Artificial Intelligence in Cybersecurity Market by Type (Application Security, Cloud Security, Endpoint Security), Component (Hardware, Services, Software Solutions), Technology, Applications, Organization Size, Deployment, Industry - Global Forecast 2024-2030

[196 Pages Report] The Artificial Intelligence in Cybersecurity Market size was estimated at USD 18.89 billion in 2023 and expected to reach USD 23.25 billion in 2024, at a CAGR 24.18% to reach USD 86.07 billion by 2030.

Artificial intelligence (AI) in cybersecurity refers to the use of machine learning algorithms, natural language processing, and other AI methodologies to enhance cyber defense systems. Integrating AI into cybersecurity allows for automating complex processes for detecting, repelling, and recovering from cyber threats. This includes real-time threat detection, pattern recognition, and predictive analytics to identify potential vulnerabilities before they are exploited. More connected devices and flexible work policies increase vulnerability points, prompting stronger AI cyber defenses and stringent data protection laws, necessitating more advanced security solutions and augmenting the demand for AI in cybersecurity. However, concern regarding technical issues associated with AI in cybersecurity solutions may impede the market adoption. Nevertheless, developing autonomous security systems, AI-driven security for edge computing, machine learning models for predicting new malware, and integrating AI with blockchain for enhanced data integrity may create potential opportunities for market growth.

In the Americas, the market for AI in cybersecurity is driven by a sophisticated digital infrastructure and high cyber threat exposure. Customers in this region exhibit strong awareness and are proactive in adopting innovative AI-powered solutions to mitigate risks. Recently, there has been significant investment in cybersecurity AI, with companies and government agencies filing for numerous patents, reflecting a robust R&D environment with the U.S. governmental initiatives, including the Cybersecurity National Action Plan (CNAP) and private sector investments, underscoring the priority given to AI in combating cyber threats. The countries in the Asia-Pacific region are AI technology exporters and have vast markets for cybersecurity solutions. The Cybersecurity Law imposes strict requirements, creating customer demand for compliant AI security products, and reflects the focus on data sovereignty and protection. Furthermore, investments are surging in local startups focusing on AI in cybersecurity, with a particular emphasis on affordable and scalable solutions suitable for businesses and consumers in Asia-Pacific. The EU's alignment under regulations such as the General Data Protection Regulation (GDPR) provides a common ground for AI-driven cybersecurity solutions focused on data protection and privacy. The European Union Agency for Cybersecurity (ENISA) plays a significant role in nurturing AI security through research and guidelines to harmonize practices across Europe. The Middle East is witnessing an increasing need for AI in cybersecurity as digital transformation initiatives surge. Analysis of customer purchasing behavior indicates a growing inclination toward AI solutions tailored to prevent financial fraud and protect critical energy infrastructure. Initiatives across the continent focus on capacity building and awareness, contributing to gradual market development.

Application security includes measures taken to improve the security of applications by identifying, fixing, and preventing security vulnerabilities. AI enhances this domain by automating threat detection and response at the application level. Enterprises prioritize application security to protect customer data and uphold trust, particularly businesses with a broad digital footprint encompassing web-based services and mobile applications. Cloud security protects the data stored online from theft, leakage, and deletion. AI in cloud security strengthens protection through behavior analysis and anomaly detection, which is crucial for businesses relying on cloud computing. Organizations with significant cloud footprints, offering SaaS or utilizing large-scale cloud infrastructure, would benefit from these AI-based solutions. Endpoint security aims to secure every endpoint, or entry point, of end-user devices, including desktops, laptops, and mobile devices, from exploitation by malicious actors. AI significantly upscales the detection capabilities by identifying patterns and providing real-time defense. This is particularly important for organizations with a remote or mobile workforce and those employing IoT devices. Network security protects the integrity of a company’s network and its data. AI in network security bolsters traditional defense mechanisms, including firewalls and intrusion detection systems, by enabling them to learn from network flow patterns, predict anomalies, and initiate autonomous responses. This is pivotal for enterprises managing an expansive network infrastructure and those handling sensitive or proprietary information.

