Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Think back to the old image of a “cyberattack”: a hacker in a dark room trying to smash through a digital firewall to reach a dusty server. In 2026, that’s a relic. Today, the most dangerous threat actors aren’t breaking into your house; they’re walking through the front door with a copied key.

Remote access into OT and ICS environments has always carried risk. But the nature of that risk has changed. Threat detections now happen in seconds. Sensors identify anomalous behavior in real time. Identity platforms continuously evaluate trust. SIEM and OT security tools generate rich, contextual alerts instantly. Yet in most environments, access enforcement is still manual. A detection triggers a ticket. A human reviews. A decision is made. Minutes—or hours—pass before action is taken.

Endpoint security encompasses the processes and technologies used to protect end-user devices—including laptops, servers, mobile devices, IoT systems, and any connected asset with access to corporate resources. As organizations become more distributed and adversaries become more sophisticated, the endpoint has evolved into both a preferred target for threat actors and a pivotal control point within a modern security architecture.

The Family Educational Rights and Privacy Act (FERPA) has governed how universities handle student records since 1974. Fundamentally, FERPA is a federal privacy law that grants students the ability to exert some meaningful authority over their academic information. At the same time, it also assigns responsibility for the maintenance and safeguarding of student education records to the universities that maintain them.

You’ve been securing Kubernetes workloads for years. Your CSPM is running, your CNAPP is configured, your team knows how to triage container alerts. Then an AI agent lands in your cluster — maybe from the data science team, maybe from a vendor integration, maybe from a tool you didn’t even know was running. Within a week, it’s making API calls nobody planned, accessing data stores that aren’t in the architecture diagram, and executing code it generated itself.

You’re forty-five minutes into a vendor demo for AI workload security. The dashboard looks polished—posture scores, misconfiguration findings, vulnerability counts, all tagged with an “AI workload” label that wasn’t there last quarter. You ask the obvious question: “Show me how this detects a prompt injection attack on our production agent.” Long pause. The SE pulls up a generic process anomaly rule.

It’s 2:47 AM and your SOC dashboard lights up. Six alerts fire across three hours from a single Kubernetes cluster: an outbound HTTP fetch to an unfamiliar domain, a tool invocation inside a customer support agent, an API call to an internal service the agent has never contacted, a service account token read, a file write to a model artifact directory, and an outbound data transfer that looks like normal API usage.

When your SOC alerts on “suspicious AI activity” in a production trading system, your response team faces a question that didn’t exist two years ago: can you explain to regulators exactly which function processed the malicious prompt, which internal tool it called, and how customer data ended up leaving your environment?

Modern software development relies on open-source components to accelerate innovation. This efficiency, however, introduces significant risk. Your application’s security is now tied to a vast and complex supply chain of code you did not write. The persistent software supply chain challenge is that this external code is a primary source of critical vulnerabilities and a hard.