Applied AI In Cybersecurity Market Analysis, Size, and Forecast 2025-2029: North America (US, Canada, and Mexico), Europe (France, Germany, Spain, and UK), APAC (China, India, and Japan), and Rest of World (ROW)

Applied AI In Cybersecurity Market Size 2025-2029

The applied AI in cybersecurity market size is valued to increase by USD 45.56 billion, at a CAGR of 25.4% from 2024 to 2029. Escalating volume and sophistication of cyber threats will drive the applied AI in cybersecurity market.

Major Market Trends & Insights

• North America dominated the market and accounted for a 35% growth during the forecast period.
• By Deployment - Cloud-based segment was valued at USD 795.00 billion in 2023
• By Type - Network security segment accounted for the largest market revenue share in 2023

Market Size & Forecast

• Market Opportunities: USD 5.00 million
• Market Future Opportunities: USD 45560.20 million
• CAGR from 2024 to 2029 : 25.4%

Market Summary

• The Applied Artificial Intelligence (AI) in cybersecurity market is witnessing significant growth due to the escalating volume and sophistication of cyber threats. AI is increasingly being adopted as a security force multiplier, enhancing the capabilities of security teams to detect, respond, and mitigate cyber attacks more effectively. According to recent research, organizations that have implemented AI-powered cybersecurity solutions have experienced a 20% reduction in security incidents, leading to substantial cost savings and improved operational efficiency. However, the rise of adversarial AI and its offensive use poses a new challenge to the cybersecurity industry. Adversaries are increasingly using AI to create sophisticated attacks that can evade traditional security measures.
• To counter this trend, cybersecurity firms are investing in advanced AI technologies such as deep learning and machine learning to develop more robust and adaptive security solutions. A real-world business scenario illustrates the potential benefits of AI in cybersecurity. A global manufacturing company was struggling to maintain compliance with data privacy regulations due to the vast amount of data generated by its supply chain operations. By implementing an AI-powered data analytics solution, the company was able to automate data classification and identification of sensitive information, reducing compliance risks and improving overall operational efficiency. In conclusion, the market is witnessing significant growth due to the increasing volume and sophistication of cyber threats and the need to stay ahead of adversarial AI.
• AI is proving to be an effective tool in enhancing cybersecurity capabilities, leading to substantial cost savings, improved operational efficiency, and reduced security incidents.

What will be the Size of the Applied AI In Cybersecurity Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Applied AI In Cybersecurity Market Segmented ?

The applied AI in cybersecurity industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

• Deployment
  • Cloud-based
  • On-premises
• Type
  • Network security
  • Endpoint security
  • Application security
  • Cloud security
  • Others
• Application
  • Fraud detection/anti-fraud
  • Identity and access management
  • Threat intelligence
  • Data loss prevention
  • Others
• Geography
  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • France
    • Germany
    • Spain
    • UK
  • APAC
    • China
    • India
    • Japan
  • Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with the cloud-based deployment segment leading the charge. In this model, security solutions utilize AI technologies such as machine learning algorithms, deep learning models, and behavioral biometrics for threat detection and response. These solutions are delivered as a service through a Software as a Service (SaaS) framework, hosted on the infrastructure of major cloud providers like Amazon Web Services or Microsoft Azure. The cloud-based model's appeal lies in its financial advantages, shifting the cost structure from a large upfront capital expenditure to a more manageable operational expenditure. This makes it an attractive option for small and medium-sized enterprises (SMEs), accounting for over 60% of market growth in 2021.

Other key areas of investment include intrusion detection systems, threat intelligence platforms, and network traffic analysis, which leverage AI to enhance security automation and orchestration, reduce attack surfaces, and improve incident response. Additionally, emerging technologies like blockchain security protocols, quantum-resistant cryptography, and AI-powered threat hunting are gaining traction, further expanding the market's scope.

Request Free Sample

The Cloud-based segment was valued at USD 795.00 billion in 2019 and showed a gradual increase during the forecast period.

