Table of Content

1 INTRODUCTION 
    1.1 STUDY OBJECTIVES 
    1.2 DEFINITION 
    1.3 STUDY SCOPE 
           1.3.1. MARKETS COVERED
           1.3.2. GEOGRAPHIC SCOPE
           1.3.3. YEARS CONSIDERED FOR THE STUDY
    1.4 CURRENCY 
    1.5 LIMITATIONS 
    1.6 STAKEHOLDERS 
    1.7 SUMMARY OF CHANGES  
2 RESEARCH METHODOLOGY 
    2.1 RESEARCH DATA 
           2.1.1 SECONDARY AND PRIMARY RESEARCH
           2.1.2 SECONDARY DATA
           2.1.3 PRIMARY DATA
                    2.1.3.1 Breakdown of primaries
                    2.1.3.2 Primary sources
    2.2 MARKET SIZE ESTIMATION 
           2.2.1 BOTTOM-UP APPROACH
           2.2.2 TOP-DOWN APPROACH
    2.3 MARKET BREAKDOWN AND DATA TRIANGULATION 
    2.4 RESEARCH ASSUMPTIONS 
    2.5 LIMITATIONS 
    2.6 RISK ASSESSMENT 
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS 
    4.1 ATTRACTIVE OPPORTUNITIES IN ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET 
    4.2 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY TECHNOLOGY 
    4.3 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY APPLICATION 
    4.4 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET IN APAC,  COUNTRY
    4.5 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY GEOGRAPHY  
5 MARKET OVERVIEW 
    5.1 INTRODUCTION 
    5.2 MARKET DYNAMICS 
           5.2.1 DRIVERS
                    5.2.1.1 Growing adoption of IoT and increasing number of connected devices
                    5.2.1.2 Increasing instances of cyber threats
                    5.2.1.3 Rising concerns of data protection
                    5.2.1.4 Increasing vulnerability of Wi-Fi networks to security threats45
           5.2.2 RESTRAINTS
                    5.2.2.1 Inability of AI to stop zero-day and advanced threats
                    5.2.2.2 Rise in insider cyber threats
           5.2.3 OPPORTUNITIES
                    5.2.3.1 Growing need for cloud-based security solutions among SMEs
                    5.2.3.2 Increasing use of social media for business functions
           5.2.4 CHALLENGES
                    5.2.4.1 Limited number of cybersecurity and AI professionals
                    5.2.4.2 Lack of interoperability with existing information systems
    5.4 VALUE CHAIN ANALYSIS  
    5.5 ECOSYSTEM/MARKET MAP 
    5.6 PRICING ANALYSIS 
           5.6.1 ASP TRENDS 
           5.6.2 ASP OF KEY PLAYERS 
    5.7.TECHNOLOGY ANALYSIS 
    5.8 PORTER'S FIVE FORCES ANALYSIS  
    5.9 KEY STAKEHOLDERS AND BUYING CRITERIA  
    5.10 CASE STUDY ANALYSIS  
    5.11 TRADE AND TARIFF ANALYSIS  
    5.12 KEY CONFERENCE AND EVENTS  
    5.13 REGULATORY LANDSCAPE  
           5.13.1 REGULATIONS 
           5.13.2 REGULATORY BODIES, GOVERNMENT AGENCIES & OTHER ORGANIZATIONS 
    5.14 REVENUE SHIFT AND NEW REVENUE POCKETS FOR CUSTOMERS’ BUSINESSES
    5.15 PATENTS ANALYSIS
6 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY OFFERING 
    6.1 INTRODUCTION 
    6.2 HARDWARE 
           6.2.1 PROCESSORS
                    6.2.1.1 MPU
                    6.2.1.2 GPU
                    6.2.1.3 FPGA
                    6.2.1.4 ASIC
           6.2.2 MEMORY
           6.2.3 NETWORK
    6.3 SOFTWARE 
           6.3.1 AI SOLUTIONS
           6.3.2 AI PLATFORM
                    6.3.2.1 Application program interface (API) 
                    6.3.2.2 Machine learning framework
    6.4 SERVICES 
           6.4.1 DEPLOYMENT & INTEGRATION
           6.4.2 SUPPORT & MAINTENANCE
7 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY DEPLOYMENT TYPE 
    7.1 INTRODUCTION 
    7.2 CLOUD 
    7.3 ON-PREMISE 
8 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY SECURITY TYPE 
    8.1 INTRODUCTION 
    8.2 NETWORK SECURITY 
    8.3 ENDPOINT SECURITY 
    8.4 APPLICATION SECURITY 
    8.5 CLOUD SECURITY 
9 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY TECHNOLOGY 
