Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As a kid, fruit punch always seemed like a magical drink. A mix of orange, cherry, apple, and cranberry created a unique flavor that differed substantially from any one juice. These hybrid drinks not only quenched thirst but their complexity made it difficult to truly recreate them by hand.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo The AI SOC is cybersecurity’s fastest-growing category, and for very good reason. Machine-speed threats demand machine-speed responses, and the $82.45 billion market forming around this reality reflects just how urgent that need has become. The Torq AI SOC Platform delivers agentic insights and the ability to streamline action across the full security stack.

Your platform team deployed eBPF-based runtime sensors on AKS last week. Defender for Containers is enabled. Azure Policy is enforcing pod security standards across your AI workload namespaces. And your Observe pillar is still blind — because nobody enabled the Diagnostic Setting that routes kube-audit logs to the Log Analytics workspace where your tooling can actually consume them.

Cloud environments create a flood of security signals, often reaching tens of thousands per day depending on the organization’s size. Security engineers and analysts spend a disproportionate share of their time triaging these signals instead of acting on legitimate threats. But the time-intensive parts of that work, such as identifying related signals and building a timeline, can be handled systematically, leaving teams free to focus on what actually requires human judgment.

As AI adoption accelerates on Google Cloud, the challenge for most teams today is no longer just building AI-powered applications. It’s also managing the full AI stack from end to end, including data pipelines, infrastructure, release process, and security operations. Many teams are monitoring these layers with different tools, creating complexity, fragmenting visibility, and slowing decisions on what to do next.

If you are not using AI to defend against AI, you will lose. But for organizations operating in air-gapped environments, the path to AI-driven defense can be blocked by the very isolation that protects them. Today, we're announcing that Elastic Security is now the embedded security layer for Google Distributed Cloud (GDC) air-gapped environments, expanding our collaboration with Google Cloud.

Complexity has become a defining security challenge as organizations expand across hybrid and multi-cloud environments. In fact, 52% of surveyed organizations ranked multi/hybrid cloud complexity among their top three infrastructure concerns.1 This complexity creates fragmented visibility across cloud providers, workloads, and Kubernetes environments — gaps that adversaries increasingly exploit to move undetected.

In enterprise software, winning a deal is not just about product fit. The buying process matters just as much. Even when a customer is committed to moving forward, procurement friction can slow or stall a deal. New vendor setup, contract reviews, billing workflows, approval chains, and budget constraints all add complexity that extends timelines and increases the risk of deals falling apart. That is why procurement flexibility is not a back-end operational concern. It is a customer experience issue.

For us humans to interact with the online world, we need a gateway: keyboard, screen, browser, device. What is called "human detection" online are patterns that humans use when interacting with such devices.

Most application migration projects fail the same way: someone picks a single strategy for the entire portfolio, then tries to force every workload into it. Lift-and-shift everything to meet a data centre exit deadline. Refactor everything because someone read a cloud-native manifesto. Retire nothing because no one wants to make the decision. AWS’s 7 Rs framework exists to prevent that.