As enterprises adopt AI-native architectures to drive innovation and automation, the software development lifecycle is evolving rapidly — shifting from traditional DevOps to AIOps. In this new paradigm, models, data, and code co-exist in a unified pipeline, enabling continuous learning and deployment of intelligent systems. But this transformation brings with it a drastically expanded and undersecured attack surface.
In this session, we’ll explore how our organization is addressing these risks through hands-on experience securing AI-enabled platforms like MiQ Sigma — a multi-agent, LLM-powered system that automates campaign planning and orchestration across hundreds of data sources and DSPs. We’ll walk through the technical and governance challenges we faced securing a dynamic AI supply chain, and the solutions we implemented across data ingestion, model deployment, and inference execution.
You’ll gain actionable insights into:
The anatomy of an AI-integrated software pipeline, and how it differs from traditional DevOps
Unique supply chain threats in AI systems, including model poisoning, prompt injection, and malicious pretrained models
Our internal security blueprint for protecting LLM workflows — from data integrity checks to prompt pipeline hardening
Best practices for securing CI/CD pipelines that manage AI artifacts
Guardrails for continuous training (CT) and safe inference in production
Approaches to ensure compliance with evolving AI regulations (e.g., GDPR, EU AI Act)
Attendees will leave with a real-world playbook for securing AI-native environments — balancing innovation with resilience, and aligning DevSecOps principles with the demands of modern AIOps.