Artificial Intelligence in Cybersecurity Market

[312 Pages Report] The artificial intelligence in cybersecurity market size is valued at USD 22.4 Billion in 2023 and is anticipated to be USD 60.6 Billion by 2028; growing at a CAGR of 21.9%.

Factors such as the growing adoption of IoT and increasing number of connected devices, increasing instances of cyber threat, rising concerns of data protection, and increasing vulnerability of Wi-Fi networks to security threats are driving the growth of the AI in cybersecurity industry during the forecast period.

Artificial Intelligence in Cybersecurity Market Forecast to 2028

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Artificial intelligence in cybersecurity Market Dynamics

Driver: Increasing instances of cyber threats

The instances of cyberattacks are gradually increasing globally. Cybercriminals attack endpoints, networks, data, and other IT infrastructure that lead to a huge financial loss for individuals, enterprises, and governments. The primary motive of cybercriminals includes political rivalry, financial gain, harm reputation, international rivalry, and radical religious group interest. The majority of cyberattacks are for financial gains. WannaCry, Petya, NotPetya, and BadRabbit are among the significant ransomware that have affected enterprises and government organizations on a large scale.

According to the CISCO cybersecurity threat trends report 2021, 86% of the organizations of organizations had at least one user try to connect to a phishing site, 70% of organizations had users that were served malicious browser ads, 69% of organizations experienced some level of unsolicited crypto mining, and 50% of organizations encountered ransomware-related activity.

Cyber threats are not only hampering the productivity of businesses but also damaging the critical IT infrastructure and sensitive data of organizations. Owing to the rapid rise in the digital transaction globally across various industries, there is an increase in the frequency of cybercrimes. The increase in enterprise data breaches or data leakage is fueling the market for cybersecurity products and services. Therefore, safeguarding against cyber threats has become essential for enterprises’ business growth. Nowadays, cyber attackers are using sophisticated techniques to launch ransomware attacks and aim to raise money through small ransoms comprising a few hundred dollars each. With the advancements of technologies, automated and sophisticated cyberattacks are accelerating. The increasing sophistication of attacks is triggering organizations across the world to adopt cybersecurity solutions and services to combat with swarm cyberattacks. According to Microsoft US was the target of 46% of cyberattacks in 2020, more than double any other country.

Restraint: Inability of AI to stop zero-day and advanced threats

All algorithms or approaches used in AI, including machine learning, genetics algorithm, deep learning, or neural networks, are based on experiences from the past. This means that AI in cybersecurity is based on learning from past malware about what malware looks like and how it behaves. A zero-day threat exploits an unknown computer security vulnerability. An advanced persistent threat (APT) is a network attack in which an unauthorized person gains access to a network and stays undetected for a long period of time. APTs are armed with new methods to invoke application programming interfaces (APIs) and innovative techniques to access system resources, while a few APT behaviors might be similar to past events that AIs can recognize them, new APTs have no past events. Real protection against sophisticated, advanced threats must not rely on prior malware or prior attacks. The inability of AI to stop advanced threats is thus acting as a restraint in this market.

Opportunity: Zero trust framework providing advanced security

Zero trust is a framework that considers that the security of complex networks is constantly under threat from both internal and external threats. A zero-trust security model validates and authorizes each connection made by a user to an application or software that connects to a data set via an application programming interface (API). It helps organize and strategize a thorough approach to counter cyber threats. According to the zero-trust principle, no one or any application should ever be presumed to be trustworthy. According to the principle of least-privileged access, which is a cornerstone of zero trust, trust should be established in accordance with the context (such as user identification and location, endpoint security posture, and the app or service being requested), with policy checks at each level. It utilizes deep learning which reveals document meaning and context to provide accurate, granular categories that reflect business criticality. Once categorized, deep learning can establish a security baseline for each category. That baseline encompasses how files are permissioned, shared, stored, and managed, and it reflects the policy decisions made by the people who know those files best – the owners and end-users

Challenge: Shortcomings of AI

AI is sometimes just useful for detecting threats; it does not always remove threats. However, issues can occur even when AI is used to detect threats as AI and ML are often positioned to perform anomaly detection, which highlights unknowns while also highlighting additional unknowns that are unrelated to security. To avoid this, security teams must constantly train the existing model using training data that includes a strong feedback loop. However, it requires additional effort and cost in addition to looking into the finding itself. The idea that artificial intelligence and machine learning can take the place of human judgment is a misconception. Human decision-making is unmatched. For instance, creating detection criteria based on attack paths, new vulnerabilities, and emerging threat intelligence involves context, research, and creativity. Being aware of impending attacks via research, replicating them, determining where detectability can occur across the stack, and building detections and playbooks are all distinctly human tasks that AI can assist with but cannot fully perform on its own.

Software to hold the largest market share of artificial intelligence in cybersecurity market during the forecast period

Software holds a major share of the overall AI in cybersecurity market owing to developments in AI software and related software development kits. AI systems require different types of software, including application program interfaces, such as language, speech, vision, and sensor data, along with machine learning algorithms, to realize the applications for cybersecurity. The software platforms and solutions are available at higher costs as there are only a limited number of experts that develop machine learning algorithms. Companies such as Darktrace (UK), Cylance (US), Securonix (US), and IBM (US) are among the frontrunners in the development of software for the AI in cybersecurity market.

