Generative AI Cybersecurity Market Size, Share and Global Market Forecast to 2030

Overview

The generative AI cybersecurity market is undergoing rapid expansion, with estimates projecting a substantial market value surge from USD 8.65 billion in 2025 to USD 35.50 billion by 2031, at a CAGR of 26.5% during the forecast period. One of the biggest forces driving the market is the rise of task-executing AI agents, which can autonomously perform actions but require strong runtime guardrails, policy checks, and isolation to prevent misuse or unintended harm. The IBM Cost of a Data Breach Report 2025 found that 13% of surveyed global organizations reported breaches of AI models or applications, and 60% of those breaches led to compromised data, while 31% caused operational disruption. Organizations dealing with "shadow AI" unsanctioned AI tools faced an average added cost of USD 670,000 per breach compared to those with low or no shadow AI usage. At the same time, sensitive data leakage through prompts, model memory, retrieval-augmented generation (RAG) sources, or generated outputs has become a critical concern, amplifying the need for advanced data loss prevention (DLP) tailored for AI workflows. Additionally, sophisticated AI-native attack techniques, including prompt injection, jailbreaks, indirect prompt leaks, and model poisoning, are pushing demand for specialized, purpose-built defenses that go beyond traditional cybersecurity tools.

Attractive Opportunities in the Generative AI Cybersecurity Market

ASIA PACIFIC

The Asia Pacific generative AI cybersecurity market is projected to record the highest CAGR during the forecast period. As AI technologies spread quickly, cyber threats are becoming more advanced. Investments in generative AI-powered cybersecurity solutions are rising, driven by the urgent need to secure sensitive data and AI workloads from sophisticated attacks. Close cooperation between governments and private players is strengthening efforts to build robust security frameworks.

In Asia Pacific, industries like finance, healthcare, and government are quickly adopting generative AI-based security tools for better threat detection and faster response. Government agencies are using AI-driven measures to protect critical infrastructure and public services from cyberattacks.

Generative AI cybersecurity solutions deliver both protection and operational efficiency. AI-driven security automates incident response, reduces detection time, and minimizes disruption to critical services. In Asia Pacific’s competitive business environment, this enables organizations to secure sensitive data, meet compliance requirements, and maintain customer trust.

Cybersecurity plays a key role in protecting generative AI by ensuring the integrity, confidentiality, and availability of AI models and training data, making sure they are not tampered with or stolen by attackers.

Asia Pacific offers strong revenue potential due to its combination of high digital adoption rates and rising cyber threat levels. The increasing complexity of AI systems has created demand for niche, high-value solutions such as federated learning security, adversarial attack detection, and secure model deployment.

Global Generative AI Cybersecurity Market Dynamics

Driver: Driving Cyber Resilience through AI-powered SOC Efficiency

Operational efficiency gains through AI-assisted Security Operations Centers (AI-SOC) are becoming a pivotal growth driver in the generative AI cybersecurity market, as enterprises increasingly prioritize faster detection, investigation, and remediation of advanced threats. The integration of generative AI into SOC workflows enables dynamic threat modeling, automated triage, and context-rich incident analysis, reducing manual workload and accelerating decision-making across complex security environments. Vendors are embedding AI-driven orchestration and adaptive playbooks to optimize analyst productivity, while cloud-native deployments allow scalability across global operations. The ability to process vast multi-source telemetry in real time, coupled with predictive analytics, positions AI-SOC as a strategic asset for minimizing dwell time and mitigating business risk. Organizations adopting these capabilities report measurable improvements in operational KPIs such as mean time to detect and mean time to respond, strengthening both compliance alignment and cyber resilience. Competitive dynamics are intensifying as leading security providers leverage proprietary AI models, data network effects, and integration ecosystems to differentiate their offerings, creating high barriers to entry. This shift is aligning investment priorities toward AI-first SOC transformation, where early adopters secure long-term advantages in threat intelligence quality, resource optimization, and cost efficiency. The resulting market momentum underscores AI-SOC innovation as a cornerstone of sustainable value creation in generative AI cybersecurity.

Restraint: Balancing AI Accuracy with Explainability in Security Decisions

The limited transparency and explainability of large language models in security decision-making remain a critical restraint shaping the growth trajectory of the generative AI cybersecurity market. While these models deliver unmatched capabilities in detecting anomalies, predicting attack patterns, and automating response workflows, their opaque decision logic often poses trust and compliance barriers in regulated sectors such as banking, healthcare, and government. Security leaders face mounting pressure to justify AI-driven actions to auditors, regulators, and internal governance boards, especially when high-stakes incidents involve data privacy, civil liberties, or operational continuity. This opacity complicates root cause analysis, incident forensics, and risk attribution, slowing organizational adoption and investment velocity. Vendors are exploring explainable AI frameworks, model auditing protocols, and secure AI governance toolkits to address these limitations, yet widespread integration remains nascent. In competitive landscapes where decision latency can mean significant financial and reputational loss, the ability to pair advanced AI capabilities with interpretable outputs will define differentiation. As enterprises increasingly prioritize transparency alongside accuracy, market participants that can operationalize explainability without sacrificing performance will capture a decisive share of emerging growth opportunities.