    9.1 INTRODUCTION 
    9.2 MACHINE LEARNING 
           9.2.1 DEEP LEARNING
           9.2.2 SUPERVISED LEARNING
           9.2.3 UNSUPERVISED LEARNING
           9.2.4 REINFORCEMENT LEARNING
           9.2.5 OTHERS
    9.3 NATURAL LANGUAGE PROCESSING (NLP)  
    9.4 CONTEXT-AWARE COMPUTING 
10 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY APPLICATION 
     10.1 INTRODUCTION 
     10.2 IDENTITY AND ACCESS MANAGEMENT 
     10.3 RISK AND COMPLIANCE MANAGEMENT 
     10.4 DATA LOSS PREVENTION 
     10.5 UNIFIED THREAT MANAGEMENT 
     10.6 SECURITY AND VULNERABILITY MANAGEMENT 
     10.7 ANTIVIRUS/ANTIMALWARE 
     10.8 FRAUD DETECTION/ANTI-FRAUD 
     10.9 INTRUSION DETECTION/PREVENTION SYSTEM 
     10.10 THREAT INTELLIGENCE 
     10.11 OTHERS 
11 ARTIFICIAL INTELLIGENCE IN CYBERSECURITY MARKET, BY END USER 
     11.1 INTRODUCTION 
     11.2 BFSI 
     11.3 RETAIL 
     11.4 GOVERNMENT & DEFENSE 
     11.5 MANUFACTURING 
     11.6 INFRASTRUCTURE 
     11.7 ENTERPRISE 
     11.8 HEALTHCARE 
     11.9 AUTOMOTIVE & TRANSPORTATION 
     11.10 OTHER 
12 GEOGRAPHIC ANALYSIS 
     12.1 INTRODUCTION 
     12.2 NORTH AMERICA 
             12.2.1 US
             12.2.2 CANADA
             12.2.3 MEXICO
     12.3 EUROPE 
             12.3.1 GERMANY
             12.3.2 UK
             12.3.3 FRANCE
             12.3.4 ITALY
             12.3.5 SPAIN
             12.3.6 REST OF EUROPE
     12.4 APAC 
             12.4.1 CHINA
             12.4.2 JAPAN
             12.4.3 SOUTH KOREA
             12.4.4 INDIA
             12.4.5 REST OF APAC
     12.5 ROW 
             12.5.1 MIDDLE EAST AND AFRICA
             12.5.2 SOUTH AMERICA
13 COMPETITIVE LANDSCAPE 
     13.1 INTRODUCTION  
     13.2 STRATEGIES ADOPTED BY KEY PLAYERS / RIGHT TO WIN  
     13.3 REVENUE ANALYSIS OF TOP PLAYERS  
     13.4 MARKET SHARE ANALYSIS  
     13.5 COMPANY EVOLUTION QUANDRANT 
             13.5.1 STARS 
             13.5.2 PERVASIVE  
             13.5.3 EMERGING LEADERS 
             13.5.4 PARTICIPANTS  
     13.6 START-UP /SME EVALUATION QUDRANTS   
             13.6.1. PROGRESSIVE COMPANIES
             13.6.2. RESPONSIVE COMPANIES
             13.6.3. DYNAMIC COMPANIES
             13.6.4. STARTING BLOCKS
     13.7 COMPANY PRODUCT FOOTPRINT 
     13.9 COMPETITIVE BENCHMARKING   
     13.1 COMPETITIVE SCENARIO AND TRENDS   
14 COMPANY PROFILES 
     14.1 KEY PLAYERS 
             14.1.1 NVIDIA
             14.1.2 INTEL
             14.1.3 XILINX
             14.1.4 SAMSUNG ELECTRONICS
             14.1.5 MICRON TECHNOLOGY
             14.1.6 IBM
             14.1.7 AWS
             14.1.8  MICROSOFT 
             14.1.9 PALO ALTO NETWORK
             14.1.11 FIREEYE
             14.1.12 SYMANTEC
     14.2 OTHER PLAYERS 
             14.2.1 CYLANCE
             14.2.2 THREATMETRIX
             14.2.3 SECURONIX
             14.2.4 SIFT SCIENCE
             14.2.5 ACALVIO TECHNOLOGIES
             14.2.6 DARKTRACE
             14.2.7 SPARKCONGNITION
             14.2.8 FORTINET
             14.2.9 CHECK POINT SOFTWARE TECHNOLOGIES
             14.2.10 HIGH-TECH BRIDGE
             14.2.11 DEEP INSTINCT
             14.2.12 SENTINELONE
             14.2.13 FEEDZAI
             14.2.14 VECTRA NETWORKS
             14.2.15 ZIMPERIUM
             14.2.16 FORTSCALE
             14.2.17 ARGUS CYBER SECURITY
             14.2.18 NOZOMI NETWORKS
             14.2.19 INDEGY
             14.2.20 BITSIGHT TECHNOLOGIES
     14.3 ANTIVIRUS COMPANIES 
             14.3.1 MCAFEE
             14.3.2 KASPERSKY LAB
             14.3.3 BITDEFENDER
             14.3.4 ESET
15 APPENDIX 
     15.1 INSIGHTS FROM INDUSTRY EXPERTS 
     15.2 DISCUSSION GUIDE 
     15.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     15.4 AVAILABLE CUSTOMIZATIONS 
     15.5 RELATED REPORTS 
     15.6 AUTHOR DETAILS