Abstract
Summary
The global market for Al in Cybersecurity is projected to grow from USD 22.4 billion in 2023 to USD 60.6 billion by 2028, at a CAGR of 21.9% during the forecast period. The rising frequency and sophistication of cyberattacks are compelling enterprises to explore advanced and innovative defense solutions, driving the growth of the artificial intelligence in cybersecurity market. The expanding use of IoT devices across diverse applications, coupled with the escalating volume of data generated by these devices, presents lucrative opportunities for the global AI in cybersecurity market. The increasing demand for adaptable security services across businesses is expected to propel market growth. Furthermore, the emergence of visualization tools for security data is anticipated to provide lucrative growth prospects in the forecast period.
“The Automotive vertical is projected to hold the largest CAGR during the forecast period.”
AI in Cybersecurity is being adopted in the healthcare and life sciences vertical to enable smart medical facilities with digital diagnosis and remote patient monitoring. The automotive industry is embracing AI for cybersecurity due to the growing complexity of cars, the rise of vulnerable autonomous vehicles, and a vast global supply chain ripe for attack. AI excels at real-time threat detection, data protection, and automating manual tasks, leading to a more efficient and adaptable defense against cyberattacks. However, challenges such as data bias and opaque AI models necessitate careful implementation and integration with existing systems. Additionally, AI is poised to play a crucial role in ensuring the future of connected and autonomous vehicles.
“Among applications, Identity & Access Management to account for the largest market during the forecast period.”
The exponential growth of Identity & Access Management (IAM) in the AI in cybersecurity market is propelled by several key factors. The rising complexity of cyber threats necessitates advanced security measures, where IAM plays a pivotal role in fortifying defenses. The integration of Artificial Intelligence enhances IAM capabilities, enabling real-time threat analysis and adaptive access controls. Additionally, regulatory compliance mandates drive the adoption of IAM solutions, ensuring organizations meet stringent security standards. Furthermore, the surge in remote work amplifies the need for robust IAM, safeguarding access across diverse environments. IAM in AI cybersecurity is thus buoyed by the imperative to combat evolving cyber threats, comply with regulations, and secure remote access.
“Among technology, machine learning is anticipated to account for the largest market share during the forecast period.”
The adoption of Machine Learning (ML) in the AI in cybersecurity market is experiencing a robust upward trajectory. ML's capability to analyze vast datasets enables proactive threat detection and response. Its application in anomaly detection and pattern recognition enhances cybersecurity measures, identifying potential risks in real time. ML-driven solutions adapt and evolve, staying ahead of sophisticated cyber threats. The dynamic nature of cyberattacks necessitates a continuous learning approach, aligning with ML's strength. Organizations are increasingly integrating ML into their cybersecurity strategies to bolster defense mechanisms, mitigate risks, and ensure resilient protection against the evolving landscape of cyber threats. The growing adoption underscores ML's pivotal role in fortifying AI-driven cybersecurity initiatives.
“North America to account for the largest market size during the forecast period.”
North America, leads globally in security vendors and cyber incidents. Safeguarding critical infrastructures and sensitive data faces increasing challenges with the rise of interconnections and digitalization. The US, a significant early adopter of cybersecurity solutions, is expected to dominate market revenue. Escalating cyberattacks, including DDoS, ransomware, and spear phishing, pose critical economic and national security challenges. Despite technological advancements such as cloud computing and IoT adoption, businesses are confronted with sophisticated threats. The region's vulnerabilities and compliance demands make North America the most lucrative market for various cybersecurity vendors, with the US and Canada at the forefront.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI in Cybersecurity market.
By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
By Region: North America: 40%, Europe: 20%, APAC: 30%, MEA: 5%, Latin America: 5%
Major vendors offering AI in Cybersecurity solutions and services across the globe are NVIDIA (US), Intel (US), Xilinx Inc. (US), Samsung Electronics Co., Ltd (South Korea), Micron Technology, Inc. (US), IBM Corporation (US), Amazon Web Services, Inc. (US), Microsoft (US), Palo Alto Networks Inc. (US), Trellix (US), CrowdStrike (US), NortonLifeLock (US), Cylance Inc. (US), ThreatMetrix Inc. (US), Securonix Inc. (US), Sift Science (US), Acalvio Technologies (US), Darktrace (UK), SparkCognition Inc. (US), Fortinet (US), Check Point Software Technologies, Ltd (US), High-Tech Bridge (Switzerland), Deep Instinct (US), SentinelOne (US), Feedzai (US), Vectra (US) Zimperium (US), Argus Cyber Security (Israel), Nozomi Networks (US), BitSight Technologies (US), and Antivirus companies such as Kaspersky Lab (Russia), Bitdefender (Romania), and ESET (US).
