Episode 2: Prediction Is Not Meaning: Why “Knowing” Isn’t the Same as Caring

Prediction Is Not Meaning: Why “Knowing” Isn’t the Same as Caring - By Merritt Baer, CSO, Enkrypt AI

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One of the biggest misunderstandings in AI right now—especially among non-technical executives—is the leap from “the model predicted correctly” to “the model understands.”

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These are not the same thing.

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Not even close.

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AI excels at correlation. Humans live in meaning. And CISOs stand at the awkward intersection, defending systems that are predictive but not purposeful.

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Prediction ≠ Understanding

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When an AI model “knows” something, what it really has is compressed statistical experience, not insight. It’s storing the ghosts of patterns, not participating in the world.

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If a model recommends access revocation for anomalous behavior, it isn’t “concerned” about insider threat risk.

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If it flags a vulnerability, it isn’t “worried” about exploitation.

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It has no fear, no context, no aspirations for Tuesday to go well.

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This gap may seem academic—until it’s not.

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Why this matters for security leaders

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CISOs are already painfully aware that a system can be technically correct but practically dangerous.

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Think of:

AI introduces a similar dynamic at scale.

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We’re building systems that can perform without caring—and that means our meaning-making layer becomes more important, not less.

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Humans still define the “why”

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As we architect, secure, test, and deploy AI, the burden falls back on us to articulate intention:

AI isn’t going to stop us and say, “Are you sure this aligns with your values?”

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It has no values.

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That’s the work of people.

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People who understand consequences.

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People who get to die—which means we also get to care.

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Next in the series

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In the third installment, we’ll dig into something that’s becoming increasingly clear: security is now stewardship—not just of data and systems, but of the humans shaped by the outcomes.

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