In Part 1, we established how to implement a universal capture layer across Gemini CLI, Antigravity, Claude Code, Cursor, Codex, and API proxies with pre-execution endpoint DLP.
Once capture is active across the engineering organization, leadership encounters a critical operational challenge: How do we monitor, attribute, and govern AI usage across hundreds of developers, models, and codebases?
Without an organizational monitoring layer, enterprises suffer from three systemic blindspots:
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│ THE ENTERPRISE AI MONITORING GAP │
