AI investment has become a must in the business world, and companies from all over the globe are embracing this trend. Nearly 90% of organizations plan to put more money into AI by 2025.

One of the main areas of investment is deep learning. The World Economic Forum approves of this initiative, as the cutting-edge technology can boost productivity, optimize cybersecurity, and enhance decision-making.

Knowing that deep learning is making waves is great, but it doesn’t mean much if you don’t understand the basics. Read on for deep learning applications and the most common examples.

Artificial Neural Networks

Once you scratch the surface of deep learning, you’ll see that it’s underpinned by artificial neural networks. That’s why many people refer to deep learning as deep neural networking and deep neural learning.

There are different types of artificial neural networks.

Perceptron

Perceptrons are the most basic form of neural networks. These artificial neurons were originally used for calculating business intelligence or input data capabilities. Nowadays, it’s a linear algorithm that supervises the learning of binary classifiers.

Convolutional Neural Networks

Convolutional neural network machine learning is another common type of deep learning network. It combines input data with learned features before allowing this architecture to analyze images or other 2D data.

The most significant benefit of convolutional neural networks is that they automate feature extraction. As a result, you don’t have to recognize features on your own when classifying pictures or other visuals – the networks extract them directly from the source.

Recurrent Neural Networks

Recurrent neural networks use time series or sequential information. You can find them in many areas, such as natural language processing, image captioning, and language translation. Google Translate, Siri, and many other applications have adopted this technology.

Generative Adversarial Networks

Generative adversarial networks are architecture with two sub-types. The generator model produces new examples, whereas the discriminated model determines if the examples generated are real or fake.

These networks work like so-called game theory scenarios, where generator networks come face-to-face with their adversaries. They generate examples directly, while the adversary (discriminator) tries to tell the difference between these examples and those obtained from training information.

Deep Learning Applications

Deep learning helps take a multitude of technologies to a whole new level.

Computer Vision

The feature that allows computers to obtain useful data from videos and pictures is known as computer vision. An already sophisticated process, deep learning can enhance the technology further.

For instance, you can utilize deep learning to enable machines to understand visuals like humans. They can be trained to automatically filter adult content to make it child-friendly. Likewise, deep learning can enable computers to recognize critical image information, such as logos and food brands.

Natural Language Processing

Artificial intelligence deep learning algorithms spearhead the development and optimization of natural language processing. They automate various processes and platforms, including virtual agents, the analysis of business documents, key phrase indexing, and article summarization.

Speech Recognition

Human speech differs greatly in language, accent, tone, and other key characteristics. This doesn’t stop deep learning from polishing speech recognition software. For instance, Siri is a deep learning-based virtual assistant that can automatically make and recognize calls. Other deep learning programs can transcribe meeting recordings and translate movies to reach wider audiences.

Robotics

Robots are invented to simplify certain tasks (i.e., reduce human input). Deep learning models are perfect for this purpose, as they help manufacturers build advanced robots that replicate human activity. These machines receive timely updates to plan their movements and overcome any obstacles on their way. That’s why they’re common in warehouses, healthcare centers, and manufacturing facilities.

Some of the most famous deep learning-enabled robots are those produced by Boston Dynamics. For example, their robot Atlas is highly agile due to its deep learning architecture. It can move seamlessly and perform dynamic interactions that are common in people.

Autonomous Driving

Self-driving cars are all the rage these days. The autonomous driving industry is expected to generate over $300 billion in revenue by 2035, and most of the credits will go to deep learning.

The producers of these vehicles use deep learning to train cars to respond to real-life traffic scenarios and improve safety. They incorporate different technologies that allow cars to calculate the distance to the nearest objects and navigate crowded streets. The vehicles come with ultra-sensitive cameras and sensors, all of which are powered by deep learning.

Passengers aren’t the only group who will benefit from deep learning-supported self-driving cars. The technology is expected to revolutionize emergency and food delivery services as well.

Deep Learning Algorithms

Numerous deep learning algorithms power the above technologies. Here are the four most common examples.

Backpropagation

Backpropagation is commonly used in neural network training. It starts from so-called “forward propagation,” analyzing its error rate. It feeds the error backward through various network layers, allowing you to optimize the weights (parameters that transform input data within hidden layers).

Stochastic Gradient Descent

The primary purpose of the stochastic gradient descent algorithm is to locate the parameters that allow other machine learning algorithms to operate at their peak efficiency. It’s generally combined with other algorithms, such as backpropagation, to enhance neural network training.

Reinforcement Learning

The reinforcement learning algorithm is trained to resolve multi-layer problems. It experiments with different solutions until it finds the right one. This method draws its decisions from real-life situations.