Cloud-based deployment held the largest share of the market during the forecast period

Cloud deployment is gaining popularity among SMEs as it is cost-effective. Organizations can avoid costs related to hardware, software, storage, and technical staff by using cloud-based cybersecurity solutions. Cloud-based platforms offer a unified way in the form of SaaS-based security services to secure business applications. These platforms are beneficial for organizations that have strict budgets for security investments. SMEs deploy their solutions on the cloud as it enables them to focus on their core competencies, rather than investing their capital on security infrastructure. The cloud-based deployment is growing as cloud-cybersecurity solutions are easy to maintain and upgrade. IBM (US), Cylance (US), Microsoft (US), Amazon (US), and Darktrace (UK) are among the major providers of cloud-based deployment services.

Application security expected to grow at the second fastest rate

Application security solutions enable enterprises to secure their business-sensitive applications through greater visibility and control. The use of existing security solutions, such as IAM, web filtering, and application whitelisting, enables the security of enterprise applications and software codes from external and internal threats, such as web attacks, DDoS, site scraping, and fraud. Business-sensitive applications are among the primary targets of attackers as they carry information about critical assets, as well as processes that are followed by an enterprise. Several organizations rely on network security and neglect applications, which create a security gap and make them susceptible to attacks. The cybersecurity solutions and services prevent user applications from being attacked. These solutions and services collect the security and the event information from all web applications, after which they analyze the data for vulnerable or malicious events and study the event behavior to protect user applications.  This security type includes mobile application security and web application security. The mobile application security ensures the confidentiality of the data that is transferred among mobile applications, along with transparency and visibility among the business processes. Increasing number of mobile devices is expected to drive the market for application security.

North America to hold the largest share of the artificial intelligence in cybersecurity market during the forecast period

North America being a technologically advanced region, tops the world in terms of the presence of security vendors and cyber incidents. North America is the early adopter of cybersecurity solutions and services across the world. Increasing instances of cyberattacks are identified as the most crucial economic and national security challenge by governments in the region.  Businesses in this region top the world in terms of adoption of advanced technologies and infrastructures, such as cloud computing, big data analytics, and IoT. The attacks are increasing dramatically and becoming more sophisticated in nature, and targeting business applications in various industries. Sophisticated cyberattacks include DDoS, ransomware, bot attacks, malware, zero-day attacks, and spear phishing attacks.  Growing trends, such as IoT, Internet of Everything (IoE), Industrial Internet of Things (IIoT), and BYOD, have brought tremendous growth in the region, but have also triggered the rise in cyberattacks and added more complexities and concerns for enterprises. The rise in threats and vulnerabilities, and government compliances have made North America the most lucrative market for different categories of vendors.

Artificial Intelligence in Security Market by Region

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Key Market Players:

The AI in cybersecurity Companies is dominated by a few globally established players such as NVIDIA Corporation (US), Intel Corporation (US), Xilinx Inc. (US), Samsung Electronics Co., Ltd (South Korea), Micron Technology, Inc. (US), IBM Corporation (US), Amazon Web Services, Inc. (US), Microsoft (US), Palo Alto Networks Inc. (US), Trellix (US), CrowdStrike (US), NortonLifeLock (US), Cylance Inc. (US), ThreatMetrix Inc. (US), Securonix Inc. (US), Sift Science (US), Acalvio Technologies (US), Darktrace (UK), SparkCognition Inc. (US), Fortinet (US), Check Point Software Technologies, Ltd (US), High-Tech Bridge (Switzerland), Deep Instinct (US), SentinelOne (US), Feedzai (US), Vectra Networks (US) Zimperium (US), Argus Cyber Security (Israel), Nozomi Networks (US), BitSight Technologies (US), and Antivirus companies such as Kaspersky Lab (Russia), Bitdefender (Romania), and ESET (US).

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Scope of the Report

Artificial Intelligence in Cybersecurity Market Highlights

The study categorizes the artificial intelligence in cybersecurity  market based on  offering, Deployment Type, Security Type, Applications, End Users, Technology, and Geography

Recent Developments

• In September 2022, NVIDIA launched NVIDIA IGX, which is a platform for high-precision edge AI, bringing advanced security and proactive safety to sensitive industries such as manufacturing, logistics and healthcare.
• In November 2021, IBM launched QRadar XDR Connect which is a cloud-native, open XDR solution. Advanced analytics, along with artificial intelligence (AI), automation, and connected workflows from XDR offer deeper insights, quick action, and automatic response to stop and slow the progression of threats.
• In March 2021, Samsung collaborated with Mastercard (US) to work on a biometric scanning payment card with a built-in fingerprint reader,
• In December 2019, Intel acquired Habana Labs (Israel), an Israel-based developer of programmable deep learning accelerators for data centers

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