Hardware involves physical devices specifically designed for AI processing and cybersecurity tasks. This includes specialized processors such as graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs) tailored for machine learning and data processing, physical security tokens, and biometric scanners. Hardware plays a critical role in AI cybersecurity by providing the required computational power to handle complex algorithms and large volumes of data at high speed, thus ensuring real-time threat detection and prevention. Services in AI cybersecurity involve consultative, implementation, support, and maintenance activities conducted by specialized professionals and companies. These services can include initial security assessments, vulnerability analyses, and more complex tasks such as implementing AI-powered solutions, training machine learning models, ongoing threat monitoring, and incident response. Software solutions are crucial for cybersecurity AI, providing the tools for threat detection, behavior analysis, and incident response. This category comprises security information and event management (SIEM) software, threat intelligence platforms, antivirus programs, intrusion detection and prevention systems (IDPS), and various types of security orchestration, automation, and response (SOAR) tools. Hardware presents a fundamental substrate for high-performance data processing, which is indispensable for AI computations; services in AI cybersecurity provide expertise and tailored solutions, particularly beneficial to medium and small enterprises that lack the in-house capabilities for managing AI in cybersecurity efficiently, while software solutions offer the necessary tools that organizations need to protect their digital assets.

Computer vision technology allows machines to interpret and make decisions based on visual data. Computer vision in cybersecurity is used for identity authentication through facial recognition, scanning QR codes for secure entry, and monitoring video surveillance to detect unauthorized access or anomalies. Machine learning (ML) algorithms analyze patterns in large data sets to detect potential threats such as malware, phishing attempts, and unusual network behavior. ML enables systems to adapt over time, becoming more effective as they learn from new data. Machine learning is more central to cybersecurity than computer vision and natural language processing (NLP), owing to its broad threat detection and response applications.NLP enables computers to understand, interpret, and generate human language. Furthermore, in cybersecurity, NLP is essential for analyzing phishing emails, social engineering attempts, and security policies. It can assist in automating compliance reporting and analyzing the dark web for potential threats.

Data loss prevention (DLP) systems are paramount for safeguarding sensitive information from potential breaches and unauthorized access. AI enhances DLP by identifying patterns that may indicate a data exfiltration attempt before the information leaves the organization. AI algorithms can learn from several data transactions and effectively flag anomalous behaviors that traditional software might overlook. Identity and access management (IAM) ensures that the right individuals access the right resources at the right times for the right reasons. AI bolstered IAM systems to sharpen security by employing behavioral analytics to monitor user activities. This can enhance security protocols by detecting and responding to unusual access patterns. Intrusion detection/prevention systems (IDPS) are critical in identifying suspicious activities that could lead to security breaches. AI augments IDPS by learning normal network behavior dynamically and identifying aberrations that may signify an attack. These systems are suitable for organizations with large networks that need continuous monitoring. Risk and compliance management systems are essential for organizations to proactively meet regulatory requirements and manage risks. AI allows these systems to evaluate complex datasets to predict potential threats and non-compliance issues. Businesses operating in highly regulated environments, such as finance and healthcare, can gain the most from AI-driven risk and compliance management tools. DLP systems prioritize data protection at rest and in transit, targeting insider threats and external breaches, while IAM focuses on user access rights. In contrast, IDPS monitors network patterns for potential external attacks. Risk and compliance management systems are not directly involved in preventing attacks; however, they play a preventive role by ensuring adherence to standards that mitigate the probability of breaches and the associated legal liabilities.

Large enterprises have significantly larger volumes of data, more complex infrastructure, and greater cybersecurity risk exposure. These organizations have a dedicated cybersecurity team and a larger budget for advanced security solutions. AI-driven security systems for large enterprises ACare designed to handle high volumes of data and complex threat detection and remediation tasks. Small and medium-sized enterprises have different cybersecurity needs compared to their larger counterparts. SMEs usually have smaller cybersecurity teams and budgets. Consequently, their AI cybersecurity solutions are typically more cost-effective and simpler to deploy without requiring extensive customization. SMEs need AI cybersecurity tools that are user-friendly and affordable and do not demand extensive cybersecurity knowledge to manage. These organizations prefer solutions that offer basic automated threat detection and response that can scale as they grow. Large enterprises tend to adopt AI solutions that can seamlessly integrate with their existing systems, offer a broad spectrum of security features, and provide in-depth analytics and reporting capabilities.

Cloud-based deployment of artificial intelligence (AI) in cybersecurity refers to utilizing AI-powered tools and services hosted on the vendor’s servers and accessible over the internet. This model is widely used among businesses of all sizes owing to its scalability, flexibility, and cost-effectiveness. Organizations can leverage advanced threat detection and response capabilities with cloud-based AI cybersecurity solutions without significant upfront investment in hardware or specialized IT staff. On-premises deployment involves installing and managing AI-powered cybersecurity systems within the physical confines of an organization. This approach is preferred by organizations with stringent data control requirements or those operating in highly regulated industries. An on-premises solution provides greater control over the AI tools, potentially resulting in enhanced security compliance and data privacy. Cloud-based solutions offer a pay-as-you-go model, reducing the need for significant capital expenditure, and are highly scalable to accommodate fluctuating data volumes. An on-premises approach offers increased control and potentially better compliance with industry regulations. Moreover, it can provide a perceived higher level of security, as all data and processes remain within the corporate boundary.

