"The breakneck pace of AI deployment across enterprises is creating a monumental challenge for executives and company boards. In contrast to traditional IT systems, AI data and related ecosystems, which encompass everything from LLM models and training data to custom prompt data, have emerged as valuable intellectual property. They often represent millions of dollars in investment and months or even years of engineering effort."
"Any loss of AI data can cause catastrophic loss for an organization, especially those that have integrated crucial processes, such as decision-making and risk analysis, with AI systems. If AI systems get compromised or the integrity of their results comes into doubt, it can cause loss of both customer trust and revenue. In some edge cases, you may even need to build everything up from scratch."
"Step 1: Identify and classify your "crown jewel" AI assets As an executive, the first action that you need to undertake is getting your team to perform a comprehensive audit of what actually needs to be secured. It is important to realize the full scope of AI infrastructure and its complexity. Typically, the backup strategy should make allowances for different kinds of asset categories."
AI data and related ecosystems, including LLM models, training data, and custom prompt data, constitute valuable intellectual property that often represents millions of dollars and months or years of engineering effort. Loss or compromise of AI data can cause catastrophic organizational damage, undermining decision-making, risk analysis, customer trust, and revenue, and may require rebuilding systems from scratch in some cases. Executives must make strategic decisions to secure AI data and ensure business continuity. Organizations should identify and classify crown-jewel AI assets, design strategic backup architectures, and integrate backups with machine learning operations rather than relying on manual backups.
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