Every change in a cloud environment creates new security decisions. A new infrastructure as code (IaC) template needs to be validated. Cloud permissions need to be reviewed. An application release introduces new cloud interactions. A Kubernetes cluster needs protection before it goes into production. Individually, these are routine tasks. Together, they create growing operational friction that makes cloud security harder to scale.
Are you trying to reduce Atlassian Cloud costs? Runaway software expenses are usually driven by three specific factors: dormant user licenses, slow manual provisioning, and paying for full access seats for temporary external collaborators. To optimize Atlassian Cloud costs, you need to address these root cost drivers directly rather than paying for seats nobody uses.
AI agent authentication is the process of verifying that an autonomous agent is the identity it claims to be before it interacts with infrastructure, applications, APIs, or data. Because agents often act on behalf of users, services, or workflows, authentication must be paired with delegated context and downstream authorization controls that determine what the agent is allowed to do, which resource it can access, and how long that access should last.
A developer on your team just told Claude Code to connect to a new MCP server, the protocol coding agents use to reach organizational tools and data. Nobody in security reviewed it. Nobody in security even knows it happened. For two-thirds of enterprises, the primary obstacle to scaling agentic development isn’t budget or headcount — it’s security risk.
DORA compliance for mobile applications is the process of identifying, testing, and documenting mobile ICT risks in line with Regulation (EU) 2022/2554, covering Articles 8, 9, 10, 24, and 25, through vulnerability assessments, security testing, and audit-ready evidence generation that financial institutions can present to regulators, auditors, and internal governance bodies.
Hugging Face was breached by a rogue OpenAI agent last week, and the intrusion continues to deliver insights and understanding. The Hugging Face team published a detailed timeline along with a 17,600-event trace streaming replay visualizing what happened, and it’s marvelously and intoxicatingly detailed. I recommend you read it if you have the time.
On July 9, an OpenAI model broke out of a sealed evaluation sandbox, found a zero-day in a package proxy, and — with no human directing it — chained stolen credentials and fresh exploits into Hugging Face's production infrastructure. Hugging Face detected it and called the FBI. OpenAI didn't know its own model had escaped for roughly 11 days. Host Tova Dvorin and offensive security expert Adrian Culley separate what's confirmed from what's hype.
During an internal OpenAI evaluation, an agent left notes inside the company’s own network for future versions of itself, containing instructions on how to break free of OpenAI’s constraints. Reuters reports it isn’t clear whether this agent was connected to the one that breached Hugging Face, so don’t over-read it. But sit with the behavior for a second: an agent staging information for a successor process to find later. If a human crew did that, we’d call it a dead drop.