Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Hybrid cloud backup is a data protection strategy that combines both on-premises and cloud-based storage for backup data. This approach stands out from other backup strategies by including a cloud component to enhance redundancy, availability, resilience, and cost efficiency.

Josh Rector is the Compliance Director, Public Sector at Ace of Cloud, a security and compliance consulting firm, certified CMMC Third-Party Assessor Organization (C3PAO), and Registered Provider Organization (RPO). With more than a decade of experience in cybersecurity compliance, he has worked both sides of the assessment table, leading internal and external assessments, serving as ISSO for systems at federal agencies, and guiding cloud service providers through the FedRAMP authorization process.

Two significant software supply chain cybersecurity attacks, seven days apart, with one hundred and eighty million weekly downloads between them. The chaos from development teams to the boardroom is real. And the pace is only going to get faster. Much, much faster…

Conversations at RSA 2026 circled back to the same topic: identity is the foundation of AI agent security. While it’s understandable, it’s the wrong way to look at things. Identity tells you who showed up. It says nothing about whether what they did made sense.

One man’s perspective on RSA 2026 and what the AI agent security market actually looks like up close. Every year at RSA, there's a theme, not the official one printed on the lanyards, but the real one. The one that shows up in every booth conversation, every hallway argument, every dinner where people finally say what they wouldn't say on a panel. A few years back, it was cloud. Then zero trust took over and held the room for a while. XDR came through and confused everyone. Identity had its moment.

Most mobile security workflows end in a familiar way. A scan runs, a report is generated, and the output looks reassuring. There are no critical issues, maybe a few medium findings, nothing that blocks a release. The process completes, the team moves forward, and the app ships. At that moment, the assumption is clear. The app has been tested. The risk is understood. But there is a question that rarely gets asked, and it changes the entire conversation.

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.