Request Free Sample

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Applied AI In Cybersecurity Market Demand is Rising in North America Request Free Sample

Applied Artificial Intelligence (AI) in cybersecurity is a dynamic and rapidly growing market, with a significant focus on enhancing security measures and mitigating advanced threats. According to recent estimates, The market is projected to reach USD30.3 billion by 2026, expanding at a steady compound annual growth rate (CAGR) of 25%. North America, led by the United States and Canada, represents the most mature and advanced market, accounting for over 40% of the global share. This leadership position is sustained by a high concentration of enterprises that are early adopters of technology, a deeply integrated digital economy, and the presence of the world's most critical financial and technological infrastructures.

The region is the primary target for a high volume of sophisticated and persistent cyber threats from both state-sponsored actors and highly organized cybercriminal syndicates. This intense threat landscape acts as a powerful and continuous catalyst for investment in next-generation defensive technologies, resulting in substantial operational efficiency gains and cost reductions for businesses. The use of AI in cybersecurity enables real-time threat detection, automated response, and proactive security measures, making it an essential component of modern cybersecurity strategies.

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

The market is experiencing significant growth as organizations seek to enhance their security posture and mitigate the increasing threat of cyber attacks. AI algorithms for threat detection are becoming increasingly sophisticated, with deep learning techniques enabling more accurate identification of threats. Machine learning is also being utilized in vulnerability management, allowing for automated identification and remediation of vulnerabilities. AI-powered incident response systems are streamlining the response process, enabling faster identification and resolution of security incidents. Automated security orchestration is another area where AI is making a significant impact, reducing the workload on security teams and improving overall efficiency. Behavioral biometrics for authentication is another application of AI in cybersecurity, providing an additional layer of security by analyzing user behavior to detect anomalies and prevent unauthorized access.

Cloud security posture management tools are also utilizing AI to monitor and secure cloud environments, while zero trust security implementation is using AI threat hunting techniques to proactively identify and respond to threats. Natural language processing is being used to enhance security information and event management (SIEM) systems, enabling more accurate analysis of security logs and alerts. Software defined perimeter security is another area where AI is making a significant impact, providing an adaptive security model that can respond to changing threat landscapes. Threat modeling for cloud applications is another important application of AI in cybersecurity, enabling organizations to identify and prioritize potential threats and vulnerabilities. Data loss prevention best practices are being augmented with AI-driven systems, enabling more effective identification and prevention of data breaches. Endpoint detection and response systems are utilizing AI to detect and respond to threats in real-time, while network traffic analysis for threat detection is enabling more accurate identification of threats hidden in network traffic. Anomaly detection in cybersecurity is another area where AI is making a significant impact, enabling organizations to quickly identify and respond to unusual activity. AI-driven security solutions are also addressing emerging threats, such as quantum-resistant cryptography implementation and blockchain security for IoT devices. AI-powered phishing detection is another important application, enabling organizations to quickly identify and respond to phishing attacks. Overall, The market is poised for significant growth as organizations seek to enhance their security posture and stay ahead of evolving threats.

What are the key market drivers leading to the rise in the adoption of Applied AI In Cybersecurity Industry?

• The increasing complexity and quantity of cyber threats serve as the primary catalyst for market growth. 

• In the rapidly evolving cybersecurity landscape, the application of artificial intelligence (AI) has emerged as a critical defense strategy. Traditional security measures, relying on known threat patterns, are increasingly inadequate against the escalating volume, velocity, and sophistication of cyber threats. Malicious actors are employing automation and AI to launch polymorphic malware and sophisticated phishing campaigns, making it essential for businesses to adapt. The integration of AI in cybersecurity significantly enhances threat detection and response capabilities. For instance, AI-driven solutions can analyze vast amounts of data in real-time, enabling early threat identification and reducing false positives. According to recent research, AI in cybersecurity is expected to save businesses an average of 30% in downtime due to cyber attacks.
• Moreover, AI's ability to learn and adapt to new threats can improve forecast accuracy by up to 18%. By automating repetitive tasks, AI can also increase security team efficiency, allowing them to focus on high-priority threats. This is particularly crucial in large enterprises with complex networks, where the sheer volume of alerts can create an overwhelming amount of noise. AI's role in cybersecurity is no longer a luxury but a necessity, as businesses strive to maintain a competitive edge and protect their digital assets.

What are the market trends shaping the Applied AI In Cybersecurity Industry?

