There's no shortage of data that can be classified. To help you build an effective data classification policy, we look at 50 different examples of data classification.

When it comes to classification, not all data needs to be treated the same way. There are five common categories that organizations can follow.

Discover common Microsoft 365 security risks — from misconfigurations to IAM weaknesses — and learn how to protect sensitive data and reduce breach exposure.

Human error now plays a role in 95 percent of data breaches, making it more critical than ever for security teams to treat it as the substantial threat it is. In some cases, employees intentionally expose sensitive data for personal gain. But the most pervasive insider threats aren’t malicious — they’re accidental. This challenges the notion that an organization can become invulnerable if only it...

Protecting data in the context of zero trust means moving beyond perimeter-based defenses to a model where no user, device, or application is inherently trustworthy. As a result, every access request is verified, continuously monitored, and limited to the minimum necessary permissions needed to perform a job function. This approach reduces the risk of insider threats, compromised credentials, and...

Data Leak Prevention encompasses strategies and tools designed to safeguard sensitive data from unauthorized access, use, or disclosure.

Digital forensics identifies, collects, and analyzes digital evidence to trace cyber breaches, assess data loss, and enhance future security.

Data loss is the unintentional deletion, corruption, or inaccessibility of critical data due to various causes preventing user access.

Data ingress is incoming data to a system, while data egress is outgoing data. Both require secure and efficient management.

Sensitive data refers to any information that could harm or have adverse consequences when accessed without authorization.

Fortra's Q2 2025 data protection releases represent a significant step forward in making enterprise data protection both more powerful and more accessible.

Discover how AI transforms data security with real-time threat detection, predictive analytics, and automation to boost protection from cyber attacks.

Integrated Risk Management (IRM) unifies siloed risk practices into a cohesive, structured framework, enabling strategic and effective risk oversight.

AI data governance refers to the management and control of data used in AI systems in an organization.

A DLP policy is an organization's set of guidelines and procedures for protecting, monitoring, and managing its sensitive data.

End User Experience Monitoring (EUEM) measures and analyzes user interactions with a company's software, website, application, or service.