The reason it’s called reinforcement learning is that it operates on a reward/penalty basis. It aims to maximize rewards to reinforce further training.

Transfer Learning

Transfer learning boils down to recycling pre-configured models to solve new issues. The algorithm uses previously obtained knowledge to make generalizations when facing another problem.

For instance, many deep learning experts use transfer learning to train the system to recognize images. A classifier can use this algorithm to identify pictures of trucks if it’s already analyzed car photos.

Deep Learning Tools

Deep learning tools are platforms that enable you to develop software that lets machines mimic human activity by processing information carefully before making a decision. You can choose from a wide range of such tools.

TensorFlow

Developed in CUDA and C++, TensorFlow is a highly advanced deep learning tool. Google launched this open-source solution to facilitate various deep learning platforms.

Despite being advanced, it can also be used by beginners due to its relatively straightforward interface. It’s perfect for creating cloud, desktop, and mobile machine learning models.

Keras

The Keras API is a Python-based tool with several features for solving machine learning issues. It works with TensorFlow, Thenao, and other tools to optimize your deep learning environment and create robust models.

In most cases, prototyping with Keras is fast and scalable. The API is compatible with convolutional and recurrent networks.

PyTorch

PyTorch is another Python-based tool. It’s also a machine learning library and scripting language that allows you to create neural networks through sophisticated algorithms. You can use the tool on virtually any cloud software, and it delivers distributed training to speed up peer-to-peer updates.

Caffe

Caffe’s framework was launched by Berkeley as an open-source platform. It features an expressive design, which is perfect for propagating cutting-edge applications. Startups, academic institutions, and industries are just some environments where this tool is common.

Theano

Python makes yet another appearance in deep learning tools. Here, it powers Theano, enabling the tool to assess complex mathematical tasks. The software can solve issues that require tremendous computing power and vast quantities of information.

Deep Learning Examples

Deep learning is the go-to solution for creating and maintaining the following technologies.

Image Recognition

Image recognition programs are systems that can recognize specific items, people, or activities in digital photos. Deep learning is the method that enables this functionality. The most well-known example of the use of deep learning for image recognition is in healthcare settings. Radiologists and other professionals can rely on it to analyze and evaluate large numbers of images faster.

Text Generation

There are several subtypes of natural language processing, including text generation. Underpinned by deep learning, it leverages AI to produce different text forms. Examples include machine translations and automatic summarizations.

Self-Driving Cars

As previously mentioned, deep learning is largely responsible for the development of self-driving cars. AutoX might be the most renowned manufacturer of these vehicles.

The Future Lies in Deep Learning

Many up-and-coming technologies will be based on deep learning AI. It’s no surprise, therefore, that nearly 50% of enterprises already use deep learning as the driving force of their products and services. If you want to expand your knowledge about this topic, consider taking a deep learning course. You’ll improve your employment opportunities and further demystify the concept.

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Cyber Threat Landscape 2024: Human-Centric Cyber Threats
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

Human-centric cyber threats have long posed a serious issue for organizations. After all, humans are often the weakest link in the cybersecurity chain. Unfortunately, when artificial intelligence came into the mix, it only made these threats even more dangerous.

So, what can be done about these cyber threats now?

That’s precisely what we asked Tom Vazdar, the chair of the Enterprise Cybersecurity Master’s program at the Open Institute of Technology (OPIT), and Venicia Solomons, aka the “Cyber Queen.”

They dedicated a significant portion of their “Cyber Threat Landscape 2024: Navigating New Risks” master class to AI-powered human-centric cyber threats. So, let’s see what these two experts have to say on the topic.

Human-Centric Cyber Threats 101

Before exploring how AI impacted human-centric cyber threats, let’s go back to the basics. What are human-centric cyber threats?

As you might conclude from the name, human-centric cyber threats are cybersecurity risks that exploit human behavior or vulnerabilities (e.g., fear). Even if you haven’t heard of the term “human-centric cyber threats,” you’ve probably heard of (or even experienced) the threats themselves.

The most common of these threats are phishing attacks, which rely on deceptive emails to trick users into revealing confidential information (or clicking on malicious links). The result? Stolen credentials, ransomware infections, and general IT chaos.

How Has AI Impacted Human-Centric Cyber Threats?

AI has infiltrated virtually every cybersecurity sector. Social engineering is no different.

As mentioned, AI has made human-centric cyber threats substantially more dangerous. How? By making them difficult to spot.

In Venicia’s words, AI has allowed “a more personalized and convincing social engineering attack.”