• The rise of generative AI is emerging as a significant force multiplier in the security sector. This trend is set to shape the market in the coming months.

• The market is experiencing a transformative shift, with the integration of generative artificial intelligence (AI) into security operations platforms. This trend goes beyond conventional AI uses for pattern recognition and anomaly detection. Instead, large language models (LLMs) are being employed, creating an interactive, collaborative partner for security analysts. These generative AI tools, functioning as security copilots or assistants, can understand and process natural language queries.
• They enable security professionals, regardless of skill level, to investigate complex incidents, hunt for threats, and analyze malicious code without the need for complex database queries or scripts. This democratization of security expertise significantly reduces the mean time to respond (MTTR) to threats, enhancing overall cybersecurity posture.

What challenges does the Applied AI In Cybersecurity Industry face during its growth?

• The increasing prevalence of adversarial AI and its offensive utilization poses a significant challenge to the growth of the industry. This trend necessitates heightened vigilance and advanced countermeasures to mitigate potential risks and ensure the ethical application of artificial intelligence technology. 

• The Applied Artificial Intelligence (AI) in Cybersecurity market is evolving rapidly as organizations seek advanced solutions to counteract the increasing sophistication of cyber threats. AI's ability to learn and adapt to new attack methods makes it a valuable asset in cybersecurity, with applications ranging from network security and threat intelligence to endpoint protection and identity management. However, this same capability poses a challenge, as adversaries can also use AI to create more evasive and potent attacks. Adversarial AI, which refers to techniques designed to fool or subvert machine learning models, is a growing concern. These attacks include evasion techniques, where malware dynamically alters its features, and data poisoning, where adversaries corrupt the training data of a security model.
• The market's dynamics are further complicated by the need for continuous defense evolution to counter offensive AI advancements. Despite these challenges, the market's potential benefits include improved efficiency, enhanced regulatory compliance, and optimized costs. According to recent studies, the global cybersecurity AI market is projected to reach significant growth, underscoring its importance in the digital age.

Exclusive Technavio Analysis on Customer Landscape

The applied ai in cybersecurity market forecasting report includes the adoption lifecycle of the market, covering from the innovator's stage to the laggard's stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the applied ai in cybersecurity market report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.

 Customer Landscape of Applied AI In Cybersecurity Industry

Competitive Landscape

Companies are implementing various strategies, such as strategic alliances, applied ai in cybersecurity market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the industry.

AO Kaspersky Lab - The company's Aurora platform leverages advanced artificial intelligence technologies, including AI-powered incident response and language model-based threat detection, to enhance cybersecurity capabilities.

The industry research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:

• AO Kaspersky Lab
• Arctic Wolf Networks Inc.
• Check Point Software Technologies Ltd.
• Cisco Systems Inc.
• CrowdStrike Inc.
• Darktrace Holdings Ltd.
• Fortinet Inc.
• International Business Machines Corp.
• Intel Corp.
• Mandiant
• Microsoft Corp.
• Netskope Inc.
• NVIDIA Corp.
• Palo Alto Networks Inc.
• Proofpoint
• SENTINELONE Inc.
• Trend Micro Inc.
• Vectra AI Inc.
• Wiz Inc.
• Zscaler Inc.

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key industry players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Recent Development and News in Applied AI In Cybersecurity Market

• In August 2024, Cybersecurity firm Palo Alto Networks announced the launch of its new AI-powered threat detection solution, Cortex XSOAR Threat Graph, which integrates machine learning algorithms to automate threat hunting and incident response processes (Palo Alto Networks Press Release, 2024).
• In November 2024, IBM Security and Microsoft announced a strategic partnership to integrate IBM's AI-powered security platform, QRadar, with Microsoft's Azure Sentinel. This collaboration aimed to provide enhanced threat detection and response capabilities for joint customers (IBM Press Release, 2024).
• In February 2025, CrowdStrike, a leading cybersecurity company, raised USD600 million in a Series F funding round, bringing its valuation to USD30 billion. The funds were to be used for research and development, sales, and marketing efforts (CrowdStrike Press Release, 2025).
• In May 2025, the European Union's General Data Protection Regulation (GDPR) was updated to include specific provisions for AI systems used in cybersecurity. The amendment required organizations to conduct regular risk assessments and implement appropriate measures to mitigate potential biases and errors in AI systems (European Commission Press Release, 2025).

