Artificial intelligence (AI) and machine learning (ML) have been adopted widely across industries over the years due to the multifaceted benefits that the technologies bring about.
AI and ML have been also increasingly adopted across industries, from such as healthcare, education, information and communication technologies (ICT), logistics, maritime, aviation, aerospace and defense, entertainment and gaming.
Particularly, AI and ML have been used widely in cybersecurity industries, by both hacking and security communities, making the security landscape even more sophisticated. Many organizations, regardless of size, are now facing greater challenges in day-to-day security operations. Many of them indicate that the cost of threat management, particularly threat detection and response, is too high. Meanwhile, AI-driven attacks have increased in number and frequency, requiring security professionals to have more advanced, smart and automated technologies to combat these automated attacks.
The complex challenges in security operation nowadays suggest the need for a smarter, adaptable, scalable, automated and predictive security strategy in order to deal with the constantly evolving threats more effectively. AI and ML have been increasingly developed by security companies to strengthen their competitiveness. Most of them are now in the midst of developing their own AI/ML algorithms to empower their security products, either in some products or all of the product lines. AI and ML have been used in all stages of cybersecurity to enable a smarter, more proactive, and automated approach to cyber defense, from threat prevention protection, threat detection/threat hunting, or threat response, to predictive security strategy.
Security startup companies are the most proactive in introducing AI-security technologies to the market. However, large traditional security companies have also beefed up their strategies to stay abreast of the trend of integrating AI/ML into their existing security solutions.
There are hundreds of companies now in the market, with different capabilities and focus areas, from application-centric protection, or AEDR, to security analytics platform. In this report, we profile AI-driven companies and AI-centric cybersecurity companies.
This research is delivered by the author's cybersecurity research and practice team.
Key Issues Addressed
- What are the needs to adopt a smarter and holistic security framework?
- What key role are AI and ML expected to play in cybersecurity?
- How are AI/ ML adopted in cybersecurity?
- What are the use cases for AI/ML in cybersecurity?
- What are the key features and differentiators of AI-driven security solutions in the market?
1. Executive Summary
- Key Findings
- Artificial Intelligence
- Machine Learning and Deep Learning
- AI, Machine Learning, and Deep Learning
- Deep Learning, a 2-part Process
- Deep Learning Algorithms that Execute Diverse Tasks
- Diligent Resources to Support AI Applications
3. AI Adoption Trends
- AI Service Providers
- AI and Edge Computing Which Increase Cybersecurity Risks
- Increasing Human-machine Coordination
4. Artificial Intelligence in Cybersecurity
- The Need for a Smarter & Holistic Security Framework
- The Top 4 Challenges to Security Operations
- Other Challenges to Security Operations
- The Need for AI-powered Security
- AI in Cybersecurity Strategy
- Use Cases of AI Adoption - AI-enabled Security Strategy
- Use Cases for AI in Cybersecurity
5. AI-based Security Solution Profiles
- The Market Landscape
- Shape Security
- The Final Word
- Legal Disclaimer
- List of Exhibits