accessible to your adversaries.

AI is becoming a powerful tool in the hands of attackers. There are many tools available to automate common tasks such as scanning networks and discovering services, as well as a variety of offensive techniques that attackers use, such as social engineering.

Social engineering is a form of psychological manipulation that is often used in cybersecurity attacks. It involves tricking individuals into revealing sensitive information or performing actions that may compromise their own security or the security of their organization. Social engineering attacks can take many forms, including phishing scams, pretexting, baiting, and scareware.

One common example of social engineering is phishing, which involves sending fraudulent emails or messages that appear to be from a legitimate source and request sensitive information, such as login credentials or financial information.

With the development of large natural language processing models like GPT-3 and ChatGPT, it is becoming increasingly easier for cybercriminals to create more sophisticated phishing emails.

ChatGPT is a NLP model recently launched by OpenAI that is capable of generating human-like text based on a given prompt.

Attackers often rely on formats or templates when launching their campaigns. Defence systems that rely on static indicators, including text strings, would be impacted by a shift to more unique content in phishing emails.

As an example, we gave ChatGPT a format email commonly used in payroll diversion phishing email attacks and asked it to write three new variations. ChatGPT generated the list in less than a minute. As you can see in Figure 3, they are all considerably unique from one another.

ChatGPT was not developed for this kind of use, but as we can see in this simple example, it could allow scammers to craft unique content based on an initial format, making detection based on known malicious text string matches much more difficult.

ChatGPT: Write 3 new variations of the following paragraph: “I have moved last week, and I have changed my bank account as well. What details do I provide to update my direct deposit information on record? Also, can it be effective for my next payroll? Looking forward to hearing from you.”

If that was not enough, there is a brand-new category of attacks made possible in recent years due to advancements in Deep Learning, such as CEO fraud.

This type of attack is often designed to trick employees into divulging sensitive information or transferring money to the attacker's account. For example, an attacker may send an email claiming to be from the CEO and requesting that an employee transfer money to a specific account. The attacker may use fake invoices, purchase orders, or other documents to support their request and make it appear legitimate.

CEO fraud attacks can be difficult to detect because they often use logos and branding that appear legitimate, and they may use domain names that are like those of the real organization.

Because there is much more awareness among employees, this cyber crime had been greatly reduced. However, with the implementation of AI techniques, this cybercrime is back in a more sophisticated way. Attackers now can generate synthetic images, audio, and video. With synthetic data, an attacker can impersonate another person or even create a false information record.

This AI technology has been used to improve the CEO fraud technique. An attacker can use videos from senior managers to create a montage, getting the email of an employee to call them via an online meeting, emulate the face and voice of the senior manager in real-time and asking the employee to perform an urgent task that usually involves money transfer.

New breed of cyber-attacks: targeting ML algorithms and models

So far, we have reviewed how AI is being applied to leverage cybersecurity strategies. Now, let's delve deeper into the topic of cybersecurity in AI-deployed systems.

We are not just talking about the well-known attacks such as buffer overflow, denial of service, man-in-the-middle, or phishing attacks. Instead, we are discussing a new breed of attacks on the machine learning algorithms and models that are the building blocks of AI solutions.

By creating an AI-based system, we've also created a new type of attack surface for the security environment and opened up new ways for malicious actors to exploit. In the field of machine learning, there is a subfield called Adversarial Machine Learning that specifically studies these types of attacks and defences against them.

Adversarial Machine Learning focuses on developing techniques to defend against malicious attacks on machine learning models. These attacks can take many forms, such as feeding a model maliciously crafted input data to cause it to make incorrect predictions or manipulating the model's parameters to cause it to behave in unintended ways.

In one of the most relevant papers on the topic, Goodfellow et al. explores the concept of adversarial examples and how they can be used to fool machine learning models. The authors explain that adversarial examples are inputs to a model that are deliberately constructed to cause the model to make a mistake. They show that these examples can be easily generated for many types of models, and that they can be used to fool state-of-the-art image classifiers with high confidence.

In their work, the authors used GoogLeNet, a CNN with 22 layers that is trained on ImageNet to classify images into 1000 object categories. They worked with a pretrained version of this network that was trained to identify animal’s pictures and asked the questions: how can we attack this system? How can we make this system make a wrong prediction that is not visibly apparent to the human eye?

They took a picture of a panda bear and added an imperceptibly small vector, or a layer of noise. As seen in Figure 4, after adding this noise, there is no visible change to the output image. However, for the AI-based system, the addition of that layer of noise causes the internal rules generated by the CNN to not work well. As a result, after applying this layer of noise, the system incorrectly classified the image of the panda bear as another animal with 99%.

Classifying the image of a panda bear incorrectly might not seem like a big deal, but AI systems based on computer vision are being deployed in many fields, such as traffic management in cities, object detection systems, and so on. Thus, a cyber-attack on these algorithms could have devastating consequences.?

Getting your organization ready for AI in cybersecurity

As a leading consultancy firm in the field of AI and data, we understand the importance of preparing your organization for the integration of AI in cybersecurity. With the rapid advancements in technology, it's crucial for companies to stay ahead of the curve and protect their assets from cyber threats.

It is important to understand that implementing AI in cybersecurity is not a one-time task, but rather a continuous process of learning, testing, and improving.

So, what if your organization has limited or no prior experience with implementing AI? How do you even get started?

The first step in getting your security organization ready for AI is to assess your current cybersecurity infrastructure and identify any areas that can be improved with the integration of AI. This includes identifying potential vulnerabilities and areas where AI can help to automate and enhance your security capabilities.

Next, it's important to establish a clear plan and roadmap for implementation. This includes identifying specific use cases for AI in cybersecurity, such as automated threat detection and response, and identifying the necessary resources and budget for implementation.

It's also crucial to ensure that your team has the necessary skills and training to effectively implement and manage AI in cybersecurity. This may include training in data science and machine learning, as well as hands-on experience with AI platforms and tools.

In summary, getting your security organization ready for AI requires a clear understanding of the basics of AI, a thorough assessment of your current systems and processes, and a well-designed plan for integration. With the right guidance and preparation, your organization can harness the power of AI to improve your cybersecurity and protect against cyber-attacks.

At AC Consulting , we offer a wide range of services to help organizations prepare for AI in cybersecurity. From assessment and planning to implementation and training, we have the expertise and resources to help you navigate the complexities of AI and ensure that your organization is protected from cyber threats.

If you're ready to take the next step in preparing your organization for AI, contact us today to learn more about our services and how we can help you get started.