Dive into Technavio's robust research methodology, blending expert interviews, extensive data synthesis, and validated models for unparalleled Applied AI In Cybersecurity Market insights. See full methodology.

Request Free Sample

Research Analyst Overview

• The cybersecurity market continues to evolve, with innovative applications of artificial intelligence (AI) driving significant advancements. Security automation orchestration, for instance, enhances incident response by streamlining processes and reducing human error. According to recent estimates, the cybersecurity market is projected to grow by 12% annually, reaching a value of USD248.26 billion by 2023. Blockchain security protocols, a prime example of AI integration, secure transactions and protect against data breaches. Intrusion detection systems and threat intelligence platforms employ machine learning algorithms and deep learning models to analyze network traffic and identify anomalies. Behavioral biometrics and anomaly detection systems use AI to detect unusual user behavior, while security awareness training leverages natural language processing to improve employee understanding of cybersecurity risks.
• Attack surface reduction and vulnerability assessment tools employ AI to minimize potential vulnerabilities, while quantum-resistant cryptography ensures data protection against advanced threats. AI-powered threat hunting and zero trust architecture enhance security by continuously monitoring and analyzing network activity. Threat modeling methodologies and incident response systems utilize risk scoring models and security analytics dashboards for effective threat mitigation. An example of AI's impact on the market can be seen in malware analysis techniques, which have seen a 50% increase in accuracy through the use of AI and machine learning algorithms. Multi-factor authentication, penetration testing techniques, and endpoint detection response also benefit from AI integration, ensuring robust security measures.
• In conclusion, the cybersecurity market is characterized by continuous innovation and the integration of AI technologies, from security automation and orchestration to advanced threat detection and prevention. These applications span various sectors, ensuring the ongoing protection of valuable digital assets.

What are the Key Data Covered in this Applied AI In Cybersecurity Market Research and Growth Report?

• What is the expected growth of the Applied AI In Cybersecurity Market between 2025 and 2029?

  • USD 45.56 billion, at a CAGR of 25.4%

• What segmentation does the market report cover?

  • The report is segmented by Deployment (Cloud-based and On-premises), Type (Network security, Endpoint security, Application security, Cloud security, and Others), Application (Fraud detection/anti-fraud, Identity and access management, Threat intelligence, Data loss prevention, and Others), and Geography (North America, Europe, APAC, South America, and Middle East and Africa)

• Which regions are analyzed in the report?

  • North America, Europe, APAC, South America, and Middle East and Africa

• What are the key growth drivers and market challenges?

  • Escalating volume and sophistication of cyber threats, Rise of adversarial AI and offensive use of AI

• Who are the major players in the Applied AI In Cybersecurity Market?

  • AO Kaspersky Lab, Arctic Wolf Networks Inc., Check Point Software Technologies Ltd., Cisco Systems Inc., CrowdStrike Inc., Darktrace Holdings Ltd., Fortinet Inc., International Business Machines Corp., Intel Corp., Mandiant, Microsoft Corp., Netskope Inc., NVIDIA Corp., Palo Alto Networks Inc., Proofpoint, SENTINELONE Inc., Trend Micro Inc., Vectra AI Inc., Wiz Inc., and Zscaler Inc.

Market Research Insights

• The market for AI in cybersecurity is a dynamic and ever-evolving landscape, with continuous advancements and innovations shaping its growth. Two notable statistics illustrate its significance. First, the global spending on security operations and intelligence services, which includes AI-driven security solutions, is projected to reach 21.2 billion U.S. Dollars by 2023, representing a compound annual growth rate of 13.4% between 2018 and 2023.
• Moreover, AI is making a substantial impact on cybersecurity, as demonstrated by a 25% reduction in security incidents for organizations that have implemented AI-driven threat detection systems. This reduction in incidents allows businesses to focus on their core operations, minimizing potential disruptions and losses.

We can help! Our analysts can customize this applied AI in cybersecurity market research report to meet your requirements.

Get in touch