Research Coverage
The market study covers AI in Cybersecurity across segments. It aims to estimate the market size and the growth potential across different segments, such as offering, security type, technology, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for AI in Cybersecurity and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (Growing adoption of IoT and increasing number of connected devices, Rising concerns of data protection, Increasing vulnerability of Wi-Fi networks to security threats), restraints (Rise in insider cyber threats), opportunities (Growing need for cloud-based security solutions among SMEs, Increasing use of social media for business functions), and challenges (Limited number of cybersecurity and AI professionals, Lack of interoperability with existing information systems)
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in Cybersecurity market.
• Market Development: Comprehensive information about lucrative markets – the report analyses the AI in Cybersecurity market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in AI in Cybersecurity market strategies; the report also helps stakeholders understand the pulse of the AI in Cybersecurity market and provides them with information on key market drivers, restraints, challenges, and opportunities.
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as CrowdStrike (US), Fortinet (US), Trellix (US), Check Point Software Technologies (US), Darktrace (UK) and others in the AI in Cybersecurity market.
Table of Contents
1 INTRODUCTION 66
1.1 STUDY OBJECTIVES 66
1.2 MARKET DEFINITION 66
1.2.1 INCLUSIONS AND EXCLUSIONS 67
1.3 MARKET SCOPE 70
1.3.1 MARKET SEGMENTATION 70
1.3.2 REGIONS COVERED 71
1.3.3 YEARS CONSIDERED 71
1.4 CURRENCY CONSIDERED 72
1.5 STAKEHOLDERS 72
1.6 SUMMARY OF CHANGES 73
1.6.1 RECESSION IMPACT 73
2 RESEARCH METHODOLOGY 74
2.1 RESEARCH DATA 74
2.1.1 SECONDARY DATA 75
2.1.2 PRIMARY DATA 75
- 2.1.2.1 Breakup of primary profiles 76
- 2.1.2.2 Key industry insights 76
2.2 MARKET FORECAST 77
2.3 MARKET SIZE ESTIMATION 78
2.3.1 TOP-DOWN APPROACH 78
2.3.2 BOTTOM-UP APPROACH 79
2.4 DATA TRIANGULATION 82
2.5 STUDY ASSUMPTIONS 83
2.6 STUDY LIMITATIONS 85
2.7 IMPACT OF RECESSION ON GLOBAL AI IN CYBERSECURITY MARKET 85
3 EXECUTIVE SUMMARY 87
4 PREMIUM INSIGHTS 99
4.1 ATTRACTIVE OPPORTUNITIES FOR AI IN CYBERSECURITY MARKET PLAYERS 99
4.2 AI IN CYBERSECURITY MARKET: TOP THREE APPLICATIONS 99
4.3 NORTH AMERICA: AI IN CYBERSECURITY MARKET, BY OFFERING AND KEY VERTICAL 100
4.4 AI IN CYBERSECURITY MARKET, BY REGION 100
5 MARKET OVERVIEW AND INDUSTRY TRENDS 101
5.1 INTRODUCTION 101
5.2 MARKET DYNAMICS 101
5.2.1 DRIVERS 102
- 5.2.1.1 Growth in adoption of IoT and increase in number of connected devices 102
- 5.2.1.2 Increase in instances of cyber threats 102
- 5.2.1.3 Rise in concerns regarding data protection 102
