Now that data breaches are more common, Digital Trust is a top priority for C-level leaders that build and maintain the IT infrastructure for digital transformation. Besides, for most organizations, losing digital trust can have a significant impact on brand reputation and the bottom line.
Artificial Intelligence (AI) and Machine Learning (ML) have been adopted for their automation benefits, from predictive outcomes to advanced data analytics. AI-based cybersecurity can augment the capabilities of IT staff and help organizations deflect cyber threats, according to the latest market study by Frost & Sullivan.
AI and ML Market Development
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 IT 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.
With digital transformation a priority for a majority of enterprises today, there is a proliferation of connected devices, offering customers convenience, efficient services and better experiences. However, this connectivity also increases the potential risk of cyberattacks for enterprises and users.
Cybercriminals are also using more sophisticated methods to attack organizations. These include polymorphic malware, AI and other automated techniques. Enterprises are struggling with a lack of trained staff and cybersecurity expertise to counter the more sophisticated attacks.
These increasing challenges in security operations suggest the need for a smarter, more adaptable, automated and predictive security strategy. AI and ML are increasingly being developed by security companies to strengthen their competitiveness using their own AI or ML algorithms to empower security products and augment the capabilities of existing IT and cybersecurity staff in enterprises.
AI and ML are being incorporated into all stages of cybersecurity to enable enterprises to adopt a smarter, more proactive and automated approach toward cyber defense, including threat prevention or protection, threat detection or hunting, and threat response to predictive security strategies.
While technology startups have been the most proactive in introducing multiple AI-enabled security offerings into the market, larger IT vendors have also incorporated AI and ML into their existing enterprise security solutions.
Outlook for AI and ML Applications Growth
"With cybersecurity solutions powered by AI capabilities, vendors can better support enterprises and their cybersecurity teams with less time and manpower investment and higher efficiency to identify the cybersecurity gaps," said Amy Lin, industry analyst at Frost & Sullivan.
Key AI and ML market trends for cybersecurity include:
Artificial Intelligence (AI) and Machine Learning (ML) have been adopted for their automation benefits, from predictive outcomes to advanced data analytics. AI-based cybersecurity can augment the capabilities of IT staff and help organizations deflect cyber threats, according to the latest market study by Frost & Sullivan.
AI and ML Market Development
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 IT 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.
With digital transformation a priority for a majority of enterprises today, there is a proliferation of connected devices, offering customers convenience, efficient services and better experiences. However, this connectivity also increases the potential risk of cyberattacks for enterprises and users.
Cybercriminals are also using more sophisticated methods to attack organizations. These include polymorphic malware, AI and other automated techniques. Enterprises are struggling with a lack of trained staff and cybersecurity expertise to counter the more sophisticated attacks.
These increasing challenges in security operations suggest the need for a smarter, more adaptable, automated and predictive security strategy. AI and ML are increasingly being developed by security companies to strengthen their competitiveness using their own AI or ML algorithms to empower security products and augment the capabilities of existing IT and cybersecurity staff in enterprises.
AI and ML are being incorporated into all stages of cybersecurity to enable enterprises to adopt a smarter, more proactive and automated approach toward cyber defense, including threat prevention or protection, threat detection or hunting, and threat response to predictive security strategies.
While technology startups have been the most proactive in introducing multiple AI-enabled security offerings into the market, larger IT vendors have also incorporated AI and ML into their existing enterprise security solutions.
Outlook for AI and ML Applications Growth
"With cybersecurity solutions powered by AI capabilities, vendors can better support enterprises and their cybersecurity teams with less time and manpower investment and higher efficiency to identify the cybersecurity gaps," said Amy Lin, industry analyst at Frost & Sullivan.
Key AI and ML market trends for cybersecurity include:
- Embracing and incorporating AI-enabled capabilities into exiting solutions to intensify the competitive advantage.
- Supporting a more holistic cybersecurity framework from detection to response and further prediction.
- Assisting cybersecurity expert teams on operations with lower false-positive rates and enhancing their ability to react.