In today’s cybersecurity landscape, attackers are leveraging AI—shouldn’t defenders do the same? This session explores how security teams can integrate AI-driven threat intelligence into real-world defense strategies. We’ll move beyond theoretical discussions to practical applications of Large Language Models (LLMs), vector databases, and machine learning in automating threat detection, investigation, and response.

Attendees will gain technical insights into optimizing AI models for real-time attack detection, adversarial threat mitigation, and risk assessment. The session will also address ethical considerations and bias in AI-driven security models, ensuring AI is used responsibly and effectively in cybersecurity operations.

Understanding Threat Detection and Alert Management Challenges

• Threat detection and response capabilities
• The volume problem: How SOC analysts are overwhelmed by security alerts
• Context switching costs during incident response
• Impact on development velocity and security posture

AI for MDR/EDR for Security Overview

• AI-powered capabilities specific to threat detection and alert management
• Integration with LLMs, Vector Databases and Threat intelligence platform
• How it processes and understands security alerts across different systems

Alert Summarization Capabilities

• How AI MDR/EDR transforms verbose alerts into actionable intelligence
• Natural language summaries of complex security incidents
• Pattern recognition across seemingly unrelated alerts
• Extracting the signal from the noise

Alert Prioritization Workflows

• Risk-based prioritization methodology
• Contextual enrichment of alerts with organizational data
• Identifying the most critical threats requiring immediate attention

A key highlight will be a technical walkthrough of an AI-powered security assistant that enhances Security Operations Center (SOC) workflows—without vendor lock-in.

Target Audience: This session is primarily aimed at Security Professionals and Software Engineers, as they are responsible for designing and implementing threat detection and response systems, including applications, APIs, and LLMs and backend systems.