Opportunity: Proactive Defense with GenAI-Powered Penetration Testing

The development of AI-driven penetration testing and vulnerability assessment platforms represents a high-value growth opportunity in the generative AI cybersecurity market. Advances in generative models enable automated identification and exploitation simulation of system weaknesses at a speed and scale unattainable through manual red teaming. AI-powered engines can analyze complex, multi-layered infrastructure, generate adaptive attack scenarios, and prioritize vulnerabilities based on exploitability and business impact, significantly accelerating remediation cycles. By integrating natural language processing and contextual reasoning, these platforms can also translate technical findings into actionable security recommendations, bridging gaps between security operations and executive decision-making. As enterprise attack surfaces expand with cloud-native architectures, IoT deployments, and API-driven ecosystems, the ability to continuously simulate and stress-test defenses in real time becomes a critical differentiator. Vendors who integrate these capabilities into Security Operations Center (SOC) workflows and compliance reporting frameworks can address both operational risk reduction and regulatory readiness. This convergence of AI-driven automation with advanced security analytics positions penetration testing platforms not only as a cost-saving measure but as a strategic enabler for proactive cyber resilience, allowing security teams to stay ahead of increasingly sophisticated threats.

Challenge: Threat of Prompt Injection Undermining AI Model Integrity

Prompt injection and model manipulation techniques are emerging as a significant restraint in the generative AI cybersecurity market, as adversaries exploit weaknesses in large language model (LLM) prompt processing to bypass embedded safeguards. These attacks involve injecting malicious instructions into prompts or inputs to alter the intended output, potentially enabling the extraction of sensitive information, the manipulation of security policies, or the generation of unauthorized responses. The sophistication of such exploits is increasing, with attackers leveraging multi-turn conversations, context poisoning, and hidden command triggers to evade detection. In high-stakes environments such as financial fraud detection, healthcare diagnostics, and secure communications, successful prompt manipulation can compromise decision-making integrity and create regulatory liabilities. The lack of standardized guardrail testing frameworks and insufficient interpretability tools for LLMs further complicates mitigation efforts, slowing enterprise adoption in security-critical sectors. Vendors are beginning to invest in fine-tuning guardrails, adversarial training, and layered prompt filtering, yet the rapidly evolving nature of these attack vectors means defensive measures must be continuously adapted. This evolving threat landscape places a premium on proactive threat modeling, red teaming of AI systems, and real-time anomaly detection within LLM interactions to maintain operational trust and compliance in mission-critical deployments.

Global Generative AI Cybersecurity Market Ecosystem Analysis

The generative AI cybersecurity ecosystem shows how different companies are working to protect systems, data, and applications using the power of generative AI. It brings together three main groups: those creating AI-based cybersecurity tools, those securing AI systems themselves, and those offering AI-powered security services. The ecosystem also maps key players based on different types of security, such as database, network, endpoint, and application security. This gives a clear view of how big tech companies and cybersecurity specialists are combining AI with advanced protection methods to tackle new and evolving cyber threats.

Top Companies in Generative AI Cybersecurity Market

Source: Secondary Research, Interviews with Experts, and MarketsandMarkets Analysis

In terms of cybersecurity software for generative AI, the generative AI model security software segment is expected to register the highest growth rate during the forecast period

The demand for generative AI model security software is growing in the generative AI cybersecurity market due to its crucial role in protecting high-value AI assets such as LLMs and foundation models. As organizations rapidly integrate generative AI into business workflows, the risk of exploitation through adversarial attacks, data poisoning, and model theft has increased sharply. This has made robust model security not just a technical requirement, but a strategic priority. AI-powered intrusion detection systems, real-time model behavior monitoring, and adversarial training techniques are improving the resilience of generative AI models against sophisticated threats. Encryption-based model protection, secure federated learning, and privacy-preserving synthetic data generation are also being adopted to minimize exposure to malicious actors. Strategically, businesses are recognizing that compromised AI models can cause significant financial and reputational damage. As a result, they are investing in multi-layered defenses that combine proactive threat intelligence, automated response systems, and secure data pipelines. The adoption of security-by-design approaches, embedding protection measures into the AI lifecycle from development to deployment, is gaining momentum. The increasing sophistication of AI-driven cyberattacks, combined with the rising economic value of generative AI models, ensures that model security will remain a top investment priority for enterprises and a high-growth opportunity for vendors in the years ahead.

