Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most law firms have moved email, documents, and collaboration to Microsoft 365. And most assume Microsoft is backing up that data. They’re wrong. According to Microsoft’s own Services Agreement, “We recommend that you regularly backup Your Content and Data that you store on the Services or store using Third-Party Apps and Services.” Microsoft provides infrastructure redundancy—if their data center has a problem, your data is replicated elsewhere.

For many organizations, ServiceNow operates as the system of record for governance, auditability, and compliance. But when incidents occur, engineers often need to consult external tools to identify and resolve the root cause. When investigations are siloed from the system of record, engineers must return to ServiceNow to manually update work notes, incident statuses, and mandatory resolution fields before closing tickets.

Proactively addressing risks in technical environments is a constant challenge. Many teams wait until it’s too late and key application functionality is disrupted or sensitive data is exposed. However, understanding risk severity in context can be difficult, especially in distributed systems where related issues and impacts may not be immediately obvious.

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.