Generative AI Cybersecurity Market

[450 Pages Report] The Generative AI cybersecurity market is undergoing rapid expansion, with estimates projecting a substantial market value surge from approximately USD 7.1 billion in 2024 to USD 40.1 billion by 2030. This phenomenal upward trend, characterized by a remarkable CAGR of 33.4% between 2024–2030, is exacerbated due to a variety of factors.  Advanced generative AI solutions are being leveraged to predict, detect, and mitigate cyber threats which have become more sophisticated and are hoodwinking traditional cyber defense methods. Generative AI algorithms, especially Generative Adversarial Networks (GANs), have proved useful in augmenting threat intelligence, automating response processes, and streamlining Security Operation Centers (SOCs). Enterprises have started investing heavily in generative AI-infused solutions that can secure cloud services and on-premise infrastructures, as well as improve endpoint security and access management controls. The expansion of the market is also due to generative AI playing dual roles in the cybersecurity industry. Not only does generative AI enhance the effectiveness of cybersecurity operations, but it also ensures safety of generative AI native workloads from data poisoning, prompt injections and complex malwares.

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Market Dynamics

Driver: High efficiency of Generative AI in combating advanced phishing attacks and deepfakes

As generative AI technologies advance, threat actors increasingly use them to develop sophisticated, AI-driven phishing campaigns and realistic deepfakes that traditional security solutions struggle to identify and fight. The increase in sophisticated, AI-enabled cyber threats is driving enterprises to implement generative AI solutions to improve their defensive capabilities. These solutions can proactively detect and respond to such assaults by automating threat intelligence, improving vulnerability management, and enabling real-time data analysis to quickly spot anomalies and potential breaches. The transition to generative AI for cybersecurity is also motivated by the need for more efficient and effective cybersecurity operations, particularly in light of the sector's growing skills gap and the increasing complexity of cyber threats.

Restraint: Concerns about AI governance and the risks associated with shadow IT

When it comes to generative AI cybersecurity market, there is one overwhelming challenge that needs to be addressed- concerns about AI governance and risks associated with shadow IT. Hence, effective AI governance is important for supervising, auditing, handling, and restricting the data managed by AI systems so as to prevent its misuse and guarantee compliance with stringent regulations. However, the rapid rise in popularity of generative AI has exacerbated the problem of shadow IT- where employees use or create technology without the knowledge or approval of cybersecurity teams. This can unwittingly introduce personal data into AI models thereby increasing data breaches and their misuses chances. For instance, surveys show that enterprises have experienced severe instances of shadow IT which underscore the need for strict governance measures and continuous employee training. The lack of oversight may result in major security flaws when deploying generative AI. In this case, robust AI governance and comprehensive education programs are key to securing application of AI technologies in cyber security.

Opportunity: Generative AI’s potential to address the persistent skills gap in the cybersecurity industry

The potential of generative AI cybersecurity to bridge the current skills gap in the cybersecurity sector represents an untapped opportunity. As generative AI continues to advance and automate several repetitive processes that were previously managed by human analysts, businesses may fill crucial cybersecurity positions with people who possess aptitude rather than extensive expertise or specialized training. This has been one of the key issues within this sector due to a shortage of skilled cybersecurity professionals. Industry surveys predict that by 2028 half of all entry-level cybersecurity positions will no longer require specialized training because generative AI will bring efficiencies to these roles. Additionally, it has been estimated that by 2026 when generative AI becomes involved, employee-driven cyber security incidents will decrease by 40% with personalized programs becoming more engaging and effective.

Challenge: Susceptibility of generative AI models to hijacking and data poisoning

One of the market challenges for generative AI cybersecurity is its susceptibility to be exploited by hackers. As generative AI grows more popular in cybersecurity, criminals are also quickly adopting these tools to improve their offensive operations. Criminals can employ generative AI for creating sophisticated phishing emails, deep fakes and other forms of social engineering that cannot be detected by traditional methods. Moreover, there has been a recent increase in cases whereby bad actors attempt to create or hijack harmful AI models with aims such as making malware invisible or using stolen data to conduct very selective attacks. This double use feature of generative AI poses a critical challenge requiring continuous improvements in defensive mechanisms against increasingly complex cyber threats.

Generative AI Cybersecurity Market Ecosystem

By offering, generative AI-powered cybersecurity software to account for the largest market share in 2024.

Transformative capabilities of the generative AI-powered cybersecurity software have rendered it as the leading segment in the market given that it has great transformative potential on threat detection, real-time response to threats and automation of complex security procedures. These highly sophisticated AI systems utilize large language models (LLMs) and machine learning algorithms for processing huge datasets, identifying exceptions as well as predicting future cyber threats at an unparallel accuracy and pace. Hence, security teams can identify and stop threats faster, reducing the time and resources required for manual surveillance and analysis activities. For instance, Microsoft has integrated generative AI with its security products, such as Microsoft Copilot for Security, which help organizations to streamline their workflows through natural language processing and provides insights that are actionable in terms of specific security needs?. Generative AI also improves deep data analytics, thereby improving detection rates for more advanced forms of cybercrime like phishing and ransomware attacks whose dynamics are ever changing?.?

