Uploaded August 2025 | Updated September 2026, 9 hours ago
Justin Page (Booz Allen Hamilton, US)
Justin Page is a leader in cybersecurity innovation and the head of Booz Allen Hamilton's DarkLabs Applied Research, with over 20 years of experience in delivering advanced solutions to complex security challenges. He and his team developed multiple agentic systems for cybersecurity challenges from compliance to detection and automated static reverse engineering. His expertise includes intrusion analysis, malware analysis, threat hunting, and cyber threat intelligence against Nation-State Advanced Persistent Threats (APTs) across government and commercial sectors.
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Malware analysis is critical for defending against cyber threats, but traditional approaches are often too slow and resource-intensive for modern needs. This talk explores how agentic AI can transform malware reverse engineering by automating repetitive tasks, improving scalability, and delivering actionable insights more efficiently. Drawing on the experience of designing and implementing an AI-driven system for malware analysis, we'll share key lessons learned, including best practices for agent collaboration, system scalability, and integration with existing tools. Attendees will gain practical strategies for applying these concepts in their own environments, as well as insights into the future of AI in cybersecurity. This session is ideal for professionals interested in enhancing malware analysis workflows and advancing their understanding of AI applications in security.
Justin Page (Booz Allen Hamilton, US)
Justin Page is a leader in cybersecurity innovation and the head of Booz Allen Hamilton's DarkLabs Applied Research, with over 20 years of experience in delivering advanced solutions to complex security challenges. He and his team developed multiple agentic systems for cybersecurity challenges from compliance to detection and automated static reverse engineering. His expertise includes intrusion analysis, malware analysis, threat hunting, and cyber threat intelligence against Nation-State Advanced Persistent Threats (APTs) across government and commercial sectors.
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Malware analysis is critical for defending against cyber threats, but traditional approaches are often too slow and resource-intensive for modern needs. This talk explores how agentic AI can transform malware reverse engineering by automating repetitive tasks, improving scalability, and delivering actionable insights more efficiently. Drawing on the experience of designing and implementing an AI-driven system for malware analysis, we'll share key lessons learned, including best practices for agent collaboration, system scalability, and integration with existing tools. Attendees will gain practical strategies for applying these concepts in their own environments, as well as insights into the future of AI in cybersecurity. This session is ideal for professionals interested in enhancing malware analysis workflows and advancing their understanding of AI applications in security.