In terms of email phishing, malicious actors use AI to write “beautifully crafted emails,” as Tom puts it. These emails contain no grammatical errors and can mimic the sender’s writing style, making them appear more legitimate and harder to identify as fraudulent.

These highly targeted AI-powered phishing emails are no longer considered “regular” phishing attacks but spear phishing emails, which are significantly more likely to fool their targets.

Unfortunately, it doesn’t stop there.

As AI technology advances, its capabilities go far beyond crafting a simple email. Venicia warns that AI-powered voice technology can even create convincing voice messages or phone calls that sound exactly like a trusted individual, such as a colleague, supervisor, or even the CEO of the company. Obey the instructions from these phone calls, and you’ll likely put your organization in harm’s way.

How to Counter AI-Powered Human-Centric Cyber Threats

Given how advanced human-centric cyber threats have gotten, one logical question arises – how can organizations counter them? Luckily, there are several ways to do this. Some rely on technology to detect and mitigate threats. However, most of them strive to correct what caused the issue in the first place – human behavior.

Enhancing Email Security Measures

The first step in countering the most common human-centric cyber threats is a given for everyone, from individuals to organizations. You must enhance your email security measures.

Tom provides a brief overview of how you can do this.

No. 1 – you need a reliable filtering solution. For Gmail users, there’s already one such solution in place.

No. 2 – organizations should take full advantage of phishing filters. Before, only spam filters existed, so this is a major upgrade in email security.

And No. 3 – you should consider implementing DMARC (Domain-based Message Authentication, Reporting, and Conformance) to prevent email spoofing and phishing attacks.

Keeping Up With System Updates

Another “technical” move you can make to counter AI-powered human-centric cyber threats is to ensure all your systems are regularly updated. Fail to keep up with software updates and patches, and you’re looking at a strong possibility of facing zero-day attacks. Zero-day attacks are particularly dangerous because they exploit vulnerabilities that are unknown to the software vendor, making them difficult to defend against.

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Nurturing a Culture of Skepticism

The key component of the human-centric cyber threats is, in fact, humans. That’s why they should also be the key component in countering these threats.

At an organizational level, numerous steps are needed to minimize the risks of employees falling for these threats. But it all starts with what Tom refers to as a “culture of skepticism.”

Employees should constantly be suspicious of any unsolicited emails, messages, or requests for sensitive information.

They should always ask themselves – who is sending this, and why are they doing so?

This is especially important if the correspondence comes from a seemingly trusted source. As Tom puts it, “Don’t click immediately on a link that somebody sent you because you are familiar with the name.” He labels this as the “Rule No. 1” of cybersecurity awareness.

Growing the Cybersecurity Culture

The ultra-specific culture of skepticism will help create a more security-conscious workforce. But it’s far from enough to make a fundamental change in how employees perceive (and respond to) threats. For that, you need a strong cybersecurity culture.

Tom links this culture to the corporate culture. The organization’s mission, vision, statement of purpose, and values that shape the corporate culture should also be applicable to cybersecurity. Of course, this isn’t something companies can do overnight. They must grow and nurture this culture if they are to see any meaningful results.

According to Tom, it will probably take at least 18 months before these results start to show.

During this time, organizations must work on strengthening the relationships between every department, focusing on the human resources and security sectors. These two sectors should be the ones to primarily grow the cybersecurity culture within the company, as they’re well versed in the two pillars of this culture – human behavior and cybersecurity.

However, this strong interdepartmental relationship is important for another reason.

As Tom puts it, “[As humans], we cannot do anything by ourselves. But as a collective, with the help within the organization, we can.”

Staying Educated

The world of AI and cybersecurity have one thing in common – they never sleep. The only way to keep up with these ever-evolving worlds is to stay educated.

The best practice would be to gain a solid base by completing a comprehensive program, such as OPIT’s Enterprise Cybersecurity Master’s program. Then, it’s all about continuously learning about new developments, trends, and threats in AI and cybersecurity.

Conducting Regular Training

For most people, it’s not enough to just explain how human-centric cyber threats work. They must see them in action. Especially since many people believe that phishing attacks won’t happen to them or, if they do, they simply won’t fall for them. Unfortunately, neither of these are true.

Approximately 3.4 billion phishing emails are sent each day, and millions of them successfully bypass all email authentication methods. With such high figures, developing critical thinking among the employees is the No. 1 priority. After all, humans are the first line of defense against cyber threats.

But humans must be properly trained to counter these cyber threats. This training includes the organization’s security department sending fake phishing emails to employees to test their vigilance. Venicia calls employees who fall for these emails “clickers” and adds that no one wants to be a clicker. So, they do everything in their power to avoid falling for similar attacks in the future.

