Trusted AI

AI Transparency Is Now a Product Decision

Disclosure, labeling and content provenance after the rules went live in the US and Europe

Sep 4 • Jon Knisley

4

August 2026

Agent Identity, Not Model Security, Is the Missing Control Plane

What your agents can access, who authorized it, and why no one can fully explain it

Aug 15 • Jon Knisley

3

July 2026

Your Agents Have Authority Nobody Granted Them

Delegation, scope and the governance question that models never forced you to answer

Jul 31 • Jon Knisley

4

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

4

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

3

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

4

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

4

The Feedback Gap

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

May 27 • Jon Knisley

4

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

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

May 16 • Jon Knisley

3

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

3

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

3

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

3