- 5.2.1.4 Increase in vulnerability of Wi-Fi networks to security threats 103
5.2.2 RESTRAINTS 103
- 5.2.2.1 Inability of AI to stop zero-day and advanced threats 103
- 5.2.2.2 Rise in insider cyber threats 103
5.2.3 OPPORTUNITIES 104
- 5.2.3.1 Growth in need for cloud-based security solutions among SMEs 104
- 5.2.3.2 Increase in use of social media for business functions 104
- 5.2.3.3 Zero trust framework providing advanced security 104
5.2.4 CHALLENGES 104
- 5.2.4.1 Limited number of cybersecurity and AI professionals 104
- 5.2.4.2 Lack of interoperability with existing information systems 105
- 5.2.4.3 Shortcomings of AI 105
5.3 TECHNOLOGY ANALYSIS 106
5.3.1 KEY TECHNOLOGY 106
- 5.3.1.1 Generative AI 106
- 5.3.1.2 Blockchain 106
- 5.3.1.3 Predictive Analytics 106
5.3.2 COMPLEMENTARY TECHNOLOGY 107
- 5.3.2.1 Tokenization 107
- 5.3.2.2 Cloud Computing 107
- 5.3.2.3 AR/VR 107
5.3.3 ADJACENT TECHNOLOGY 107
- 5.3.3.1 Quantum Computing 107
- 5.3.3.2 IoT 108
- 5.3.3.3 Big Data 108
- 5.3.3.4 5G 108
5.4 CASE STUDY ANALYSIS 109
5.4.1 BFSI 109
- 5.4.1.1 Cargills Bank implemented IBM QRadar SIEM 109
5.4.2 IT/ITES 110
- 5.4.2.1 Infosys offered Infrastructure Security Endpoint Management (ISEM) for investment giant 110
5.4.3 ENERGY & UTILITIES 110
- 5.4.3.1 Siemens used Amazon Web Services (AWS) to build data analytics platform 110
5.4.4 TELECOMMUNICATIONS 111
- 5.4.4.1 Fortune 500 TELCO used snorkel flow for classifying encrypted network data flows 111
5.4.5 GOVERNMENT & DEFENSE 112
- 5.4.5.1 US government agency used Snorkel Flow for application classification and network attack detection 112
5.5 EVOLUTION OF AI IN CYBERSECURITY MARKET 113
5.5.1 TURING, MACHINES, AND THEORETICAL FOUNDATIONS 113
5.5.2 EARLY DAYS OF COMPUTING 114
5.5.3 EXPERT SYSTEMS 114
5.5.4 MACHINE LEARNING EVOLUTION 114
5.5.5 DEEP LEARNING AND NEURAL NETWORKS 114
5.5.6 RECENT DEVELOPMENTS IN LARGE LANGUAGE MODELS 114
5.5.7 EMERGENCE OF QUANTUM COMPUTING 115
5.5.8 FUTURE AHEAD 115
5.6 ECOSYSTEM/MARKET MAP 115
5.7 TARIFF & REGULATORY LANDSCAPE 117
5.7.1 TARIFFS RELATED TO AI IN CYBERSECURITY 117
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 118
- 5.7.2.1 North America 122
- 5.7.2.1.1 US 122
- 5.7.2.1.1.1 California Consumer Privacy Act (CCPA) 122
- 5.7.2.1.1.2 Health Insurance Portability and Accountability Act (HIPAA) 122
- 5.7.2.1.1.3 Artificial Intelligence Risk Management Framework 1.0 (RMF) 122
- 5.7.2.1.2 Canada 122
- 5.7.2.1.2.1 Public Safety Canada Regulation 122
- 5.7.2.1.1 US 122
- 5.7.2.2 Europe 122
- 5.7.2.2.1 General Data Protection Regulation (GDPR) 122
- 5.7.2.2.2 EU Regulatory Framework for AI 123
- 5.7.2.3 Asia Pacific 123
- 5.7.2.3.1 South Korea 123
- 5.7.2.3.1.1 Personal Information Protection Act (PIPA) 123
- 5.7.2.3.2 China 123
- 5.7.2.3.3 India 123
- 5.7.2.3.1 South Korea 123
- 5.7.2.4 Middle East & Africa 123
- 5.7.2.4.1 UAE 123
