Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For organizations operating in the cloud, visibility is everything. You need a reliable source of truth to answer “who did what, when, and where,” whether you’re investigating a security incident, chasing compliance goals, or monitoring operational activity. Enter the Sumo Logic CloudTrail App, your go-to solution for transforming raw AWS CloudTrail logs into meaningful, actionable insights.

SaaS sprawl and shadow IT create significant security vulnerabilities, exposing organizations to unmanaged apps, unauthorized access, and compliance risks. It’s simply not enough to secure access to the applications you’re actively managing. You also need to secure everything else. That’s one of the reasons we acquired Trelica earlier this year. Organizations must be capable of identifying and managing applications that are used outside of IT and security’s purview.

AI agents are rapidly transforming how software is accessed, operated, and integrated, such as automating workflows, calling APIs, and interacting with tools and SaaS platforms on behalf of users. This paradigm unlocks powerful new capabilities, but it also raises urgent questions about how sensitive data, especially credentials and secrets, should be managed.

We’re at a major inflection point in how software operates. And I don’t say that lightly. For the past decade, we’ve seen a steady evolution toward microservices, APIs, and cloud-native architectures. But Agentic AI is something different. We’re no longer talking about static services. We’re now dealing with autonomous agents that reason, remember, and act in real-time across live environments.

Mobile apps are everywhere. From the moment we wake up and check the weather to staying connected with friends and family, our lives are woven together by apps. They manage our money, track our workouts, store our memories, and even help us find love. But with this convenience comes a hidden cost: our privacy. Every tap, every swipe, every “allow” permission is a potential gateway for data to flow, sometimes to places we never intended.

As AI capabilities grow, organizations are adopting it for compliance monitoring, risk analysis, and data processing. However, increased use also introduces new risks, making strict regulation essential, especially in sectors where sensitive data is involved—like finance, insurance, and healthcare. Mishandling this information can lead to reputational damage, legal action, or hefty fines.

There was a time when “AI red teaming” sounded like a novelty. Now, it’s fast becoming table stakes. If your organization is shipping machine learning or LLM-powered systems into the real world (especially in sensitive domains), you need to know how those systems behave under pressure. That’s where AI red teaming tools come in. These tools help teams stress-test AI the way it will actually be used (and misused).