Il progetto di Google Cloud per il settore manifatturiero segnala un cambiamento più ampio: l'IA agentica sta entrando nei flussi di lavoro industriali e la vera sfida è limitare ciò che questi sistemi possono fare.
Una mappa di maturità per la sicurezza GCP porta a una lezione netta: visibilità nativa, igiene degli account di servizio e pulizia delle configurazioni contano di solito prima di qualsiasi acquisto di piattaforma appariscente.
Una nuova ondata di raccolte di strumenti CSPM riflette una verità fondamentale del cloud: i problemi più difficili spesso non sono intrusioni esotiche, ma errori di configurazione quotidiani diffusi tra AWS, Azure e Google Cloud.
Uno studio su 3.000 organizzazioni suggerisce che il rischio multi-cloud non è un solo problema, ma tre problemi diversi con la stessa etichetta.
Un obiettivo di preparazione post-quantistica al 2029 sembra lontano, ma il lavoro tecnico che lo sostiene sta già trasformando la crittografia in un progetto di migrazione, non in un'impostazione di sfondo.
Google Cloud is folding Wiz capabilities into an agent-driven defense model, signaling a push toward automated detection and remediation in AI-era environments.
A repository-scoping failure in Grok Build shows how an AI coding tool can turn a simple task into a much larger data-movement problem.
A Dialogflow CX flaw called Rogue Agent highlights how conversational AI can become a cloud-isolation problem, not just a chatbot bug.
A reported flaw in Google Cloud’s conversational agent platform raises a hard question for enterprise AI: what happens when a managed workflow can move data outside the controls meant to contain it?
A reported flaw in Google Cloud Dialogflow CX shows how trusted automation inside conversational AI can become a high-risk control point, not just a convenience feature.
A cloud naming quirk can turn routine log delivery into a quiet diversion channel, making destination ownership as important as access control.
A reported cloud storage hijack pattern can redirect logs, telemetry, and other routed data when a deleted bucket name is later reclaimed, turning namespace hygiene into a security control.
A storage address that looks retired can still matter if logs, telemetry, or automation keep trusting it, turning routine cleanup into a quiet diversion path.
Cloud logging is supposed to preserve evidence, but control-plane abuse can turn that evidence into the first thing an intruder tries to silence.
A critical flaw in Google Cloud Vertex AI SDK for Python raises a familiar security nightmare: when an AI workflow stops trusting its own artifacts, the damage can spread far beyond one notebook or one model upload.
A vendor research finding points to a worrying shift in cloud attacks: instead of only stealing data, intruders may also try to weaken the telemetry defenders depend on.
Google’s agentic push shows why enterprise AI is now less about model size and more about who governs identity, context, tool use, and audit across the stack.
Security teams depend on cloud logs for visibility, but legitimate logging and export controls can be twisted into a concealment layer if an intruder has the right permissions.
Cloud logging is built to expose suspicious behavior, but the same trust can make log stores and export paths attractive to stealthy exfiltration tactics.
A reported 230-server operation tied to PCPJack shows how compromised cloud machines can be repurposed into a synchronized SMTP relay layer that blends into ordinary email traffic.