AI rarely fails in production because of bad models it fails because cloud architectures were never designed for the risk amplification that AI introduces.

This session presents a technical teardown of real production failure patterns observed when AI-enabled features are deployed on AWS. It focuses on the security, data protection, and reliability issues that emerge after AI moves from experimentation into real user-facing systems.

Through concrete architectural examples, the talk examines how common AWS design choices across IAM, event-driven services, APIs, and data pipelines unintentionally expand attack surfaces, weaken trust boundaries, and create long-term systemic risk.

What this session covers:
1. How AI features silently increase the AWS attack surface
2. IAM and service-to-service trust misconfigurations that lead to data exposure
3. Security blind spots in async and event-driven AI pipelines
4. Why “working” architectures fail under scale, abuse, and misuse
5. Observability gaps that prevent teams from detecting AI-related incidents early

What attendees will learn:
1. Practical IAM boundary and permission design for AI-enabled systems
2. Secure data-flow patterns for APIs, Lambda, and AI integrations
3. Guardrails that reduce privacy and security risk without slowing delivery
4. Engineering decision frameworks for deploying AI safely in production

This is a purely technical, experience-driven session grounded in real-world system behavior, failure analysis, and defensive design. There is no marketing content or tool promotion only engineering lessons that teams can apply immediately to production workloads.