The aerospace & defense sector increasingly incorporates AI into cybersecurity to protect highly sensitive national security data and complex systems against sophisticated threats. There is a pressing need for constant vigilant monitoring, threat detection, and response mechanisms. Cybersecurity in the automotive industry is prioritizing AI to defend against hacking and breaches that could compromise vehicle safety and data as vehicles become more connected and autonomous; AI is utilized for anomaly detection and adaptive response systems. The banking, financial service & insurance sector targets AI applications for fraud detection, risk assessment, and compliance. At the same time, the need-based preference is toward predictive analytics and real-time threat intelligence. Cybersecurity becomes crucial to protect assets and smart building systems from cyberattacks, with the digitalization of building infrastructure enabling predictive maintenance and threat identification with the help of AI. Artificial intelligence enhances cybersecurity by providing advanced solutions in the consumer goods & retail sector to protect customer data, secure transaction platforms, and prevent service disruptions. AI is used in combating retail fraud, securing supply chain networks, and ensuring safe and personalized shopping experiences over digital channels. The education sector benefits from AI in cybersecurity by safeguarding sensitive student and faculty data and intellectual property. AI facilitates automated monitoring of educational networks, detection of phishing attempts, and response to evolving cyber threats that target academic institutions' extensive digital assets. Artificial intelligence is increasingly pivotal in the cybersecurity strategies of the energy and utilities sector to protect against threats to critical infrastructure. AI is utilized in the government and public sector to reinforce cybersecurity defenses, combat cyber espionage, and protect necessary data. In the healthcare and life sciences sector, AI's role in cybersecurity is crucial for protecting patient data, ensuring the confidentiality of health records, and maintaining the functionality of life-saving medical devices. The information technology sector heavily relies on AI for cybersecurity to defend against sophisticated cyber threats and secure software development. Cybersecurity AI applications in manufacturing focus on protecting against industrial espionage, securing interconnected machines, and safeguarding intellectual property. AI fortifies cybersecurity in the media & entertainment sector by defending digital content from piracy, preventing data breaches, and ensuring secure distribution channels. AI plays a strategic role in the telecommunication industry by securing networks from unauthorized intrusions, enhancing threat intelligence, and maintaining service continuity. The travel & hospitality sector relies on AI to protect customer data, ensure secure booking systems, and defend against cyber threats that can impact brand reputation and revenue. Furthermore, AI's application in this sector includes enhancing fraud detection systems and ensuring the privacy and security of guest information within digital platforms.

The market dynamics represent an ever-changing landscape of the Artificial Intelligence in Cybersecurity Market by providing actionable insights into factors, including supply and demand levels. Accounting for these factors helps design strategies, make investments, and formulate developments to capitalize on future opportunities. In addition, these factors assist in avoiding potential pitfalls related to political, geographical, technical, social, and economic conditions, highlighting consumer behaviors and influencing manufacturing costs and purchasing decisions.

The market disruption analysis delves into the core elements associated with market-influencing changes, including breakthrough technological advancements that introduce novel features, integration capabilities, regulatory shifts that could drive or restrain market growth, and the emergence of innovative market players challenging traditional paradigms. This analysis facilitates a competitive advantage by preparing players in the Artificial Intelligence in Cybersecurity Market to pre-emptively adapt to these market-influencing changes, enhances risk management by early identification of threats, informs calculated investment decisions, and drives innovation toward areas with the highest demand in the Artificial Intelligence in Cybersecurity Market.

The porter's five forces analysis offers a simple and powerful tool for understanding, identifying, and analyzing the position, situation, and power of the businesses in the Artificial Intelligence in Cybersecurity Market. This model is helpful for companies to understand the strength of their current competitive position and the position they are considering repositioning into. With a clear understanding of where power lies, businesses can take advantage of a situation of strength, improve weaknesses, and avoid taking wrong steps. The tool identifies whether new products, services, or companies have the potential to be profitable. In addition, it can be very informative when used to understand the balance of power in exceptional use cases.

The value chain of the Artificial Intelligence in Cybersecurity Market encompasses all intermediate value addition activities, including raw materials used, product inception, and final delivery, aiding in identifying competitive advantages and improvement areas. Critical path analysis of the <> market identifies task sequences crucial for timely project completion, aiding resource allocation and bottleneck identification. Value chain and critical path analysis methods optimize efficiency, improve quality, enhance competitiveness, and increase profitability. Value chain analysis targets production inefficiencies, and critical path analysis ensures project timeliness. These analyses facilitate businesses in making informed decisions, responding to market demands swiftly, and achieving sustainable growth by optimizing operations and maximizing resource utilization.

