Travel's agentic AI moment is arriving slower than the headlines suggest
Over 90% of travelers trust AI-generated travel information, but almost none want the AI to act on that information independently.
Non-human identities are multiplying faster than security teams can track them
Non-human identities outnumber humans 80:1 in many orgs, but most teams lack visibility into AI agents' permissions, ownership, or lifecycle management—creating major governance gaps.
Your AI Is a Black Box- Here Are 3 Keys to Unlock It
By demanding useful explanations, installing human failsafes, and requiring clear "nutrition labels" for our AI, we can begin to pry open the black box.
From testing to reviewing: evaluating AI agents that run 30-step workflows
AI agents fail in production not because of bad architecture, but because we test them like traditional software. Complex 30-step workflows can't be tested—they must be reviewed like human work. This shift changes everything for legal and product teams.
Microsoft and NYU tested what happens when AI becomes employee #1
The research shows we're moving from AI-as-tool to AI-as-colleague, which means rethinking how we structure accountability and human oversight.
Operationalizing NIST AI Risk Model Framework: Beyond Accuracy and Checklists
The NIST framework provides the map, but fostering a true culture of responsibility is the journey.
A three-tier risk model for Agents based on production status and reversibility
IBM's framework begins with a reversibility assessment that determines which of three automation tiers applies to a given task.
When insurers won't touch AI, you're self-insuring by default
The companies that insure oil rigs and rocket launches won't touch AI systems. They can't model the failure modes well enough to price the risk. For product teams, that means you're absorbing liability that traditional risk transfer won't cover.
