Antivirus, Anti-Malware Lead Demand for AI/ML Tools

Antivirus, Anti-Malware Lead Demand for AI/ML Tools

November 4, 2024 at 06:26PM

Artificial intelligence (AI) and machine learning (ML) are increasingly adopted in cybersecurity, enhancing tools like firewalls and antivirus systems. A Dark Reading survey found significant use in phishing detection and threat response. While many use AI/ML, adoption in areas like fraud detection and user behavior analytics remains developing.

**Meeting Takeaways: AI and Machine Learning in Cybersecurity**

1. **Adoption Trends**: There is a growing adoption of artificial intelligence (AI) and machine learning (ML) tools in cybersecurity, particularly in improving the overall security posture of enterprises.

2. **Key Technologies Utilized**:
– Enterprises are implementing AI and ML across various cybersecurity technologies, including:
– Firewalls
– Endpoint detection and response (EDR) platforms
– Security information and event management (SIEM) systems
– Network traffic analyzers
– Antivirus/anti-malware solutions are notably leading in adoption, utilized by 51% of respondents.

3. **Motivation for Adoption**:
– The competitive landscape between Chief Information Security Officers (CISOs) and cyber adversaries is accelerating the integration of AI/ML tools.
– Increased usage in critical areas:
– Phishing detection (49%)
– Threat detection and response (45%)
– Endpoint security (40%)

4. **Integration in Security Teams**:
– Key areas where AI/ML is applied include:
– Malware analysis (38%)
– Intrusion detection and prevention (35%)
– Threat intelligence (35%)
– Identity and access management (34%)
– Network security analysis (33%)
– Vulnerability management (32%)
– Security information and event management (31%)

5. **Emerging Features**:
– Adoption rates for user behavior analytics/predictive analytics, fraud detection, automated security operations, and automated incident response are lower, indicating that these features are still in development:
– User behavior analytics/predictive analytics (27%)
– Fraud detection (27%)
– Automated security operations (26%)
– Automated incident response (25%)

6. **Further Information**: Participants interested in a deeper understanding of the impact of AI and ML on cybersecurity are encouraged to download the Dark Reading report titled “The State of Artificial Intelligence and Machine Learning in Cybersecurity.”

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