Small and midsize businesses face ever-increasing regulatory pressure, data privacy requirements and cyber risk. They’re held to the same standards as larger companies and are expected to maintain up-to-date data inventories in accordance with data privacy and security regulations. Many businesses, especially utilities and municipalities, rely on manual data classification techniques like folder structures and meta data tagging. But those methods are prone to error, can’t scale and lack diligence, making the process next to impossible to audit and difficult to enforce.
On the other hand, larger companies have embraced DLP technology to provide classification capabilities and visibility to data leaving the network. However, the inline data suppression capabilities they offer are usually based on whitelisting and blacklisting, and push notifications after the data has left the network, rather than while it’s in motion and recoverable.
Risks of Limited and Manual Data Discovery & Classification Capabilities:
Increased Operational Risks