Shadow AI needs a visible route back, not only detection: a low-friction request that binds each agent to a purpose, owner, data scope, tool scope, expiry, and revocation path. Which telemetry could reveal hidden use without turning disclosure into punishment?

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I’d add ownership. Every agentic workflow needs a named owner, baseline metric, exception path and rollback rule. Otherwise “data readiness” becomes a permanent pre-launch excuse.

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Tokenade works on this layer, and cost is the one people meter last. Once inference is its own line item, the number to watch is tokens per finished task.

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AI agents are writing, reviewing, and shipping code. Who's securing it? At the LA Cybersecurity Summit, Legit Security stood out for its approach to securing vibe coding and agentic AI development. My coverage of Liav Caspi's session and why it matters. decodedigitalworks.com/coverage/ai-...

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AI Reality Check Organizations talk about agentic AI, few operationalize it. The challenge isn't AI, it's data readiness, governance, and execution. By 2027, how much of your data is truly usable by AI agents? Where's the biggest gap: strategy, data, governance, or execution?

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