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
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.”
These are not the same thing.
Not even close.
AI excels at correlation. Humans live in meaning. And CISOs stand at the awkward intersection, defending systems that are predictive but not purposeful.
Prediction ≠ Understanding
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.
If a model recommends access revocation for anomalous behavior, it isn’t “concerned” about insider threat risk.
If it flags a vulnerability, it isn’t “worried” about exploitation.
It has no fear, no context, no aspirations for Tuesday to go well.
This gap may seem academic—until it’s not.
Why this matters for security leaders
CISOs are already painfully aware that a system can be technically correct but practically dangerous.
Think of:
AI introduces a similar dynamic at scale.
We’re building systems that can perform without caring—and that means our meaning-making layer becomes more important, not less.
Humans still define the “why”
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?”
It has no values.
That’s the work of people.
People who understand consequences.
People who get to die—which means we also get to care.
Next in the series
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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