By security type, the database security is projected to witness a substantial CAGR during the forecast period.

Database security is emerging as one of the fastest-growing segments in the generative AI cybersecurity market due to the escalating need to protect sensitive information from increasingly sophisticated cyber threats. With generative AI systems processing massive volumes of structured and unstructured data, the risks of data breaches, unauthorized access, and malicious data manipulation have surged. This makes robust database protection an enterprise priority. AI-powered anomaly detection, real-time access monitoring, and zero-trust database architectures are being implemented to identify suspicious activity before damage occurs. Encryption-at-rest and in-transit, combined with homomorphic encryption, are strengthening confidentiality without compromising performance. Additionally, secure multi-party computation and blockchain-backed audit trails enable verifiable data integrity and tamper resistance. From a strategic perspective, organizations are prioritizing database security to ensure compliance with stringent regulations such as GDPR, CCPA, and emerging AI governance frameworks. Failing to meet these requirements can lead to severe financial penalties and reputational harm. Businesses are also adopting database security as part of a broader “security-by-design” approach, embedding safeguards into AI pipelines from data ingestion to model training. As generative AI adoption accelerates across industries, the critical role of secure, resilient, and regulation-compliant databases will continue to make database security a high-growth opportunity for cybersecurity vendors.

North America is estimated to be the largest regional market in 2025

North America is estimated to lead the generative AI cybersecurity market in 2025, driven by its advanced technology ecosystem, strong regulatory framework, and early adoption of AI-driven security solutions. The region is home to several leading AI and cybersecurity innovators, including Microsoft, IBM, and Palo Alto Networks, which are actively integrating generative AI into threat detection, incident response, and predictive risk analysis. The region benefits from widespread deployment of cloud infrastructure, high-speed connectivity, and sophisticated AI research hubs, enabling rapid integration of AI-powered cybersecurity tools into enterprise workflows. Furthermore, collaboration between tech giants, startups, and government agencies accelerates innovation in database security, identity protection, and AI-powered anomaly detection. Regulatory developments such as the California Consumer Privacy Act (CCPA), various state-level AI governance bills, and federal initiatives on AI risk management are pushing organizations to adopt more robust, compliant AI security measures. These regulations, combined with the rising frequency and complexity of cyberattacks in sectors like BFSI, healthcare, and government, are making security investments a strategic priority. Additionally, high digital adoption rates, coupled with a skilled cybersecurity workforce, position North America as a leader in implementing next-gen AI security solutions.

LARGEST REGION BY MARKET SHARE IN 2025

CANADA: FASTEST-GROWING MARKET IN REGION

Generative AI Cybersecurity Market by region

Recent Developments of Generative AI Cybersecurity Market

In August 2025, SentinelOne announced the acquisition of Prompt Security to strengthen its AI-native Singularity Platform. This move enhances real-time visibility and provides advanced security controls for generative AI and agentic AI workloads in enterprises. By integrating Prompt Security’s expertise, SentinelOne aims to deliver more proactive threat detection, intelligent incident response, and improved security posture for AI-driven business environments.

In July 2025, Accenture and Microsoft expanded their strategic partnership to jointly invest in generative AI-powered cybersecurity solutions. The collaboration focuses on accelerating Security Operations Center (SOC) modernization, deploying AI-driven threat analysis, and enabling automated security workflows. This initiative is designed to help enterprises detect and mitigate cyber threats faster, while enhancing operational efficiency across complex IT and AI environments.

In July 2025, Palo Alto Networks completed its acquisition of Protect AI to bolster its generative AI cybersecurity capabilities. This acquisition aims to accelerate AI integration into Palo Alto’s Security Operating Platform, strengthening defenses against AI-specific threats. The combined expertise will allow enterprises to secure AI models, protect training data, and detect malicious activity targeting AI systems more effectively.

In July 2025, CrowdStrike and NVIDIA entered into a collaboration to integrate GPU-optimized AI pipelines with large language models for enhanced cybersecurity. This partnership will enable faster AI-driven threat detection, real-time anomaly recognition, and accelerated response times. Leveraging NVIDIA’s GPU performance and CrowdStrike’s threat intelligence, the solution aims to address the growing scale and complexity of modern cyberattacks.

Key Market Players

List of Top Generative AI Cybersecurity Market Companies

The Generative AI Cybersecurity Market is dominated by a few major players that have a wide regional presence. The major players in the Generative AI Cybersecurity Market are

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Scope of the Report

Report Attribute	Details
Market size available for years	2020–2031
Base year considered	2024
Forecast period	2025–2031
Forecast units	USD (Million)
Segments Covered	Offering, Generative AI-based Cybersecurity Software, Cybersecurity Software for Generative AI, Security Type, End User, and Region
Regions covered	North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Key Questions Addressed by the Report

What is generative AI cybersecurity?