The pricing analysis comprehensively evaluates how a product or service is priced within the Artificial Intelligence in Cybersecurity Market. This evaluation encompasses various factors that impact the price of a product, including production costs, competition, demand, customer value perception, and changing margins. An essential aspect of this analysis is understanding price elasticity, which measures how sensitive the market for a product is to its price change. It provides insight into competitive pricing strategies, enabling businesses to position their products advantageously in the Artificial Intelligence in Cybersecurity Market.

The technology analysis involves evaluating the current and emerging technologies relevant to a specific industry or market. This analysis includes breakthrough trends across the value chain that directly define the future course of long-term profitability and overall advancement in the Artificial Intelligence in Cybersecurity Market.

The patent analysis involves evaluating patent filing trends, assessing patent ownership, analyzing the legal status and compliance, and collecting competitive intelligence from patents within the Artificial Intelligence in Cybersecurity Market and its parent industry. Analyzing the ownership of patents, assessing their legal status, and interpreting the patents to gather insights into competitors' technology strategies assist businesses in strategizing and optimizing product positioning and investment decisions.

The trade analysis of the Artificial Intelligence in Cybersecurity Market explores the complex interplay of import and export activities, emphasizing the critical role played by key trading nations. This analysis identifies geographical discrepancies in trade flows, offering a deep insight into regional disparities to identify geographic areas suitable for market expansion. A detailed analysis of the regulatory landscape focuses on tariffs, taxes, and customs procedures that significantly determine international trade flows. This analysis is crucial for understanding the overarching legal framework that businesses must navigate.

The regulatory framework analysis for the Artificial Intelligence in Cybersecurity Market is essential for ensuring legal compliance, managing risks, shaping business strategies, fostering innovation, protecting consumers, accessing markets, maintaining reputation, and managing stakeholder relations. Regulatory frameworks shape business strategies and expansion initiatives, guiding informed decision-making processes. Furthermore, this analysis uncovers avenues for innovation within existing regulations or by advocating for regulatory changes to foster innovation.

The FPNV positioning matrix is essential in evaluating the market positioning of the vendors in the Artificial Intelligence in Cybersecurity Market. This matrix offers a comprehensive assessment of vendors, examining critical metrics related to business strategy and product satisfaction. This in-depth assessment empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success, namely Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

The market share analysis is a comprehensive tool that provides an insightful and in-depth assessment of the current state of vendors in the Artificial Intelligence in Cybersecurity Market. By meticulously comparing and analyzing vendor contributions, companies are offered a greater understanding of their performance and the challenges they face when competing for market share. These contributions include overall revenue, customer base, and other vital metrics. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With these illustrative details, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

• HiddenLayer Partners with CVE Program as a Numbering Authority to Secure AI

  HiddenLayer announced its strategic partnership with the Common Vulnerabilities and Exposures (CVE) Program, embarking on a mission to bolster artificial intelligence (AI) security. By becoming a CVE Numbering Authority (CNA), HiddenLayer can assign CVE identifiers to security vulnerabilities, specifically within the AI domain. This collaboration underscores the recognition of AI's expanding role in cybersecurity and the need for standardizing vulnerability identification to protect AI systems. [Published On: 2023-12-20]

• Salvador Technologies Secures USD 6 million Funding For Cyber-Attack Recovery Expansion

  Salvador Technologies has successfully raised USD 6 million to expand its endeavors in cyber-attack recovery solutions. This substantial funding is geared towards enhancing Salvador's unique approach to cybersecurity, which emphasizes rapid recovery from cyber incidents rather than solely focusing on prevention. By leveraging state-of-the-art artificial intelligence (AI) technologies, Salvador Technologies aims to minimize downtime and ensure business continuity for organizations facing cyber threats. [Published On: 2023-12-20]

• Opal Security Scores USD 22M Investment for IAM Technology

  Opal Security raised USD 22 million in a funding endeavor aimed at enhancing its cybersecurity offerings. This financial injection underscores the growing recognition of artificial intelligence's (AI) 's pivotal role in bolstering cybersecurity measures. The investment enables Opal Security to refine its IAM solutions, empowering businesses to navigate the complex security landscape more effectively. [Published On: 2023-12-08]

The strategic analysis is essential for organizations seeking a solid foothold in the global marketplace. Companies are better positioned to make informed decisions that align with their long-term aspirations by thoroughly evaluating their current standing in the Artificial Intelligence in Cybersecurity Market. This critical assessment involves a thorough analysis of the organization’s resources, capabilities, and overall performance to identify its core strengths and areas for improvement.

