Artificial intelligence is rapidly becoming embedded in modern applications from fraud detection and identity verification to autonomous decision systems while quantum computing advances threaten the cryptographic foundations that protect these systems today. The convergence creates a new class of risk: AI applications that rely on vulnerable encryption, exposed training pipelines, and unprotected model assets may become prime targets in a post-quantum landscape.
This session examines how quantum computing impacts the security assumptions behind AI and machine learning systems, including TLS, digital signatures, key exchange, and data integrity mechanisms used across APIs, pipelines, and MLOps workflows. We translate these risks into practical attack scenarios such as model theft, training data exposure, supply-chain compromise, and long-term “harvest now, decrypt later” threats.
Participants will learn actionable strategies to future-proof AI deployments using post-quantum cryptography, crypto-agility, secure model lifecycle controls, and AI-driven threat detection. Through architecture patterns, tooling recommendations, and real-world use cases, this talk provides a pragmatic roadmap for AppSec, cloud security, and SOC teams to secure AI systems against both current and emerging quantum-era threats.
Attendees will leave with concrete steps to assess risk, modernize cryptography, and build resilient AI applications that remain secure as quantum capabilities mature.