Traditional rule-based cybersecurity systems have proven highly effective against known malware threats. However, they face challenges in detecting novel threats. To address this issue, emerging cybersecurity systems are incorporating AI techniques, specifically deep-learning algorithms, to enhance their ability to detect incidents, analyze alerts, and respond to events. While these techniques offer a promising approach to combating dynamic security threats, they often require significant computational resources. Therefore, frameworks that incorporate AI-based cybersecurity mechanisms need to support the use of GPUs to ensure optimal performance. Many cybersecurity framework vendors do not provide sufficiently detailed information about their implementation, making it difficult to assess the techniques employed and their effectiveness. This study aims to overcome this limitation by providing an overview of the most used cybersecurity frameworks that utilize AI techniques, specifically focusing on frameworks that provide comprehensive information about their implementation. Our primary objective is to identify the deep-learning techniques employed by these frameworks and evaluate their support for GPU acceleration. We have identified a total of \emph{two} deep-learning algorithms that are utilized by \emph{three} out of 38 selected cybersecurity frameworks. Our findings aim to assist in selecting open-source cybersecurity frameworks for future research and assessing any discrepancies between deep-learning techniques used in theory and practice.

As a CTO with over a decade and a half of experience in the ever-changing field of cybersecurity, I've witnessed the enormous impact that artificial intelligence (AI) has had on the broad technological landscape. In addition, I have seen how AI-based solutions have emerged as an important aspect of improving processes in a variety of fields and disciplines over the years. The capacity of AI-based machine learning (ML) models to recognize patterns and make data-driven decisions and inferences represents a highly innovative strategy for rapidly identifying malware, directing incident response, and even anticipating potential security breaches. AI's role in cybersecurity, how it can be used to improve corporate and user security, and its limitations. Data is being generated at an exponential rate in the modern era of digitization, and an increasing amount of metadata is being saved or received online, either directly or indirectly. Furthermore, in order for data to reach its intended location or be used for specific purposes, it is frequently necessary to send it across a network or store it in a specific database or server.

As a CTO with over one and a half decades of expertise in the ever-changing field of cybersecurity, I have been observing the immense impact that artificial intelligence (AI) has had on the wide technological landscape. Also, I have witnessed how AI-based solutions have emerged as a crucial aspect of enhancing processes in various fields and disciplines over the years. And the cybersecurity field is no exception. The ability of AI-based machine learning (ML) models to identify patterns and make data-driven decisions and inferences present a highly innovative approach to quickly identifying malware, directing incident response and even predicting potential breaches before they occur. Given the significant potential of AI in the field of cybersecurity, this article explores how AI fits into the broader cybersecurity landscape and how it can be effectively leveraged to enhance the security of businesses and their users, along with some of its limitations.

Artificial Intelligence is defined as the ability of a computer program to learn and reason, and also involves a machine doing something typically associated with the intellect of a human. But what if cyber attackers use artificial intelligence to create more sophisticated cyber threats that can penetrate even the best cybersecurity systems and strategies? In this blog post we will explore the pros and cons of Artificial Intelligence in the Cybersecurity domain.

Cybersecurity awareness company NINJIO, a has acquired Israeli company DCOYA – a provider of behavior-centric cybersecurity solutions for organizations of all sizes. NINJIO says that the combination of NINJIO's cybersecurity content with DCOYA's powerful machine-learning-driven cybersecurity awareness platform will give CISOs and other company leaders the most effective cybersecurity awareness-training toolkit on the market. Like NINJIO, DCOYA focuses on behavior modification – an approach that is only becoming more crucial as cybercriminals continue to rely on social engineering to infiltrate companies and steal sensitive information. DCOYA's technology works backward from the psychological tactics of the most successful human-related hacks. The new solution will allow NINJIO to determine a person's area of greatest vulnerability (greed, fear, obedience, and others) and provide reinforcing education that specifically addresses that vulnerability.

Machine learning (ML) is a commonly used term across nearly every sector of IT today. And while ML has frequently been used to make sense of big data--to improve business performance and processes and help make predictions--it has also proven priceless in other applications, including cybersecurity. This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables. The need for machine learning has to do with complexity. Many organizations today possess a growing number of Internet of Things (IoT) devices that aren't all known or managed by IT.

Most cybersecurity technologies, such as endpoint detection and response (EDR) solutions, simply identify, track, record, and contain a threat once it has already entered an environment. Machine learning-based cybersecurity solutions are also an essential part of any security strategy, and use pre-labelled data, classified as either benign or malicious, to detect dangerous patterns. But neither set of cybersecurity solutions can proactively defend against sophisticated attacks without constant human tweaking. Fortunately, deep learning can mimic the functionality and connectivity of neurons in the human brain, enabling neural networks to independently learn from raw and un-curated data and automatically recognize unknown threats. "Deep learning is the only family of algorithms that works on raw data to identify cybersecurity threats with unmatched speed and accuracy," says Guy Caspi, CEO of Deep Instinct, a cybersecurity company.

Saryu Nayyar is CEO of Gurucul, a provider of behavioral security analytics technology and a recognized expert in cyber risk management. Providing effective cybersecurity measures for your organization is like playing a very serious cat-and-mouse game. If you aren't familiar with the idiom, cat and mouse is an interaction in which the advantage continually shifts between the contestants. One moment, the cat appears ready to pounce on the mouse, and the next moment, the mouse dodges the advance. Then, the cat blocks the mouse's path but the mouse jukes and goes the other way.

Resecurity, a cybersecurity company providing managed threat detection and response, has opened a new office in Silicon Beach, an area known as the technology capital of Southern California. Resecurity becomes the first recognizable cybersecurity player in the area, making the local tech ecosystem more diverse and progressive. While Silicon Valley is known as the tech and startup hub throughout California, Silicon Beach is a rising ecosystem in this arena. Silicon Beach is home to an innovative collection of tech companies in Los Angeles (LA). The region has attracted an estimated 500 tech companies, ranging from startups, like Bird and Fair, to global policy think tanks and established tech giants like the RAND Corporation, SpaceX, Google and Facebook.

The global Automotive Cybersecurity Market size is projected to grow from USD 2.0 billion in 2021 to USD 5.3 billion by 2026, at a CAGR of 21.3%. Increasing incidents of cyber-attacks on vehicles and massive vehicles recalls by OEMs have increased awareness about automotive cybersecurity among OEMs globally. Moreover, increasing government mandates on incorporating several safety features, such as rear-view camera, automatic emergency braking, lane departure warning system, and electronic stability control, have further opened new opportunities for automotive cybersecurity service providers globally. As a result, there are various start-ups present in the automotive cybersecurity ecosystem. Government initiatives toward building an intelligent transport system have also further escalated the demand for cybersecurity solutions all over the world.