The integration of Artificial Intelligence into IT Operations (AIOps) is transforming how we manage infrastructure, but the invisible supply chain of AI—the data itself—requires rigorous governance. When open-source projects gain advanced capabilities like Retrieval-Augmented Generation (RAG) and autonomous agents, maintainers are forced to confront complex privacy realities.

In this session, I will share architectural and legal insights from the trenches of modernizing open-source projects with AIOps. We will break down the challenge of managing a mixed-data environment where sensitive customer data, vendor-supplied data, and publicly scraped information must coexist without cross-contamination. Because securing an on-prem LLM deployment is much more than just repackaging cloud software, we will dive deep into designing AI features for strict, air-gapped environments where “phone home” telemetry and hidden network dependencies are strictly prohibited.

Furthermore, we will demystify the mechanics of data provenance in RAG architectures. Just as modern AI models can provide source citations, we must engineer systems that track which specific documents and chunks influenced an output. You will learn how to map data lineage to ensure auditability, allowing users to trace the exact origins and transformations of the data driving their AIOps tools.

Finally, we will bridge the gap between code and compliance. We will discuss how to craft End User License Agreements (EULAs) that clearly document the purpose of AI workflows, define legal authorization for data usage, and establish hard restrictions on what cannot be done with user data.

Requirements - basic understanding of how privacy is expressed in a EULA
Audience - all levels
Session duration 45 minutes