- 5.7.2.4.1.1 UAE AI Regulations 123
- 5.7.2.4.2 KSA 124
- 5.7.2.4.2.1 Saudi Arabia AI Strategy 124
- 5.7.2.4.3 Bahrain 124
- 5.7.2.4.3.1 Bahrain AI Ethics Framework 124
- 5.7.2.4.1 UAE 123
- 5.7.2.5 Latin America 124
- 5.7.2.5.1 Brazil 124
- 5.7.2.5.2 Mexico 124
- 5.7.2.1 North America 122
5.8 VALUE CHAIN ANALYSIS 124
5.8.1 RESEARCH, DESIGN, AND DEVELOPMENT 125
5.8.2 COMPONENT PROVIDERS 125
5.8.3 SYSTEM INTEGRATORS 126
5.8.4 MARKETING AND SALES EXECUTIVES 126
5.8.5 END-USER INDUSTRIES 126
5.8.6 SERVICE PROVIDERS 126
5.9 PORTER'S FIVE FORCES ANALYSIS 126
5.9.1 THREAT FROM NEW ENTRANTS 128
5.9.2 THREAT FROM SUBSTITUTES 128
5.9.3 BARGAINING POWER OF SUPPLIERS 128
5.9.4 BARGAINING POWER OF BUYERS 128
5.9.5 INTENSITY OF COMPETITIVE RIVALRY 129
5.10 KEY CONFERENCES & EVENTS 129
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA 131
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS 131
5.11.2 BUYING CRITERIA 132
5.12 PRICING ANALYSIS 133
5.12.1 INDICATIVE PRICING ANALYSIS, BY AI IN CYBERSECURITY VENDORS 133
5.12.2 AVERAGE SELLING PRICE TRENDS OF KEY PLAYERS, BY HARDWARE 134
5.13 PATENT ANALYSIS 135
5.13.1 METHODOLOGY 135
5.13.2 PATENTS FILED, BY DOCUMENT TYPE 135
5.13.3 INNOVATION AND PATENT APPLICATIONS 136
- 5.13.3.1 Top 10 applicants in AI in cybersecurity 136
5.14 TRADE ANALYSIS 142
5.14.1 IMPORT SCENARIO OF AUTOMATED DATA PROCESSING MACHINES AND UNITS 142
5.14.2 EXPORT SCENARIO OF AUTOMATED DATA PROCESSING MACHINES AND UNITS 144
5.15 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESS 146
5.16 BEST PRACTICES IN AI IN CYBERSECURITY MARKET 147
5.17 TECHNOLOGY ROADMAP OF AI IN CYBERSECURITY MARKET 148
5.18 BUSINESS MODELS OF AI IN CYBERSECURITY MARKET 150
6 AI IN CYBERSECURITY MARKET, BY OFFERING 152
6.1 INTRODUCTION 153
6.1.1 OFFERING: AI IN CYBERSECURITY MARKET DRIVERS 153
6.2 HARDWARE 155
6.2.1 HARDWARE INNOVATIONS PIVOTAL IN FORTIFYING AI-POWERED CYBERSECURITY SOLUTIONS 155
6.2.2 ACCELERATORS 157
- 6.2.2.1 Rise in demand for faster processing in AI applications 157
6.2.3 PROCESSORS 158
- 6.2.3.1 Rise in trends in AI hardware: GPUs leading, FPGAs competing, and emergence of neuromorphic chips 158
- 6.2.3.2 MPU 161
- 6.2.3.3 GPU 161
- 6.2.3.4 FPGA 162
- 6.2.3.5 ASIC 163
- 6.2.3.6 TPU 164
- 6.2.3.7 Other processors 164
6.2.4 STORAGE 165
- 6.2.4.1 Memory solutions to help store large volumes of data 165
6.2.5 NETWORK 166
- 6.2.5.1 High-bandwidth networks to enable ultrafast communication between CPUs and GPUs 166
6.3 SOLUTIONS 167
6.3.1 RISE IN CYBERSECURITY FLEXIBILITY THROUGH STRATEGIC INTEGRATION OF AI SOLUTIONS 167
6.3.2 AI SOLUTIONS IN CYBERSECURITY MARKET, BY TYPE 169
- 6.3.2.1 Software 169
- 6.3.2.1.1 AI-infused software to drive growth in cybersecurity solutions to counteract rise in cyber threats 169
- 6.3.2.1.2 Standalone 171
- 6.3.2.1.3 Integrated 171
- 6.3.2.2 Platforms 172