Generative AI cybersecurity leverages cybersecurity tools & technologies deployed to secure generative AI systems, while also accounting for the impact of generative AI-enabled tools for cybersecurity practices. The scope of the generative AI cybersecurity market includes two key angles:

Cybersecurity with generative AI: investigates how generative AI techniques are transforming the protection of enterprise security architecture, apps, networks, and data. Robust real-time anomaly detection, enhanced threat identification, and advanced threat hunting are all made possible by cybersecurity enhanced by generative AI.
Cybersecurity for generative AI: focuses on implementing advanced cybersecurity technologies, tools, policies, and practices specifically designed to protect generative AI from threats and vulnerabilities. This involves strategies such as adversarial machine learning, differential privacy, federated learning security, and secure model deployment, aimed at safeguarding data, AI models, and deployment environments.

What is the total CAGR expected to be recorded for the generative AI cybersecurity market during 2025-2031?

The generative AI cybersecurity market is expected to record a CAGR of 26.5% from 2025 to 2031.

How are generative AI and cybersecurity amalgamating into a converged technology?

Generative AI significantly enhances cybersecurity by enabling advanced threat detection and proactive defense mechanisms through real-time analysis and anomaly detection. It aids in automating and optimizing responses to cyber incidents, reducing the time and resources needed for manual intervention. On the other hand, cybersecurity significantly impacts generative AI by safeguarding the integrity, confidentiality, and availability of AI models and training data, ensuring they are not tampered with or stolen by malicious actors?.

What are the key drivers supporting the growth of the generative AI cybersecurity market?

The key factors driving the growth of the generative AI cybersecurity market include increased adoption of generative AI, which drives demand for cybersecurity solutions; rising awareness regarding the efficiency of generative AI in threat detection; stricter data regulations and compliance laws, which fuel demand for secure AI systems; and the rise of novel generative AI-based cyber threats, which fuels demand for next-gen cybersecurity solutions.

Which are the top end-users prevailing in the generative AI cybersecurity market?

The leading end-users in terms of generative AI-based cybersecurity deployment include BFSI, healthcare, and government & defense. The top end-users of cybersecurity solutions for securing generative AI encompass cloud hyper scalers, managed security service providers, and generative AI providers.

Who are the key vendors in the generative AI cybersecurity market?

Some major players in the Generative AI cybersecurity market include:
Generative AI-based Cybersecurity Vendors: Microsoft (US), IBM (US), Google (US), SentinelOne (US), NVIDIA (US), Cisco (US), CrowdStrike (US), Fortinet (US), Zscaler (US), Trend Micro (Japan), Palo Alto Networks (US), BlackBerry (Canada), Darktrace (UK), Okta (US), Sangfor (China), SecurityScorecard (US), Sophos (UK), Broadcom (US), Trellix (US), Veracode (US), LexisNexis (US), Abnormal Security (US), Aquasec (US), BigID (US), Checkmarx (US), Cohesity (US), NeuralTrust

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TITLE

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INTRODUCTION

1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION

INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE

MARKET SEGMENTATION

REGIONS COVERED

YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES

RESEARCH METHODOLOGY

2.1 RESEARCH DATA

SECONDARY DATA

- MAJOR SECONDARY SOURCES

- KEY DATA FROM SECONDARY SOURCES

PRIMARY DATA

- KEY DATA FROM PRIMARY SOURCES

- BREAKUP OF PRIMARY PROFILES

- KEY INDUSTRY INSIGHTS
2.2 MARKET BREAKUP AND DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION

TOP-DOWN APPROACH

BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 ASSUMPTIONS FOR THE STUDY
2.6 LIMITATIONS OF THE STUDY

EXECUTIVE SUMMARY

PREMIUM INSIGHTS

4.1 ATTRACTIVE MARKET OPPORTUNITIES IN GENERATIVE AI CYBERSECURITY MARKET
4.2 GENERATIVE AI CYBERSECURITY MARKET: TOP THREE GENERATIVE AI-BASED CYBERSECURITY SOFTWARE
4.3 NORTH AMERICA: GENERATIVE AI CYBERSECURITY MARKET, BY SECURITY TYPE AND END USER
4.4 GENERATIVE AI CYBERSECURITY MARKET, BY REGION

MARKET OVERVIEW AND INDUSTRY TRENDS (STRATEGIC DRIVERS WITH QUANTITATIVE IMPLICATIONS)

5.1 INTRODUCTION
5.2 MARKET DYNAMICS

DRIVERS

RESTRAINTS

OPPORTUNITIES

CHALLENGES
5.3 EVOLUTION OF GENERATIVE AI CYBERSECURITY
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.6 INVESTMENT AND FUNDING SCENARIO
5.7 CASE STUDY ANALYSIS
5.8 TECHNOLOGY ANALYSIS

