You Cannot Protect What You Cannot See
When engineering teams ship fast, data governance often takes a back seat. Developers spin up temporary databases, marketers export CSVs of customer data, and customer support agents save localized copies of KYC documents.
Implementing an agentless or agent-based scanning strategy allows you to use regex and machine learning to flag sensitive PII before an audit catches it.
The Hidden Costs of Shadow Data
Shadow data — information that exists outside of your formal data governance framework — is one of the most significant risks enterprises face today. A recent study found that over 60% of organizations have sensitive data in locations they are not aware of.
Agentless vs. Agent-Based Scanning
There are two primary approaches to automated data discovery:
- Agentless scanning connects to data stores remotely, requiring no software installation on target machines. This is ideal for cloud databases, SaaS applications, and managed services.
- Agent-based scanning deploys lightweight software on endpoints to scan local file systems. This catches data stored on laptops, desktops, and on-premise servers.
The best strategy uses both approaches in combination, giving you comprehensive visibility across your entire data landscape.
Building a Data Inventory
Once discovery is complete, the next step is building a living data inventory — a continuously updated map of what data you hold, where it lives, who has access, and what purpose it serves. This inventory becomes the foundation for every other privacy compliance activity.