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AI infrastructure introduces a new class of risk: interdependent data, models, identities, and pipelines can fail out of sync, leaving systems operational but no longer trustworthy. At the same time, a growing CIO/CISO disconnect is creating unclear ownership and slowing response when trust is lost. In this session, Commvault will show how to close that gap with a shared control plane approach: governing access, protecting the full AI stack, and recovering systems to a clean, coherent, and trusted state.
The presentation elaborated on how AI has significantly complicated traditional IT stacks, transforming previously linear and human-paced data flows into chaotic, nonlinear, multipath, stateful, and agentic processes. This expansion has led to an explosion of data copies, identities, and threat vectors, creating immense confusion regarding ownership and responsibility for threat response, patching, and control implementation. Commvault has responded by shifting its focus from mere data protection to a comprehensive “resilience operations” (ResOps) methodology over the last five years. This strategic pivot aims to help organizations recover not just quickly but also cleanly and reliably from adverse events, addressing a critical board-level concern that extends beyond traditional disaster recovery and cybersecurity to encompass new risks such as rogue AI agents and sensitive data handling within the AI ecosystem.
The speaker further detailed the new, complex recovery dependencies introduced by AI, including the critical importance of source, sensitivity, access, table, embedding, vector index, and feature versions. These elements must be recovered in sync for AI applications to function correctly and maintain trust. Simply restoring individual workloads, as was common in the past, is no longer sufficient; instead, the entire AI system must be brought back to a consistent, coherent, and trusted state. Commvault addresses this by developing reusable, agnostic frameworks designed to adapt to the rapidly evolving AI landscape, including new data formats and vector databases. This proactive approach provides a crucial “safety net,” automating recovery, ensuring data quality, and offering auditability and explainability, especially important in mitigating risks from shadow AI and the rapid, often unpredictable actions of AI agents where human-speed responses are inadequate.
Personnel: Chris Bevil, Michael Fasulo
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