AI & ML

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NSX as the Zero Trust Control Fabric: From Central Policy to Distributed Containment

TL;DR The strongest way to understand NSX security is not as a bigger firewall at the edge, but as a control fabric that turns workload context into distributed enforcement. The management plane defines intent, dynamic groups supply context, the Distributed Firewall applies policy close to each protected workload, and telemetry shows whether the model matches

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AI Feedback Loops: Building Systems That Stay Correctable

TL;DR AI feedback loops do not improve a system merely by preserving more outputs. An unsupported explanation can return through retrieval as apparent evidence, even when model weights never change. Separate generated material from approved knowledge, identify which component a correction should change, and enforce permissions outside the model. Test withdrawal, retries, policy changes, and

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AI Uncertainty: Why Confidence Scores Are Not Enough

TL;DR AI uncertainty is not one measurement. Token entropy describes prediction variability, semantic entropy examines variation in meaning, and calibration tests whether confidence estimates correspond to observed correctness. None replaces current evidence or grants permission to act. For an enterprise assistant, define the claim being evaluated, preserve missing evidence as missing, and provide a deliberate

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