In today's cloud-first world, data is the engine of innovation—but it's also a liability if mishandled. In particular, personally identifiable information (PII) sits at the heart of this challenge, ...

Rich Vibert is the CEO and cofounder of Metomic, a next-generation data security solution for SaaS and GenAI tools. Data is everywhere—and not just big data, your personal data too. Protecting ...

Personally identifiable information (PII) is any data that could potentially identify a specific individual. Any information that can be used to distinguish one person from another and can be used to ...

In the age of rapid technological advancement comes an ever-increasing cyber threat landscape. With this comes a heightened emphasis on data privacy. Today, many organizations are now at a critical ...

In the complicated web that is data privacy law, there are a lot of acronyms. There are acronyms for everything from laws and regulations to types of data, roles, frameworks, and more. Unpacking the ...

A substantial 55% of recent Data Loss Prevention (DLP) events have involved attempts to input personally identifiable information (PII), while 40% included confidential documents. The figures come ...

Sprawling. That’s one way to describe the nature of data and responsibilities in modern organizations. There’s a lot of personally identifiable information (PII) and sensitive data to track, and ...

SAN FRANCISCO--(BUSINESS WIRE)--Dialpad, Inc. – the industry-leading Ai-Powered Customer Intelligence Platform – today announced the release of PII Redaction, an Ai-powered feature designed to fortify ...

Dialpad released PII Redaction, an Ai-powered feature designed to fortify privacy safeguards of personal identifiable information (PII) and empower users with greater control over their data. As part ...

As AI moves from controlled pilots into real business processes, the question of responsibility becomes harder to ignore. AI systems now touch sensitive data, customer interactions, internal ...

The primary strategy for mitigating sensitive data risk is data minimization: evaluating what is strictly necessary and reducing the volume, identifiability, and retention period of project data.