Trusted AI

Most AI Governance Is Theater. Yours Doesn’t Have to Be.

Right-sizing NIST, ISO 42001 and the EU AI Act to the risk you actually carry

Jul 18 • Jon Knisley

3

June 2026

Building a Culture of Responsible AI

How training, incentives and everyday rituals turn trustworthy AI from policy into practice

Jun 23 • Jon Knisley

2

Why Trusted AI Programs Stall at the Product Layer

And how to build one that turns trust into adoption rather than friction

Jun 5 • Jon Knisley

3

May 2026

Trusted AI Has a Shelf Life

How to maintain reliability, detect drift, and manage change across the AI lifecycle

May 30 • Jon Knisley

3

The Feedback Gap

How in-product flags, annotation programs, and RLHF-style pipelines turn feedback into safer, smarter AI systems

May 27 • Jon Knisley

3

Evaluating Generative AI: Practical Tests for Safety, Bias and Fairness

How to assess toxicity, hallucinations, representational harms and group fairness

May 16 • Jon Knisley

2

Stop Guessing: How to Evaluate Modern LLM Applications

A practical framework for measuring what matters across prompts, retrieval, and tool behavior

May 11 • Jon Knisley

2

Your AI Passed Testing. Will It Survive Production?

Experiments, staged rollouts, and runtime guardrails that turn deployment risk into repeatable control

May 2 • Jon Knisley

2

April 2026

Why Most LLM Evaluations Fail Before Production

How to build test sets, use synthetic data wisely, and trust LLM-as-a-judge without fooling yourself

Apr 25 • Jon Knisley

2

The Wrong Scorecard: Why Most Organizations Are Failing at AI Evaluation

A decision framework for investing evaluation resources where they matter most

Apr 19 • Jon Knisley

2

Your AI is Running. But is It Working?

The observability stack that makes AI systems debuggable, auditable, and improvable

Apr 9 • Jon Knisley

2

March 2026

From ML Monitoring to AI Observability: What Changes with LLMs and Agents

Closing the visibility gap that separates AI leaders from liabilities

Mar 31 • Jon Knisley

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