How can you protect what you can’t see? Data proliferation is rampant, and unstructured data is rapidly growing, estimated to be 80% of a business’s data inventory. Unstructured data is routinely undermanaged and hard to control and track as users take sensitive files from controlled repositories, store them on laptops, mobile devices, and cloud services, and share them in collaboration applications. This means more information, and more sensitive information, exists everywhere.
How do you find it? How do you track its movement? How do you protect it?
You need to know where your data is, who is using it, and how it changes throughout its lifecycle. You need to have better visibility into the use and movement of your sensitive data to meet privacy regulations and safeguard content. Data discovery and classification tools help identify and label sensitive files for downstream controls, but to maintain this control, you need deep visibility to track data as it travels, is accessed, and transforms into other file types.
Self-reporting files
Infosec and privacy teams struggle to track sensitive files since a file can be accessed by multiple systems, applications, and devices as users share it. This challenge grows as data visibility is limited when documents travel and change through duplication or revisions. Without proper data visibility, you are not aware that sensitive information is shared, moved, changed, or deleted.
The best approach to meet this challenge is to use a self-reporting file method, which automatically traces, and records all document interactions without relying on reviewing disparate network, application, and device logs. This same technology is the foundation of a file-centric approach to protection. This bridges both privacy and security gaps by delivering deep data visibility and a strong front-line defense for your sensitive data.
Self-reporting files use an embedded ID to trace and record all interactions. Data loss prevention (DLP) and identity and access management (IAM) solutions designed for perimeter security lose track of data migrated to the cloud and when downloaded by remote workers. Privacy and legal e-discovery applications may have file mapping features, but they are siloed, don’t track all interactions, and the multiple datasets are disconnected and incomplete.
A unique ID that’s embedded and travels with the file enables persistent tracing and self-reporting of interactions regardless of a file’s location. This eliminates working with patch-work logs from multiple systems to track files and provides efficient and timely incident and privacy response.
Your existing data visibility tools perform better with an embedded ID approach. Discovery scans lack the intelligence to relate file derivatives that are copied or duplicated. With an embedded ID, derivatives of an original file, whether duplicated or renamed, inherit the parent ID tag and all its security and governance policies.
An embedded ID reduces tool sprawl so you don’t need separate tracking applications for every security, privacy, and legal e-discovery system. All applications benefit from a single source of truth for file tracing and interactions. Using an embedded file ID is the best way to achieve sustainable and auditable processes and better safeguard sensitive data.
Control at 3rd Parties
You lose data visibility when you share files outside the corporate network with supply-chain partners, and external legal and financial professionals. Regulators make you responsible for ensuring data is appropriately safeguarded. Breaches of your data while in custody of a third party require you to report the breach.
An embedded ID provides the same activity tracking as if the files were internal. You can enable additional controls to set a file expiration date and revoke access at any time to third-party locations. Adding granular permission controls ensures only users that you authorize can access sensitive content.
User Behavior Monitoring
Tracking who is accessing your data, how it is used, and where it is moved are critical inputs for monitoring solutions focusing on detecting data misuse and policy violations. Data transfers to removable drives and large uploads to cloud services may be an early warning sign of malicious insider threat intent. User behavior analytics are most effective when data visibility tools provide a full perspective of user activities across all applications and storage locations.
An embedded ID provides the highest granularity of data activity leading to earlier detection of insider threats. These data insights cue security methods, such as restricting the copy of data to removable drives and other locations.
Privacy and Security Together
Utilizing an embedded file ID for constant data visibility is the optimal solution for addressing privacy and security requirements. This approach adopts a "protect-first" security strategy, safeguarding data through encryption and access controls while eliminating the need for redundant tools across multiple networks and endpoints. It further ensures data privacy by restricting access to authorized users and maintaining real-time awareness of the location of sensitive files.