KEY TECHNOLOGIES

- ADVERSARIAL MACHINE LEARNING (AML)

- FEDERATED LEARNING

- DIFFERENTIAL PRIVACY

- HOMOMORPHIC ENCRYPTION

- SECURE MULTI-PARTY COMPUTATION (SMPC)

COMPLEMENTARY TECHNOLOGIES

- BLOCKCHAIN

- ZERO-TRUST ARCHITECTURE (ZTA)

- ENDPOINT DETECTION AND RESPONSE (EDR)

- VULNERABILITY MANAGEMENT

ADJACENT TECHNOLOGIES

- QUANTUM COMPUTING

- DEVSECOPS

- FORENSICS AND INCIDENT RESPONSE

- BIG DATA ANALYTICS
5.9 REGULATORY LANDSCAPE

REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

KEY REGULATIONS

PATENT ANALYSIS

- METHODOLOGY

- PATENTS FILED, BY DOCUMENT TYPE, 2016–2025

- INNOVATION AND PATENT APPLICATIONS

- TOP APPLICANTS

PRICING ANALYSIS

- AVERAGE SELLING PRICE OF SOFTWARE TYPES, BY KEY PLAYERS, 2025

- AVERAGE SELLING PRICE, BY OFFERING, 2025

KEY CONFERENCES AND EVENTS (2025-2026)

PORTER’S FIVE FORCES ANALYSIS

- THREAT OF NEW ENTRANTS

- THREAT OF SUBSTITUTES

- BARGAINING POWER OF SUPPLIERS

- BARGAINING POWER OF BUYERS

- INTENSITY OF COMPETITION RIVALRY

KEY STAKEHOLDERS AND BUYING CRITERIA

- KEY STAKEHOLDERS IN BUYING PROCESS

- BUYING CRITERIA

TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESS

GENERATIVE AI CYBERSECURITY MARKET, BY OFFERING (MARKET SIZE & FORECAST TO 2031 – IN VALUE (USD))

6.1 INTRODUCTION

OFFERING: GENERATIVE AI CYBERSECURITY MARKET DRIVERS
6.2 SOFTWARE, BY TYPE

GENERATIVE AI-BASED CYBERSECURITY SOFTWARE

CYBERSECURITY SOFTWARE FOR GENERATIVE AI
6.3 SOFTWARE, BY DEPLOYMENT MODE

CLOUD

ON-PREMISES
6.4 SERVICES

PROFESSIONAL SERVICES

- TRAINING & CONSULTING SERVICES

- SYSTEM INTEGRATION & IMPLEMENTATION SERVICES

- SUPPORT & MAINTENANCE SERVICES

MANAGED SERVICES

GENERATIVE AI CYBERSECURITY MARKET, BY GENERATIVE AI-BASED CYBERSECURITY SOFTWARE (MARKET SIZE & FORECAST TO 2031 – IN VALUE (USD))

7.1 INTRODUCTION

GENERATIVE AI-BASED CYBERSECURITY SOFTWARE: GENERATIVE AI CYBERSECURITY MARKET DRIVERS
7.2 THREAT DETECTION & INTELLIGENCE SOFTWARE

AUTOMATED THREAT ANALYSIS

SECURITY INFORMATION & EVENT MANAGEMENT (SIEM)

AI-NATIVE SECURITY ANALYSIS

THREAT CORRELATION

THREAT INTELLIGENCE
7.3 RISK ASSESSMENT SOFTWARE

AUTOMATED RISK INSIGHTS

IMPACT ANALYSIS

RISK INTELLIGENCE

COMPLIANCE AUTOMATION

OTHER RISK ASSESSMENT SOFTWARE
7.4 EXPOSURE MANAGEMENT SOFTWARE

VULNERABILITY ANALYSIS

EXPOSURE PRIORITIZATION

AUTOMATED EXPOSURE DETECTION

INCIDENT RESPONSE

OTHER EXPONENTIAL MANAGEMENT SOFTWARE
7.5 PHISHING SIMULATION & PREVENTION SOFTWARE

PHISHING SIMULATION CAMPAIGNS

PHISHING ATTACK ANALYSIS

DEEPFAKE DETECTION

FRAUD PREVENTION

SOCIAL ENGINEERING DETECTION
7.6 REMEDIATION GUIDANCE SOFTWARE

AUTOMATED REMEDIATION

INTERACTIVE REMEDIATION SUPPORT

PROACTIVE THREAT MANAGEMENT

COMPLIANCE REMEDIATION

OTHER REMEDIATION GUIDANCE SOFTWARE
7.7 THREAT HUNTING PLATFORMS

REAL-TIME THREAT ANALYSIS

NATURAL LANGUAGE QUERY INTERFACE

BEHAVIOR ANALYSIS

RESPONSE AUTOMATION

OTHER THREAT HUNTING PLATFORMS
7.8 CODE ANALYSIS SOFTWARE

CODE SNIPPET ANALYSIS

SOURCE CODE PROTECTION

VULNERABILITY DETECTION

AUTOMATED CODE REVIEW

COMPLIANCE CHECKS

GENERATIVE AI CYBERSECURITY MARKET, BY CYBERSECURITY SOFTWARE FOR GENERATIVE AI (MARKET SIZE & FORECAST TO 2031 – IN VALUE (USD))

