The AI Security Paradox: Asymmetric Threats and the Future of Defense

Software engineering is undergoing its biggest transformation in decades, but application security is shifting in the opposite direction. While AI tools allow developers to write code faster than ever, foundation models trained on decades of public code are accelerating the volume of vulnerable software flowing into production. At the same time, AI grants attackers infinite patience and speed—enabling automated reconnaissance and rapid exploit generation against large, complex enterprise attack surfaces.

This creates a paradox: security hasn't improved by default, and organizations are effectively paying a "token tax" to scan and fix the very vulnerabilities generated by AI.

In this session, Ammar Alim (Product Security Engineering Leader at Adobe) breaks down how developers and security practitioners across all experience levels can rebalance this equation. We will explore:

The "Wounded Buffalo" Effect: Why legacy codebases and AI-driven attack vectors give adversaries a short-term advantage.

The Token Tax Fallacy: Why buying off-the-shelf security tokens won't solve the security crisis without deep architectural integration.

Building Next-Era Defense Systems: How to combine deep domain/business logic awareness with custom AI engineering (RAG, knowledge graphs, and autonomous self-healing agents) to turn reactive security into automated, context-aware pipelines.