What Jo Covers
Jo Peterson’s work centers on the intersection of cloud strategy, cloud security, cybersecurity and AI risk — the three forces reshaping enterprise technology. This page gives you a high-level view of each, with direct access to the video insights and analysis shaping her coverage.
AI-Enhanced Operations (AIOps)
How AI automates and optimizes cloud operations
This area looks at how AI and machine learning are used to run cloud environments more efficiently and reliably—from detecting issues before outages occur to automating remediation, capacity planning, and cost optimization. The focus is on reducing operational complexity while improving performance, resilience, and security.
In practice: predictive operations, automated incident response, smarter cost and capacity management.
AI-Enhanced Cloud Architecture
How cloud platforms are redesigned for AI-first workloads
This coverage examines how cloud architectures are evolving to support modern AI workloads, including large-scale training, inference, data pipelines, and agent-based systems. AI is not just hosted in the cloud—it increasingly shapes how cloud infrastructure is designed, optimized, and scaled.
In practice: AI-native infrastructure, intelligent resource orchestration, architectures built for models, data, and agents.
AI Security
How organizations protect AI systems, models, data and infrastructure
This coverage examines securing AI Systems and AI for Security
- Securing AI Systems: Protecting the AI pipeline (data, models, and infrastructure) from attacks, for example, stealing models, manipulating training data, or exploiting vulnerabilities in large language models.
- AI for Security: Utilizing AI/Machine Learning (ML) to enhance traditional cybersecurity efforts, such as automating threat detection, analyzing behavioral patterns, and responding to incidents in real-time.
In practice: AI security is essential for building trust in AI systems and preventing the exploitation of these technologies.