8.1 INTRODUCTION

CYBERSECURITY SOFTWARE FOR GENERATIVE AI: GENERATIVE AI CYBERSECURITY MARKET DRIVERS
8.2 GENERATIVE AI TRAINING DATA SECURITY SOFTWARE

DATA INTEGRITY VERIFICATION

SECURE DATA AUGMENTATION

AUTOMATED DATA CLEANING

DATA QUALITY MONITORING

DATA ANONYMIZATION
8.3 GENERATIVE AI MODEL SECURITY SOFTWARE

MODEL INTEGRITY

ADVERSARIAL TRAINING & TESTING

SECURE MODEL TRAINING ENVIRONMENTS

MODEL DRIFT & BIAS DETECTION

ROBUSTNESS TESTING
8.4 GENERATIVE AI INFRASTRUCTURE SECURITY SOFTWARE

CONTINUOUS MONITORING

AUTOMATED SECURITY PATCHING

SECURE API MANAGEMENT

REAL-TIME THREAT DETECTION

SECURITY AUDITS
8.5 GENERATIVE AI APPLICATION SECURITY SOFTWARE

PROMPT INJECTION SECURITY

DATA LEAKAGE PREVENTION

USER AUTHENTICATION & ACCESS CONTROL

MONITORING & ANOMALY DETECTION

ETHICAL AI GOVERNANCE

GENERATIVE AI CYBERSECURITY MARKET, BY SECURITY TYPE (MARKET SIZE & FORECAST TO 20301– IN VALUE (USD))

9.1 INTRODUCTION

SECURITY TYPE: GENERATIVE AI CYBERSECURITY MARKET DRIVERS
9.2 DATABASE SECURITY

DATA LOSS PREVENTION (DLP)

DATA USAGE MONITORING

DATA COMPLIANCE & GOVERNANCE

DATA ENCRYPTION

DATA MASKING & TOKENIZATION

ACCESS CONTROL
9.3 NETWORK SECURITY

NETWORK TRAFFIC ANALYSIS (NTA)

SECURE ACCESS SERVICE EDGE (SASE)

ZERO TRUST NETWORK ACCESS (ZTNA)

FIREWALLS

INTRUSION DETECTION/PREVENTION SYSTEMS (IDS/IPS)

VPNS & SECURE TUNNELING
9.4 ENDPOINT SECURITY

ENDPOINT DETECTION & RESPONSE (EDR)

ENDPOINT PROTECTION PLATFORMS (EPP)
9.5 APPLICATION SECURITY

STATIC APPLICATION SECURITY TESTING (SAST)

DYNAMIC APPLICATION SECURITY TESTING (DAST)

LLM SECURITY

RUNTIME PROTECTION

INCIDENT RESPONSE & RECOVERY

GOVERNANCE, RISK, AND COMPLIANCE (GRC)

GENERATIVE AI CYBERSECURITY MARKET, BY END USER (MARKET SIZE & FORECAST TO 2030 – IN VALUE (USD))

GENERATIVE AI CYBERSECURITY MARKET, BY END USER (MARKET SIZE & FORECAST TO 2030 – IN VALUE (USD))

10.1 INTRODUCTION

END USER: GENERATIVE AI CYBERSECURITY MARKET DRIVERS
10.2 END USERS: GENERATIVE AI-BASED CYBERSECURITY

GOVERNMENT & DEFENSE

BFSI

IT/ITES

HEALTHCARE & LIFE SCIENCES

RETAIL & ECOMMERCE

MANUFACTURING

ENERGY & UTILITIES

TELECOMMUNICATIONS

AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS

- MEDIA & ENTERTAINMENT

- OTHER END USERS
10.3 END USERS: CYBERSECURITY FOR GENERATIVE AI

CLOUD HYPERSCALERS

MANAGED SECURITY SERVICE PROVIDERS

GENERATIVE AI PROVIDERS

- FOUNDATION MODEL/LLM DEVELOPERS

- DATA ANNOTATORS

- CONTENT CREATION PLATFORM PROVIDERS

- GENERATIVE AI-AS-A-SERVICE PROVIDER

GENERATIVE AI CYBERSECURITY MARKET, BY REGION (MARKET SIZE & FORECAST TO 2031 – IN VALUE (USD))

