Remove tag privacy-rights
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Data privacy examples

IBM Big Data Hub

These are just some examples of how organizations support data privacy , the principle that people should have control of their personal data, including who can see it, who can collect it, and how it can be used. One cannot overstate the importance of data privacy for businesses today.

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Information Security and Compliance Through the Prism of Healthcare and Retail

AIIM

Organizations must focus strategically on how to manage digital content and understand that: 1) end-users are consuming technology differently; 2) consumer devices are being increasingly used as “on-ramps” to digital workflows; and 3) how you secure the scan and capture process becomes increasingly important. Want to find out more?

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The DDR Advantage: Real-Time Data Defense

Security Affairs

Status-quo cybersecurity works by securing the “boxes” in which our data resides. After all, you can’t secure what you can’t see, and today’s environments obfuscate the true location of data so well that we, as security practitioners, can hardly keep up with it. The second benefit is what we’ll be focusing on today.

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How Data Governance Protects Sensitive Data

erwin

How can companies protect their enterprise data assets, while also ensuring their availability to stewards and consumers while minimizing costs and meeting data privacy requirements? Data Security Starts with Data Governance. Lack of a solid data governance foundation increases the risk of data-security incidents.

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Guest Post -- GDPR Compliance starts with Data Discovery

AIIM

This is the seventh post in a series on privacy by Andrew Pery. You might also be interested in: Mitigate Data Privacy and Security Risks with Machine Learning. The Privacy and Security Dichotomy. Privacy by Design: The Intersection of Law and Technology. What Do the GDPR and new Privacy Laws Mean for U.S.

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How Machine Learning Can Accelerate and Improve the Accuracy of Sensitive Data Classification

Thales Cloud Protection & Licensing

Traditional approaches to data classification use manual tagging which is labor-intensive, error-prone, and not easily scalable. ML builds on different types of models for different purposes, for example categorization to determine a document category or Named Entity Recognition (NER) to identify sensitive data across diverse locations.

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What is metadata management and why is it important?

Collibra

Business metadata such as glossary terms are often not formally documented and available only as tribal knowledge. Data enrichment through automatic discovery and user tagging and rating (for example, tagging personally identifiable information or PII). Support for security and privacy through rules and policy management.