The report delves into recent significant developments in the Artificial Intelligence in Cybersecurity Market, highlighting leading vendors and their innovative profiles. These include Acalvio Technologies, Inc., Advanced Micro Devices, Inc., Amazon Web Services, Inc., Argus Cyber Security Ltd., BitSight Technologies, Inc., BlackBerry Limited, Broadcom Inc., Capgemini Services SAS, CHECK POINT SOFTWARE TECHNOLOGIES LTD, Cisco Systems, Inc., Cloudflare, Inc., CROWDSTRIKE HOLDINGS, INC, CYBERARK SOFTWARE LTD., Darktrace Holdings Limited, Dassault Systèmes S.E., Deep Instinct, Inc., Feedzai – Consultadoria e Inovação Tecnológica, S.A., Fortinet, Inc., Gen Digital Inc., Google LLC by Alphabet Inc., Infosys Limited, Intel Corporation, International Business Machines Corporation, LexisNexis Risk Solutions, Inc., Mastercard Incorporated, McAfee, LLC, Microsoft Corporation, Nozomi Networks Inc., NVIDIA Corporation, Palo Alto Networks, Inc., Proofpoint Inc., RAPID7, INC., Samsung Electronics Co., Ltd., Securonix, Inc., Sentinelone, Inc., SparkCognition, Inc., Telefonaktiebolaget LM Ericsson, Tenable, Inc., Vectra AI, Inc., WatchGuard Technologies, Inc., Wipro Limited, and Zimperium, Inc..

This research report categorizes the Artificial Intelligence in Cybersecurity Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Type
  • Application Security
  • Cloud Security
  • Endpoint Security
  • Network Security
• Component
  • Hardware
  • Services
  • Software Solutions
• Technology
  • Computer Vision
  • Machine Learning
  • Natural Language Processing
• Applications
  • Data Loss Prevention
  • Identity & Access Management
  • Intrusion Detection/Prevention Systems
  • Risk and Compliance Management
• Organization Size
  • Large Enterprises
  • Small & Medium Enterprises
• Deployment
  • On-Cloud
  • On-Premises
• Industry
  • Aerospace & Defense
  • Automotive & Transportation
  • Banking, Financial Services & Insurance
  • Building, Construction & Real Estate
  • Consumer Goods & Retail
  • Education
  • Energy & Utilities
  • Government & Public Sector
  • Healthcare & Life Sciences
  • Information Technology
  • Manufacturing
  • Media & Entertainment
  • Telecommunication
  • Travel & Hospitality

• Region
  • Americas
    • Argentina
    • Brazil
    • Canada
    • Mexico
    • United States
      • California
      • Florida
      • Illinois
      • New York
      • Ohio
      • Pennsylvania
      • Texas
  • Asia-Pacific
    • Australia
    • China
    • India
    • Indonesia
    • Japan
    • Malaysia
    • Philippines
    • Singapore
    • South Korea
    • Taiwan
    • Thailand
    • Vietnam
  • Europe, Middle East & Africa
    • Denmark
    • Egypt
    • Finland
    • France
    • Germany
    • Israel
    • Italy
    • Netherlands
    • Nigeria
    • Norway
    • Poland
    • Qatar
    • Russia
    • Saudi Arabia
    • South Africa
    • Spain
    • Sweden
    • Switzerland
    • Turkey
    • United Arab Emirates
    • United Kingdom

1. Market Penetration: This section thoroughly overviews the current market landscape, incorporating detailed data from key industry players.
2. Market Development: The report examines potential growth prospects in emerging markets and assesses expansion opportunities in mature segments.
3. Market Diversification: This includes detailed information on recent product launches, untapped geographic regions, recent industry developments, and strategic investments.
4. Competitive Assessment & Intelligence: An in-depth analysis of the competitive landscape is conducted, covering market share, strategic approaches, product range, certifications, regulatory approvals, patent analysis, technology developments, and advancements in the manufacturing capabilities of leading market players.
5. Product Development & Innovation: This section offers insights into upcoming technologies, research and development efforts, and notable advancements in product innovation.

1. What is the current market size and projected growth?
2. Which products, segments, applications, and regions offer promising investment opportunities?
3. What are the prevailing technology trends and regulatory frameworks?
4. What is the market share and positioning of the leading vendors?
5. What revenue sources and strategic opportunities do vendors in the market consider when deciding to enter or exit?