11.1 INTRODUCTION
11.2 NORTH AMERICA

NORTH AMERICA: GENERATIVE AI CYBERSECURITY MARKET DRIVERS

NORTH AMERICA: MACROECONOMIC OUTLOOK

US

CANADA
11.3 EUROPE

EUROPE: GENERATIVE AI CYBERSECURITY MARKET DRIVERS

EUROPE: MACROECONOMIC OUTLOOK

UK

GERMANY

FRANCE

REST OF EUROPE
11.4 ASIA PACIFIC

ASIA PACIFIC: GENERATIVE AI CYBERSECURITY MARKET DRIVERS

ASIA PACIFIC: MACROECONOMIC OUTLOOK

CHINA

INDIA

JAPAN

REST OF ASIA PACIFIC
11.5 MIDDLE EAST AND AFRICA

MIDDLE EAST AND AFRICA: GENERATIVE AI CYBERSECURITY MARKET DRIVERS

MIDDLE EAST AND AFRICA: MACROECONOMIC OUTLOOK

SAUDI ARABIA

UAE

REST OF MIDDLE EAST

AFRICA
11.6 LATIN AMERICA

LATIN AMERICA: GENERATIVE AI CYBERSECURITY MARKET DRIVERS

LATIN AMERICA: MACROECONOMIC OUTLOOK

BRAZIL

MEXICO

REST OF LATIN AMERICA

COMPETITIVE LANDSCAPE

12.1 INTRODUCTION
12.2 KEY PLAYERS STRATEGIES, 2022–2025
12.3 REVENUE ANALYSIS, 2024
12.4 MARKET SHARE ANALYSIS, 2024
12.5 PRODUCT COMPARISON
12.6 COMPANY VALUATION AND FINANCIAL METRICS
12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024

STARS

EMERGING LEADERS

PERVASIVE PLAYERS

PARTICIPANTS

COMPANY FOOTPRINT: KEY PLAYERS, 2024

- COMPANY FOOTPRINT

- REGION FOOTPRINT

- OFFERING FOOTPRINT

- SECURITY TYPE FOOTPRINT

- END USER FOOTPRINT
12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024

PROGRESSIVE COMPANIES

RESPONSIVE COMPANIES

DYNAMIC COMPANIES

STARTING BLOCKS

COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024

- DETAILED LIST OF KEY STARTUPS/SMES

- COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
12.9 COMPETITIVE SCENARIO

PRODUCT LAUNCHES AND ENHANCEMENTS

DEALS

COMPANY PROFILES

13.1 INTRODUCTION
13.2 KEY PLAYERS

MICROSOFT

IBM

AWS

GOOGLE

SENTITELONE

NVIDIA

CISCO

CROWDSTRIKE

FORTINET

- ZSCALER

- TREND MICRO

- PALO ALTO NETWORKS

- BLACKBERRY

- DARKTRACE

- F5

- OKTA

- SANGFOR TECHNOLOGIES

- VERACODE

- LEXISNEXIS

- SECURITYSCORECARD

- SOPHOS

- BROADCOM

- TRELLIX

- TENABLE

- COHESITY

- ELASTIC NV

- SNYK
13.3 SMES/START-UPS

NEURALTRUST

ABNORMAL SECURITY

ADVERSA AI

AQUASEC

BIGID

CHECKMARX

CREDO AI

CYBEREASON

DEEPKEEP

- FLASHPOINT

- LAKERA

- MOSTLY AI

- RECORDED FUTURE

- SECUREFRAME

- SKYFLOW

- SLASHNEXT

- TROJAI

- VIRUSTOTAL

- XENONSTACK

- ZEROFOX

ADJACENT AND RELATED MARKETS

14.1 INTRODUCTION
14.2 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2032

MARKET DEFINITION

MARKET OVERVIEW
14.3 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET – GLOBAL FORECAST TO 2028

MARKET DEFINITION

MARKET OVERVIEW

APPENDIX

15.1 DISCUSSION GUIDE
15.2 KNOWLEDGESTORE: MARKETANDMARKETS’ SUBSCRIPTION PORTAL
15.3 CUSTOMIZATIONS OPTIONS
15.4 RELATED REPORTS
15.5 AUTHOR DETAILS

This research study on the generative AI cybersecurity market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred generative AI cybersecurity providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews with primary respondents, including key industry participants and subject matter experts, were conducted to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.