However, the key to successful employee training in this area also involves avoiding sending similar fake emails. If the company keeps trying to trick the employees in the same way, they’ll likely become desensitized and less likely to take real threats seriously.

So, Tom proposes including gamification in the training. This way, the training can be more engaging and interactive, encouraging employees to actively participate and learn. Interestingly, AI can be a powerful ally here, helping create realistic scenarios and personalized learning experiences based on employee responses.

Following in the Competitors’ Footsteps

When it comes to cybersecurity, it’s crucial to be proactive rather than reactive. Even if an organization hasn’t had issues with cyberattacks, it doesn’t mean it will stay this way. So, the best course of action is to monitor what competitors are doing in this field.

However, organizations shouldn’t stop with their competitors. They should also study other real-world social engineering incidents that might give them valuable insights into the tactics used by the malicious actors.

Tom advises visiting the many open-source databases reporting on these incidents and using the data to build an internal educational program. This gives organizations a chance to learn from other people’s mistakes and potentially prevent those mistakes from happening within their ecosystem.

Stay Vigilant

It’s perfectly natural for humans to feel curiosity when it comes to new information, anxiety regarding urgent-looking emails, and trust when seeing a familiar name pop up on the screen. But in the world of cybersecurity, these basic human emotions can cause a lot of trouble. That is, at least, when humans act on them.

So, organizations must work on correcting human behaviors, not suppressing basic human emotions. By doing so, they can help employees develop a more critical mindset when interacting with digital communications. The result? A cyber-aware workforce that’s well-equipped to recognize and respond to phishing attacks and other cyber threats appropriately.

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Cyber Threat Landscape 2024: The AI Revolution in Cybersecurity
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

There’s no doubt about it – artificial intelligence has revolutionized almost every aspect of modern life. Healthcare, finance, and manufacturing are just some of the sectors that have been virtually turned upside down by this powerful new force. Cybersecurity also ranks high on this list.

But as much as AI can benefit cybersecurity, it also presents new challenges. Or – to be more direct –new threats.

To understand just how serious these threats are, we’ve enlisted the help of two prominent figures in the cybersecurity world – Tom Vazdar and Venicia Solomons. Tom is the chair of the Master’s Degree in Enterprise Cybersecurity program at the Open Institute of Technology (OPIT). Venicia, better known as the “Cyber Queen,” runs a widely successful cybersecurity community looking to empower women to succeed in the industry.

Together, they held a master class titled “Cyber Threat Landscape 2024: Navigating New Risks.” In this article, you get the chance to hear all about the double-edged sword that is AI in cybersecurity.

How Can Organizations Benefit From Using AI in Cybersecurity?

As with any new invention, AI has primarily been developed to benefit people. In the case of AI, this mainly refers to enhancing efficiency, accuracy, and automation in tasks that would be challenging or impossible for people to perform alone.

However, as AI technology evolves, its potential for both positive and negative impacts becomes more apparent.

But just because the ugly side of AI has started to rear its head more dramatically, it doesn’t mean we should abandon the technology altogether. The key, according to Venicia, is in finding a balance. And according to Tom, this balance lies in treating AI the same way you would cybersecurity in general.

Keep reading to learn what this means.

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Implement a Governance Framework

In cybersecurity, there is a governance framework called ISO/IEC 27000, whose goal is to provide a systematic approach to managing sensitive company information, ensuring it remains secure. A similar framework has recently been created for AI— ISO/IEC 42001.

Now, the trouble lies in the fact that many organizations “don’t even have cybersecurity, not to speak artificial intelligence,” as Tom puts it. But the truth is that they need both if they want to have a chance at managing the risks and complexities associated with AI technology, thus only reaping its benefits.

Implement an Oversight Mechanism

Fearing the risks of AI in cybersecurity, many organizations chose to forbid the usage of this technology outright within their operations. But by doing so, they also miss out on the significant benefits AI can offer in enhancing cybersecurity defenses.

So, an all-out ban on AI isn’t a solution. A well-thought-out oversight mechanism is.

According to Tom, this control framework should dictate how and when an organization uses cybersecurity and AI and when these two fields are to come in contact. It should also answer the questions of how an organization governs AI and ensures transparency.

With both of these frameworks (governance and oversight), it’s not enough to simply implement new mechanisms. Employees should also be educated and regularly trained to uphold the principles outlined in these frameworks.

Control the AI (Not the Other Way Around!)

When it comes to relying on AI, one principle should be every organization’s guiding light. Control the AI; don’t let the AI control you.

Of course, this includes controlling how the company’s employees use AI when interacting with client data, business secrets, and other sensitive information.

Now, the thing is – people don’t like to be controlled.

