AI Arms Race Intensifies as Endpoints Emerge as Critical Battleground for Cybersecurity

The AI arms race between cybersecurity firms and attackers is intensifying, with endpoints emerging as a critical battleground for AI companies’ valuable intellectual property, financials, and future R&D plans.

Malware-free attacks on the rise: Adversaries are increasingly using legitimate tools and fileless execution techniques to breach endpoints undetected, making AI companies a prime target:

• CrowdStrike reports that 71% of detections were malware-free, and the use of remote monitoring and management tools for malware-free attacks skyrocketed by 312% year-over-year in 2023.
• Attackers exploit gaps such as outdated endpoint patches, lack of multi-factor authentication, and privilege escalation to launch sophisticated intrusion attempts.

Telemetry data crucial for endpoint security: AI companies are leveraging real-time telemetry data to identify anomalous patterns, predict breaches, and gain granular insights into endpoint configurations:

• Leading vendors like BitDefender, CrowdStrike, Cisco, and others capture and analyze telemetry data to derive endpoint analytics and predictions as part of their extended detection and response (XDR) systems.
• Cisco and Palo Alto Networks are doubling down on native AI integration and extensive telemetry data collection to bolster their cybersecurity offerings.

Indicators of attack and compromise: Companies use real-time telemetry data to calculate indicators of attack (IOAs) and indicators of compromise (IOCs), enabling proactive threat detection and response:

• IOAs focus on detecting attackers’ intent and goals, while IOCs provide forensic evidence of a network breach.
• CrowdStrike has developed AI-powered IOAs that operate synchronously with other defensive layers to improve detection and response capabilities against complex threats.

Generative AI’s potential in closing the endpoint security gap: Roundtable participants identified ten key areas where generative AI can significantly enhance endpoint security:

• Continuous network telemetry monitoring and verification
• Real-time threat detection and response
• Behavioral analysis and anomaly detection
• Reduction of false positives through adaptive learning
• Automated threat response
• Enhanced real-time visibility and correlation
• More accurate threat hunting
• Automating manual SOC workloads
• More precise predictive analytics

Adapting to the era of weaponized AI: As attackers leverage AI to exploit identity and endpoint gaps, the cybersecurity industry must harness the full potential of AI and machine learning technologies to stay ahead of evolving threats and protect the organizations they serve.