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How A Data Catalog Enhances Data Risk Management

Alation

Alation joined with Ortecha , a data management consultancy, to publish a white paper providing insights and guidance to stakeholders and decision-makers charged with implementing or modernising data risk management functions. The Increasing Focus On Data Risk Management. Download the complete white paper now.

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Maximize your data dividends with active metadata

IBM Big Data Hub

Metadata management performs a critical role within the modern data management stack. However, as data volumes continue to grow, manual approaches to metadata management are sub-optimal and can result in missed opportunities. This puts into perspective the role of active metadata management.

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What Cyber Criminals Can Do With Your Metadata

Smart Data Collective

When that happens, tens of thousands of people are put at risk for identity theft when their metadata is stolen. What is metadata and how is it used? What Metadata Contains. Metadata is basically a trail of data that is spread out across a network. Why a Cyber-Criminal Steals Metadata.

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Practical Points from the DGPO: An Introduction to Information Risk Management

TDAN

Individuals are starting to pay attention to organizational vulnerabilities that compound risks associated with managing, protecting, and enabling access […]. There is an ever-increasing awareness of concerns about data privacy, corporate data breaches, increasing demands for regulatory compliance.

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Best Practices for Data Catalog Implementation

Octopai

Catalogs also allow for better Risk Management; data catalogs help businesses maintain regulatory compliance by providing a clear record of what data is stored and how it’s used. Automate Metadata Capture Leverage metadata management tools like Octopai to automate the process of capturing metadata from various sources.

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Operationalizing responsible AI principles for defense

IBM Big Data Hub

Providing interpretable AI model metadata (for example, as factsheets ) specifying accountable persons, performance benchmarks (compared to human), data and methods used, audit records (date and by whom), and audit purpose and results. Capture key metadata to render AI models transparent and keep track of model inventory.

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AI Governance: Break open the black box

IBM Big Data Hub

This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. Everything needed to develop a consistent transparent model management process is included in IBM AI Governance. Risk Management: Manage risk and compliance to business standards, through automated facts and workflow management.

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