Testing & Security

Software moved faster than governance, faster than architecture, and faster than most teams could safely absorb, and that speed exposed a new class of failures where AI-generated code looked correct in isolation yet quietly broke security guarantees, drifted from service contracts, and collapsed

Context and Stakes Credentials multiplied faster than services could be secured, and pipelines turned into high-speed conduits for risk unless secrets were handled with rigor from commit to production. That pressure reshaped how teams think about identity, trust, and automation. In cloud-native

From Push-to-Prod to Proof: Why Modern Teams Redesign the Path to Production Release nerves have been eating roadmaps for years, and practitioners from product shops to platform teams keep reporting the same pattern: pipelines that look fast on paper collapse under manual reviews, fragile tests,

Metrics that promise safety by counting lines executed crumble when a missed permission check drains funds or delays medication, and that gap between comforting numbers and consequential reality sets the stage for a reset in how quality is planned, measured, and delivered in banking and healthcare.

Boardrooms are betting that an AI engineer can ship production code reliably, safely, and at scale, and that wager now underpins Cognition AI’s bid to raise a round that could value the company near $25 billion. The catalyst is Devin, an autonomous software developer that doesn’t just autocomplete

A New Pace of Software Risk An obscure configuration bug that once languished in a backlog for months can now be chained with a permissive log parser and an overlooked API edge case to yield a working exploit in a single afternoon. That is the promise—and the problem—unlocked when frontier AI is