- 6.3.2.2.1 Rapid adoption of AI platforms, including Machine Learning and Deep Learning, to safeguard sensitive data 172
- 6.3.2.2.2 Application Program Interface (API) 174
- 6.3.2.2.3 Machine learning framework 175
- 6.3.2.1 Software 169
6.3.3 AI IN CYBERSECURITY MARKET FOR SOLUTIONS, BY DEPLOYMENT MODE 176
- 6.3.3.1 On-premises 177
- 6.3.3.1.1 Full control of platforms, applications, systems, and data available on-premises 177
- 6.3.3.2 Cloud 178
- 6.3.3.2.1 Cost-effectiveness of cloud-based solutions to be advantageous 178
- 6.3.3.1 On-premises 177
6.4 SERVICES 180
6.4.1 PROFESSIONAL SERVICES 181
- 6.4.1.1 Surge in AI-powered professional services for enhanced threat detection and incident response 181
- 6.4.1.2 Consulting services 183
- 6.4.1.3 Deployment & integration 184
- 6.4.1.4 Support & maintenance 185
6.4.2 MANAGED SERVICES 186
- 6.4.2.1 AI integration to amplify managed cybersecurity services for enhanced threat management and response 186
7 AI IN CYBERSECURITY MARKET, BY SECURITY TYPE 188
7.1 INTRODUCTION 189
7.1.1 SECURITY TYPE: AI IN CYBERSECURITY MARKET DRIVERS 189
7.2 INFRASTRUCTURE SECURITY 191
7.2.1 APPLICATION OF AI TO PROTECT DATA FROM NUMEROUS THREATS 191
7.2.2 NETWORK SECURITY 193
- 7.2.2.1 Strengthening corporate networks with advanced cybersecurity measures and integration of AI 193
7.2.3 ENDPOINT SECURITY 194
- 7.2.3.1 Real-time threat detection, prevention, and response to network devices 194
7.2.4 CLOUD SECURITY 195
- 7.2.4.1 Complete threat protection against ransomware, internal email risks, and file-sharing risks 195
7.2.5 OTHER INFRASTRUCTURE SECURITY TYPES 196
7.3 DATA SECURITY 197
7.3.1 POTENTIAL FOR PRIVACY INFRINGEMENT WITH AI IDENTIFYING DATA PATTERNS, DELIVERING ACTIONABLE GUIDANCE, AND AUTONOMOUS THREAT MITIGATION 197
7.4 APPLICATION SECURITY 198
7.4.1 RELIANCE ON NETWORK SECURITY AND NEGLECTING APPLICATIONS TO CREATE SECURITY GAP, MAKING ORGANIZATIONS SUSCEPTIBLE TO ATTACKS 198
7.5 OTHER SECURITY TYPES 199
8 AI IN CYBERSECURITY MARKET, BY TECHNOLOGY 201
8.1 INTRODUCTION 202
8.1.1 TECHNOLOGY: AI IN CYBERSECURITY MARKET DRIVERS 202
8.2 MACHINE LEARNING 204
8.2.1 MACHINE LEARNING TO HELP BUSINESSES DEAL WITH LARGE VOLUMES OF DATA 204
8.2.2 DEEP LEARNING 206
- 8.2.2.1 Convolutional neural networks (CNN) 207
- 8.2.2.2 Recurrent neural networks (RNN) 207
- 8.2.2.3 Generative AI 207
- 8.2.2.3.1 Generative adversarial networks (GAI) 207
- 8.2.2.3.2 Variational autoencoders 208
- 8.2.2.3.3 Transformative AI 208
- 8.2.2.3.4 Other deep learning technologies 208
8.2.3 SUPERVISED LEARNING 208
- 8.2.3.1 Crucial role in threat detection and mitigation 208
8.2.4 UNSUPERVISED LEARNING 209
- 8.2.4.1 Using unsupervised learning for advanced cyber threat detection 209
8.2.5 REINFORCEMENT LEARNING 210
- 8.2.5.1 Impact and efficiency of reinforcement learning in adaptive defense strategies 210
8.2.6 NEURAL NETWORKS 211
