2026-09-11 · Fuente：deep-siliconangle.com-p14
As software delivery accelerates and AI-armed adversaries move faster, Black Hat USA 2026 discussions centered on breaking data silos so security and engineering can prioritize risk together.
The wall between security and engineering teams came under pointed scrutiny at Black Hat USA 2026, where the friction created by separated data was a major focus of the conference. The argument advanced there is blunt: as software delivery accelerates and adversaries move faster by putting AI to work, the two functions can no longer operate in silos. Both sides, the reasoning goes, need shared context drawn from production data.
The problem, as framed at the event, is historical rather than sudden. Data separation between security and engineering groups has long created delays in how organizations handle risk. Security teams often find themselves working from an incomplete picture, while engineering teams push software out at an accelerating clip. Because each side holds only part of the relevant data, prioritizing what matters becomes harder, and response to live risk slows at exactly the moment speed is most valuable.
The remedy highlighted at Black Hat is shared context rooted in production data. When security and engineering operate from the same production-grounded view, both sides can prioritize risk on a common basis and respond without the friction that siloed data imposes. In that framing, context is not a reporting layer bolted on after the fact but the mechanism that lets risk decisions happen where the software actually runs.
The timing explains the urgency. Adversaries are moving faster with AI, which raises the cost of every delay introduced by separated data. For organizations shipping software at an accelerating pace, a security function that learns about production state after the fact cannot keep up with opponents that move at machine speed. The emphasis at Black Hat USA 2026 suggests a simple operational test is taking hold: how quickly can a risk surfaced in production be prioritized and acted on jointly by security and engineering, before an AI-accelerated adversary exploits the gap?
Etiquetas: Black Hat USA 2026 · Cybersecurity · DevSecOps · Risk Response · Production Data · AI Threats
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