Barno is a consultant and researcher specializing in AI security, data engineering, and machine learning security. She works at the intersection of offensive and defensive AI security & developing methodologies to protect AI systems from adversarial threats across the full stack, from data pipelines and ML models to LLM-powered applications and autonomous agents. Additionally in her role as an AI security trainer Barno has designed and delivered hands-on training programs covering AI red teaming, prompt injection, adversarial ML, RAG and vector database security, and the OWASP Top 10 for LLM and Agentic AI. Her training approach is practitioner-first: participants attack and defend real-world AI systems, leaving with techniques they can apply immediately. She is actively involved in AI governance and evaluation efforts at the national level and regularly contributes to the AI security community through speaking engagements, publications, and conference submissions. She bridges the gap between rapid AI adoption and the critical need for robust, field-tested defenses in an evolving threat landscape.