8.3 NATURAL LANGUAGE PROCESSING 212
8.3.1 REAL-TIME TRANSLATION AND DEVELOPMENT OF SYSTEMS TO INTERACT THROUGH DIALOGUES 212
- 8.3.1.1 Text analysis 215
- 8.3.1.2 Chatbot analysis 215
- 8.3.1.3 Sentiment analysis 216
- 8.3.1.4 Natural language generation 217
- 8.3.1.5 Named entity recognition 218
- 8.3.1.6 Natural language understanding 219
8.4 CONTEXT-AWARE COMPUTING 220
8.4.1 DEVELOPMENT OF SOPHISTICATED HARD AND SOFT SENSORS 220
- 8.4.1.1 Automated threat intelligence 222
- 8.4.1.2 Threat hunting 223
- 8.4.1.3 Automation & orchestration 224
- 8.4.1.3.1 Security Orchestration, Automation, and Response (SOAR) 225
- 8.4.1.3.2 Robotic Process Automation (RPA) 225
8.5 COMPUTER VISION 226
8.5.1 IMAGE RECOGNITION 228
8.5.2 OBJECT DETECTION 229
8.5.3 ANOMALY DETECTION 229
8.5.4 VIDEO ANALYSIS 230
8.5.5 FACIAL RECOGNITION 231
8.5.6 SECURITY SURVEILLANCE OPTIMIZATION 232
9 AI IN CYBERSECURITY MARKET, BY APPLICATION 234
9.1 INTRODUCTION 235
9.2 IDENTITY & ACCESS MANAGEMENT 237
9.2.1 INCREASE IN THREAT OF DATA BREACHES DUE TO INSIDER ATTACKS 237
9.2.2 ACCESS POLICY ENFORCEMENT 239
9.2.3 USER PROVISIONING & DEPROVISIONING 239
9.2.4 SINGLE SIGN-ON (SSO) 239
9.2.5 IDENTITY GOVERNANCE & ADMINISTRATION (IGA) 240
9.2.6 MULTI-FACTOR AUTHENTICATION (MFA) 240
9.2.7 OTHER IDENTITY & ACCESS MANAGEMENT APPLICATIONS 240
9.3 RISK & COMPLIANCE MANAGEMENT 240
9.3.1 INCREASE IN NEED FOR INTERNAL AUDIT FEATURES AND REGULATORY COMPLIANCE MANDATES 240
9.3.2 AUTOMATED COMPLIANCE AUDITING 242
9.3.3 AUDIT TRAIL GENERATION 242
9.3.4 REGULATORY COMPLIANCE REPORTING 243
9.3.5 THREAT MODELING 243
9.3.6 INCIDENT RESPONSE PLANNING 243
9.3.7 OTHER RISK & COMPLIANCE MANAGEMENT APPLICATIONS 243
9.4 DATA LOSS PREVENTION 243
9.4.1 STRICT IMPLEMENTATION OF GOVERNMENT REGULATIONS AND LAWS 243
9.4.2 DATA ENCRYPTION & TOKENIZATION 245
9.4.3 CONTENT DISCOVERY & CLASSIFICATION 245
9.4.4 USER ACTIVITY MONITORING 246
9.4.5 INSIDER THREAT DETECTION 246
9.4.6 DATA LEAK DETECTION 246
9.4.7 OTHER DATA LOSS PREVENTION APPLICATIONS 246
9.5 UNIFIED THREAT MANAGEMENT 247
9.5.1 PROVISION OF MULTIPLE SECURITY FUNCTIONS TO PROTECT ENTERPRISES FROM EVOLVING CYBER THREATS 247
9.5.2 NETWORK MONITORING & REPORTING 248
9.5.3 BANDWIDTH MANAGEMENT 248
9.5.4 GATEWAY ANTIVIRUS 249
9.5.5 BOT IDENTIFICATION 249
9.5.6 SPAM FILTERING 249
9.5.7 OTHER UNIFIED THREAT MANAGEMENT APPLICATIONS 249
9.6 SECURITY & VULNERABILITY MANAGEMENT 250
9.6.1 GROWTH IN VOLUME OF DATA AND RISE IN ADOPTION OF BYOD TREND 250
9.6.2 PATCH MANAGEMENT 252
9.6.3 VULNERABILITY SCANNING & ASSESSMENT 252
9.6.4 SECURITY INFORMATION & EVENT MANAGEMENT (SIEM) 252
9.6.5 BREACH RISK PREDICTION 252
9.6.6 CONFIGURATION MANAGEMENT 252
9.6.7 OTHER SECURITY & VULNERABILITY MANAGEMENT APPLICATIONS 253
9.7 FRAUD DETECTION 253
9.7.1 NEED FOR ROBUST INFRASTRUCTURE WITH HIGH-PERFORMANCE COMPUTING AND SCALABILITY 253