By cybersecurity for generative AI, model security is slated to register the highest growth rate during the forecast period.

Generative AI model security software is the fastest-growing segment in generative AI cybersecurity market owing to its critical role in protecting AI models, especially LLMs/foundation models, from adversarial attacks and ensuring robustness. The need to protect these models from being exploited by cyber criminals has become more important as organizations increasingly deploy generative AI in diversified applications. Generative AI model security software are designed to secure threats including data poisoning, model theft, and adversarial attacks that can be used to manipulate model outputs and lead to acute damage. The surge in demand is also fueled by a rise in complex cyber threats targeting AI systems, and growing recognition by businesses of vulnerabilities specific for generative AI. Companies are heavily investing in top-notch security solutions to safeguard their AI assets thereby boosting the need for generative AI model security software.

By security type, database security is poised to grow at a substantial CAGR during the forecast period.

Due to the high requirement for protection of sensitive information from increasingly complex cyber threats, database security is becoming the fastest-growing segment in generative AI cybersecurity. As enormous quantities of data pass through generative AI applications, there has been a surge in data breaches, unauthorized access and data manipulation that require strong mechanisms of security. To counter this, enterprises are complementing their databases driven by generative AI. This is aimed at ensuring the confidentiality, integrity, and availability of their data. Additionally, organizations are now giving priority to database security due to increased regulatory requirements on data protection and privacy - like GDPR and CCPA, so as not to incur huge penalties or damage their reputation.

By end-user, generative AI providers are set to witness the fastest growth rate over the forecast period.

In the end-user segment, generative AI providers are rapidly growing due to unique threats and particular requirements in deploying large-scale AI models. These providers require robust cyber protection mechanisms as they have voluminous data that is used in training their sensitive and confidential AI models. Moreover, the complexity of the LLMs introduces new forms of attack vectors such as adversarial attacks and model theft which require advanced levels of security. In this regard, it is imperative for companies to ensure transparency and security of these models since this will be a part of trust building exercise between them and stakeholders. This also ensures compliance with regulations within specific sectors where generative AI finds use. Thus, businesses are increasingly investing in specialized artificial intelligence (AI) security solutions to protect their intellectual property while enhancing the durability of their AI infrastructure.

By region, Asia Pacific is set to experience the fastest growth rate during the forecast period.

Asia Pacific regional market is expanding at a generous rate. Its strong economic growth and extensive public and private investments into artificial intelligence research and development create conducive conditions for bringing out cutting-edge cyber-security provisions. Cyber criminals are increasingly resorting to generative-AI based attacks in this region, hence there is a more urgent need for advanced cyber security measures. The rate of incidence of cybercrime has increased significantly leading to countries such as India seeing an increase in the number of attacks, forcing them to enhance their cyber security systems. For instance, the Indian Computer Emergency Response Team (CERT-In) managed a substantial number of security incidents in recent years. Legislations like National Security Law and Cyber-Security Law that can improve national defense against cyberspace threats are helping China in strengthening its cybersecurity framework. Japan, on the other hand, has allocated vast resources towards boosting its cyber defense capacities.

Key Market Players

The generative AI cybersecurity solution and service providers have implemented several types of go-to-market strategies, such as new product launches, product upgrades, partnerships, and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. Some major players in the generative AI cybersecurity market include Palo Alto Networks (US), AWS (US), CrowdStrike (US), SentinelOne (US), and Google (US), along with SMEs and startups such as MOSTLY AI (Austria), XenonStack (UAE), BigID (US), Abnormal Security (US), and Adversa AI (Israel)

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Scope of the Report

This research report categorizes the generative AI cybersecurity market based on offering, generative AI-based cybersecurity, cybersecurity for generative AI, security type, end-user, and region:

By Offering:

• Software
  • Software, By Type
    • Generative AI-based Cybersecurity Solutions
    • Cybersecurity Solutions For Generative AI
  • Software, By Deployment Mode
    • Cloud
    • On-Premises
• Services
  • Professional Services
    • Training & Consulting Services
    • System Integration & Deployment Services
    • Support & Maintenance Services
  • Managed Services

By Generative AI-based Cybersecurity Solutions

• Threat Detection & Intelligence Software
  • Automated Threat Analysis
  • Security Information & Event Management (SIEM)
  • AI-native Security Analysis
  • Threat Correlation
  • Threat Intelligence
• Risk Assessment Software
  • Automated Risk Insights
  • Impact Analysis
  • Risk Intelligence
  • Compliance Automation
  • Others
• Exposure Management Software
  • Vulnerability Analysis
  • Exposure Prioritization
  • Automated Exposure Detection
  • Incident Response
  • Others
• Phishing Simulation & Prevention Software
  • Phishing Simulation Campaigns
  • Phishing Attack Analysis
  • Deepfake Detection
  • Fraud Prevention
  • Social Engineering Detection
• Remediation Guidance Software
  • Automated Remediation
  • Interactive Remediation Support
  • Proactive Threat Management
  • Compliance Remediation
  • Others
• Threat Hunting Platforms
  • Real-time Threat Analysis
  • Natural Language Query Interface
  • Behavior Analysis
  • Response Automation
  • Others
• Code Analysis software
  • Code Snippet Analysis
  • Source Code Protection
  • Vulnerability Detection
  • Automated Code Review
  • Compliance Checks

