Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Top tips is a weekly column where we highlight what’s trending in the tech world and share ways to stay ahead. This week, we’re taking a moment to think about something that often gets overlooked. When the world feels unpredictable, our routines change. We rely more on our devices to stay connected, informed, and reassured.

Protecting sensitive data is essential in today’s digital world, where personal information is stored across multiple devices and online accounts. From financial details to login credentials, even small pieces of data can be used by cybercriminals if they fall into the wrong hands. The good news is that you can protect sensitive data with simple, practical steps.

Microsoft Purview is a powerful platform, but power without expertise can lead to underutilization, misconfiguration, and missed opportunities. Across industries, organizations are grappling with a common set of challenges: The stakes are high. A single compliance incident can cost organizations between $100,000 and $5 million in fines and penalties. And that figure doesn't account for the reputational damage, operational disruption, and remediation costs that follow.

Sensitive data discovery tools vary widely in hybrid coverage, identity context, and time-to-value. Most platforms handle cloud or on-premises infrastructure well, but rarely both. The strongest options connect discovery to identity and permissions, turning a file inventory into actionable risk intelligence. For Microsoft-heavy hybrid teams, that integration determines whether discovery produces reports or drives remediation.

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.

Every new AI feature that ships into a platform your employees already use is a security question your stack probably can't answer yet. It sounds like hyperbole, but it's the structural reality of how AI adoption works in 2026. A recent update to WhatsApp is a useful illustration of why.

It’s getting harder than ever for businesses to keep their data safe. Between remote work, cloud services, and third-party integrations, sensitive information now moves faster and across more touchpoints than traditional security models were built to handle. Luckily, there’s a new generation of data loss prevention tools on the market. Ones that boast advanced features and leverage AI to resolve data security incidents.

Organizations today face a common challenge: sensitive data is everywhere. It lives across collaboration platforms, endpoints, databases, SaaS applications, and cloud storage systems. Employees and partners need to access and share information quickly, often across teams, organizations, and even countries. At the same time, regulatory requirements, security mandates, and privacy obligations demand stronger protection for sensitive data.

Enterprise data environments have fundamentally outpaced the security architectures designed to protect them. Sensitive data now exists across endpoints, cloud infrastructure, SaaS platforms, and AI workflows simultaneously, often replicated in fragments that carry no labels and trigger no file-based controls.