9.7.2 ENDPOINT DETECTION & RESPONSE (EDR) 255
9.7.3 PATTERN RECOGNITION 255
9.7.4 TRANSACTION MONITORING 255
9.7.5 GEOLOCATION ANALYSIS 255
9.7.6 PHISHING DETECTION 255
9.7.7 OTHER FRAUD DETECTION APPLICATIONS 255
9.8 INTRUSION DETECTION/PREVENTION SYSTEM 256
9.8.1 NEED TO MONITOR NETWORK ACROSS ENTERPRISES FOR SUSPICIOUS ACTIVITY 256
9.8.2 THRESHOLD MONITORING 258
9.8.3 PROTOCOL-BASED INTRUSION DETECTION SYSTEM (PIDS) 258
9.8.4 USER & ENTITY BEHAVIOR ANALYTICS (UEBA) 258
9.8.5 FILE INTEGRITY MONITORING 258
9.8.6 HOST-BASED INTRUSION PREVENTION SYSTEM (HIPS) 258
9.8.7 OTHER INTRUSION DETECTION/PREVENTION SYSTEM APPLICATIONS 259
9.9 OTHER APPLICATIONS 259
10 AI IN CYBERSECURITY MARKET, BY VERTICAL 261
10.1 INTRODUCTION 262
10.1.1 VERTICAL: AI IN CYBERSECURITY MARKET DRIVERS 262
10.2 BFSI 264
10.2.1 RISE IN INSTANCES OF CYBER-ATTACKS IN FINANCIAL VERTICAL 264
10.3 GOVERNMENT & DEFENSE 266
10.3.1 INCREASE IN SPENDING ON AI-BASED CYBERSECURITY SOLUTIONS 266
10.4 MANUFACTURING 267
10.4.1 HUGE INVESTMENTS IN INDUSTRY ON AUTOMATION 267
10.5 HEALTHCARE & LIFE SCIENCES 269
10.5.1 INCREASE IN NUMBER OF DATA BREACHES AND COMPROMISED ELECTRONIC HEALTHCARE RECORDS 269
10.6 RETAIL & ECOMMERCE 270
10.6.1 HARNESSING AI FOR ENHANCED THREAT PROTECTION AND COMPLIANCE MANAGEMENT 270
10.7 TELECOMMUNICATIONS 271
10.7.1 LARGE NUMBER OF MONETARY TRANSACTIONS AND EXPANDING CONSUMER BASE 271
10.8 IT/ITES 273
10.8.1 NEED TO IMPLEMENT AI-POWERED CYBERSECURITY SOLUTIONS TO ENHANCE SECURITY POSTURE 273
10.9 MEDIA & ENTERTAINMENT 274
10.9.1 RISE IN ENTERTAINMENT SECURITY TO UTILIZE AI FOR CHALLENGING CYBER DEFENSES 274
10.10 AUTOMOTIVE 275
10.10.1 INCREASE IN CYBERATTACKS DUE TO RISE IN NUMBER OF CONNECTED AND AUTONOMOUS CARS 275
10.11 OTHER VERTICALS 277
11 AI IN CYBERSECURITY MARKET, BY REGION 278
11.1 INTRODUCTION 279
11.2 NORTH AMERICA 281
11.2.1 NORTH AMERICA: AI IN CYBERSECURITY MARKET DRIVERS 281
11.2.2 NORTH AMERICA: IMPACT OF RECESSION 281
11.2.3 US 295
- 11.2.3.1 Rapid growth in technological innovations and increased use of internet 295
11.2.4 CANADA 295
11.3 EUROPE 296
11.3.1 EUROPE: AI IN CYBERSECURITY MARKET DRIVERS 297
11.3.2 EUROPE: IMPACT OF RECESSION 297
11.3.3 UK 310
- 11.3.3.1 Government initiatives toward cybersecurity for training and educating organizations 310
11.3.4 GERMANY 311
- 11.3.4.1 2021 Cyber Security Strategy and government investment in AI promotion 311
11.3.5 FRANCE 311
- 11.3.5.1 High internet penetration rate and developed digital and technological infrastructure to increase vulnerability to cyberattacks 311
11.3.6 ITALY 312
- 11.3.6.1 Surge in ransomware incidents across Italy 312
11.3.7 SPAIN 313
- 11.3.7.1 Need to integrate AI in cybersecurity for increasingly technological society 313
11.3.8 NETHERLANDS 313
- 11.3.8.1 Advancing cybersecurity technology, backed by robust legal and IT structures 313
11.3.9 REST OF EUROPE 314