But without control, things can go off the rails pretty quickly.

Tom gives just one example of this. In 2022, an improperly trained (and controlled) chatbot gave an Air Canada customer inaccurate information and a non-existing discount. As a result, the customer bought a full-price ticket. A lawsuit ensued, and in 2024, the court ruled in the customer’s favor, ordering Air Canada to pay compensation.

This case alone illustrates one thing perfectly – you must have your AI systems under control. Tom hypothesizes that the system was probably affordable and easy to implement, but it eventually cost Air Canada dearly in terms of financial and reputational damage.

How Can Organizations Protect Themselves Against AI-Driven Cyberthreats?

With well-thought-out measures in place, organizations can reap the full benefits of AI in cybersecurity without worrying about the threats. But this doesn’t make the threats disappear. Even worse, these threats are only going to get better at outsmarting the organization’s defenses.

So, what can the organizations do about these threats?

Here’s what Tom and Venicia suggest.

Fight Fire With Fire

So, AI is potentially attacking your organization’s security systems? If so, use AI to defend them. Implement your own AI-enhanced threat detection systems.

But beware – this isn’t a one-and-done solution. Tom emphasizes the importance of staying current with the latest cybersecurity threats. More importantly – make sure your systems are up to date with them.

Also, never rely on a single control system. According to our experts, “layered security measures” are the way to go.

Never Stop Learning (and Training)

When it comes to AI in cybersecurity, continuous learning and training are of utmost importance – learning for your employees and training for the AI models. It’s the only way to ensure all system aspects function properly and your employees know how to use each and every one of them.

This approach should also alleviate one of the biggest concerns regarding an increasing AI implementation. Namely, employees fear that they will lose their jobs due to AI. But the truth is, the AI systems need them just as much as they need those systems.

As Tom puts it, “You need to train the AI system so it can protect you.”

That’s why studying to be a cybersecurity professional is a smart career move.

However, you’ll want to find a program that understands the importance of AI in cybersecurity and equips you to handle it properly. Get a master’s degree in Enterprise Security from OPIT, and that’s exactly what you’ll get.

Join the Bigger Fight

When it comes to cybersecurity, transparency is key. If organizations fail to report cybersecurity incidents promptly and accurately, they not only jeopardize their own security but also that of other organizations and individuals. Transparency builds trust and allows for collaboration in addressing cybersecurity threats collectively.

So, our experts urge you to engage in information sharing and collaborative efforts with other organizations, industry groups, and governmental bodies to stay ahead of threats.

How Has AI Impacted Data Protection and Privacy?

Among the challenges presented by AI, one stands out the most – the potential impact on data privacy and protection. Why? Because there’s a growing fear that personal data might be used to train large AI models.

That’s why European policymakers sprang into action and introduced the Artificial Intelligence Act in March 2024.

This regulation, implemented by the European Parliament, aims to protect fundamental rights, democracy, the rule of law, and environmental sustainability from high-risk AI. The act is akin to the well-known General Data Protection Regulation (GDPR) passed in 2016 but exclusively targets the use of AI. The good news for those fearful of AI’s potential negative impact is that every requirement imposed by this act is backed up with heavy penalties.

But how can organizations ensure customers, clients, and partners that their data is fully protected?

According to our experts, the answer is simple – transparency, transparency, and some more transparency!

Any employed AI system must be designed in a way that doesn’t jeopardize anyone’s privacy and freedom. However, it’s not enough to just design the system in such a way. You must also ensure all the stakeholders understand this design and the system’s operation. This includes providing clear information about the data being collected, how it’s being used, and the measures in place to protect it.

Beyond their immediate group of stakeholders, organizations also must ensure that their data isn’t manipulated or used against people. Tom gives an example of what must be avoided at all costs. Let’s say a client applies for a loan in a financial institution. Under no circumstances should that institution use AI to track the client’s personal data and use it against them, resulting in a loan ban. This hypothetical scenario is a clear violation of privacy and trust.

And according to Tom, “privacy is more important than ever.” The same goes for internal ethical standards organizations must develop.

Keeping Up With Cybersecurity

Like most revolutions, AI has come in fast and left many people (and organizations) scrambling to keep up. However, those who recognize that AI isn’t going anywhere have taken steps to embrace it and fully benefit from it. They see AI for what it truly is – a fundamental shift in how we approach technology and cybersecurity.

Those individuals have also chosen to advance their knowledge in the field by completing highly specialized and comprehensive programs like OPIT’s Enterprise Cybersecurity Master’s program. Coincidentally, this is also the program where you get to hear more valuable insights from Tom Vazdar, as he has essentially developed this course.

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