A jolt to the ai-ops status quo Deals that reset the balance of power rarely turn on slogans; they turn on who owns the data that machines learn from, the context that humans trust, and the workflow that fixes problems before they spread across cloud estates. A $3.35 billion bet on Chronosphere
When breaches increasingly begin with valid credentials and trust shifts from networks to context-rich identity, the center of gravity in security moves from guarding walls to governing who and what crosses every digital doorway. That shift has turned identity and access management into the control
Imagine a fortress of data, meticulously guarded with the latest cybersecurity measures, only to be infiltrated not through the front gate but via a forgotten back door—a trusted vendor. In today’s interconnected business landscape, third-party breaches have surged, becoming a silent plague that
Cyber defenders feel the clock start the moment a hash dump surfaces, and in that race between exposure and remediation, Hashcat’s blend of speed, flexibility, and hard-earned pragmatism has become the tool that tips the balance for ethical password recovery and forensic analysis. In an era where
Rising breach costs, sprawling multi-cloud estates, and fast-moving AI deployments have pushed cloud risk to a pace that alert queues and after-the-fact tickets rarely match, forcing security leaders to weigh whether detection-centric tooling can continue to anchor modern defense. Into that tension
As AI applications scale from small proofs of concept into production pipelines that touch code repositories, data lakes, and customer-facing interfaces, the attack surface has sprawled faster than most security stacks can see or control, and that gap has invited threats tuned to exploit model