By Cybersecurity Solutions for Generative AI

• Generative AI Training Data Security Software
  • Data Integrity Verification
  • Secure Data Augmentation
  • Automated Data Cleaning
  • Data Quality Monitoring
  • Data Anonymization
• Generative AI Model Security Software
  • Model Integrity
  • Impact Analysis
  • Adversarial Training & Testing
  • Secure Model Training Environments
  • Model Drift & Bias Detection
  • Robustness Testing
• Generative AI Infrastructure Security Software
  • Continuous Monitoring
  • Automated Security Patching
  • Secure API Management
  • Real-Time Threat Detection
  • Security Audits
• Generative AI Application Security Software
  • Prompt Injection Security
  • Data Leakage Prevention
  • User Authentication & Access Control
  • Monitoring & Anomaly Detection
  • Ethical AI Governance

By Security Type

• Generative AI Training Data Security Software
  • Data Loss Prevention (DLP)
  • Data Usage Monitoring
  • Data Compliance & Governance
  • Data Encryption
  • Data Masking & Tokenization
  • Access Control
• Generative AI Model Security Software
  • Network Traffic Analysis
  • Secure Access Service Edge (SASE)
  • Zero Trust Network Access
  • Firewalls
  • Intrusion Detection/Prevention Systems (IDS/IPS)
  • VPN & Secure Tunneling
• Generative AI Infrastructure Security Software
  • Endpoint Detection & Response (EDR)
  • Endpoint Protection Platforms
• Generative AI Application Security Software
  • Static Application Security Testing (SAST)
  • Dynamic Application Security Testing (DAST)
  • LLM Security
  • Runtime Protection
  • Incident Response & Recovery
  • Governance, Risk, & Compliance (GRC)

By End-user

• Generative AI-based Cybersecurity End-Users
  • BFSI
  • IT & ITeS
  • Telecommunications
  • Government & Defense
  • Healthcare & Life Sciences
  • Manufacturing
  • Media & Entertainment
  • Retail & E-Commerce
  • Energy & Utilities
  • Automotive, Transportation & Logistics
  • Other Enterprises
• Cybersecurity for Generative AI End-Users
  • Cloud Hyper scalers
  • Managed Security Service Providers
  • Generative AI Providers
    • Foundation Model/LLM Developers
    • Data Annotators
    • Content Creation Platform Providers
    • Generative AI-as-a-Service Providers

By Region

• North America
  • United States
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Netherlands
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Singapore
  • Australia and New Zealand (ANZ)
  • Rest of Asia Pacific
• Middle East and Africa
  • Saudi Arabia
  • UAE
  • Turkey
  • Qatar
  • Rest of Middle East
  • Africa
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

Recent Developments:

• In June 2024, SentinelOne and Advantage entered into a partnership, wherein Advantage will integrate SentinelOne’s PurpleAI, one of the leading generative AI powered security tools, into its cutting-edge managed detection and response (MDR) service, empowering organizations of all sizes to defend against AI-driven attacks with AI-driven solutions.
• In May 2024, Palo Alto Networks and IBM announced a broad-reaching partnership to deliver AI-powered cybersecurity outcomes for customers. The announcement is a testament to Palo Alto Networks' and IBM's commitment to each other's platforms and innovative capabilities.
• In May 2024, CrowdStrike announced an expanded strategic partnership with Google Cloud to power Mandiant’s Incident Response (IR) and Managed Detection and Response (MDR) services leveraging the CrowdStrike Falcon platform and the Google Cloud Security Operations platform. The partnership focuses on CrowdStrike’s market-leading Endpoint Detection and Response (EDR), Identity Threat Detection and Response (ITDR) and Exposure Management solutions.
• In April 2024, Microsoft launched its Copilot for Security. The solution will help security and IT professionals catch what others miss, move faster, and strengthen team expertise. Copilot is informed by large-scale data and threat intelligence, including more than 78 trillion security signals processed by Microsoft each day, and coupled with large language models to deliver tailored insights and guide next steps.
• In May 2023, Amazon Web Services (AWS) partnered with CrowdStrike to develop powerful new generative AI applications that help customers accelerate their cloud, security, and artificial intelligence (AI) journeys. These include both cybersecurity-related generative AI applications, as well as cloud-plus-cloud security solutions designed to help customers build and secure their own generative AI applications.
• In April 2023, Google announced Cloud Security AI Workbench, a cybersecurity suite powered by a specialized security AI language model called Sec-PaLM. An offshoot of Google’s PaLM model, Sec-PaLM is fine-tuned for security use cases— incorporating security intelligence such as research on software vulnerabilities, malware, threat indicators and behavioral threat actor profiles.

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