Secondary Research

In the secondary research process, various sources were referred to to identify and collect information for the study. The secondary sources included annual reports, press releases, investor presentations of companies, white papers, journals, certified publications, and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as AI conferences and related magazines. Additionally, the generative AI cybersecurity spending of various countries was extracted from respective sources. Secondary research was used to obtain key information about the industry’s supply chain to identify key players by solution, service, market classification, and segmentation according to the offerings of major players and industry trends related to offerings, generative AI-based cybersecurity software, cybersecurity software for generative AI, security types, end users, and regions, and key developments from both market and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors specializing in business development, marketing, and generative AI cybersecurity; related key executives from generative AI cybersecurity offering vendors, SIs, managed service providers, and industry associations; and key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, offerings, security types, end users, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using generative AI cybersecurity solutions, were interviewed to understand the buyer’s perspective on suppliers, products, and their current usage of generative AI cybersecurity solutions, which would impact the overall generative AI cybersecurity market.

Generative AI Cybersecurity Market Size, and Share

Note: Tier 1 companies account for annual revenue of >USD 10 billion; tier 2 companies’ revenue ranges
between USD 1 billion and USD 10 billion; and tier 3 companies’ revenue ranges between USD 500 million
and USD 1 billion. Other designations include VPs, global heads, and product leaders.
Source: MarketsandMarkets Analysis

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Multiple approaches were adopted to estimate and forecast the generative AI cybersecurity market. The first approach involved estimating the market size by summing the companies’ revenue generated by selling solutions.

Market Size Estimation Methodology- Top-down approach

In the top-down approach, an exhaustive list of all the vendors offering solutions in the generative AI cybersecurity market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated based on the breadth of solutions according to offering type, security type, and end users. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets repository for validation.

Market Size Estimation Methodology-Bottom-up approach

In the bottom-up approach, the adoption rate of generative AI cybersecurity solutions among different end users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of generative AI cybersecurity solutions among industries, along with different use cases with respect to their regions, was identified and extrapolated. Use cases identified in different regions were given weightage for the market size calculation.

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the generative AI cybersecurity market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major generative AI cybersecurity providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primary interviews, the exact values of the overall generative AI cybersecurity market size and segments’ size were determined and confirmed using the study.

Generative AI Cybersecurity Market : Top-Down and Bottom-Up Approach

Data Triangulation

The market was split into several segments and subsegments after arriving at the overall market size using the market size estimation processes as explained above. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

Generative AI cybersecurity includes the techniques & technology used to secure generative AI systems, as well as the revolutionary impact of generative AI tools on cybersecurity practices. The scope of the generative AI cybersecurity market includes two key angles:

Cybersecurity with generative AI: investigates how generative AI techniques are transforming the protection of enterprise security architecture, apps, networks, and data. Robust real-time anomaly detection, enhanced threat identification, and advanced threat hunting are all made possible by cybersecurity enhanced by generative AI.
Cybersecurity for generative AI: focuses on implementing advanced cybersecurity technologies, tools, policies, and practices specifically designed to protect generative AI from threats and vulnerabilities. This involves strategies such as adversarial machine learning, differential privacy, federated learning security, and secure model deployment, aimed at safeguarding data, AI models, and deployment environments.

Stakeholders

Generative AI cybersecurity providers
Third-party administrators
Business analysts
Cloud service providers
Consulting service providers
Enterprise end users
Distributors and value-added resellers (VARs)
Government agencies
Independent software vendors (ISVs)
Market research and consulting firms
Support & maintenance service providers
System integrators (SIs)/migration service providers
Technology providers

Report Objectives

To define, describe, and forecast the generative AI cybersecurity market by offering, generative AI-based cybersecurity software, cybersecurity software for generative AI, security type, and end user
To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the market
To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
To forecast the market size of segments for the five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
To profile the key players and comprehensively analyze their market ranking and core competencies
To analyze competitive developments, such as partnerships, product launches, and mergers & acquisitions, in the generative AI cybersecurity market

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per your company’s specific needs. The following customization options are available for the report:

Product Analysis

Product quadrant, which gives a detailed comparison of the product portfolio of each company.

Geographic Analysis as per Feasibility

Further breakup of the North American generative AI cybersecurity market
Further breakup of the European generative AI cybersecurity market
Further breakup of the Asia Pacific generative AI cybersecurity market
Further breakup of the Middle Eastern & African generative AI cybersecurity market
Further breakup of the Latin American generative AI cybersecurity market

Previous Versions of this Report

Generative AI Cybersecurity Market Size, Share, Growth Analysis, By Generative AI-native Tools (Threat Hunting, Remediation), Cybersecurity Tools for Generative AI (Model Security, Data Security), End-user and Region - Global Industry Forecast to 2030

Report Code TC 9099

Published in Jul, 2024, By MarketsandMarkets™

Generative AI Cybersecurity Market

Generative AI Cybersecurity Market by Generative AI-based Cybersecurity (SIEM, Risk Assessment, Threat Intelligence), Cybersecurity Software for Generative AI (AI Model Security), Security Type (Data Encryption, Access Control) - Global Forecast to 2031