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Bring light to the black box

IBM Big Data Hub

Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. Building responsible AI requires upfront planning, and automated tools and processes designed to drive fair, accurate, transparent and explainable results.

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AI Governance: Break open the black box

IBM Big Data Hub

This is due to: An inability to access the right data. Multiple unsupported tools for building and deploying models. Well-planned and executed AI requires reliable data backed by transparent, automated tools and explainable processes. Many organizations struggle when adopting AI. Challenges around managing risk.

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Duo Security created open tools and techniques to identify large Twitter botnet

Security Affairs

Researchers at security firm Duo Security have created a set of open source tools and disclosed techniques that could be used to identify large Twitter botnet. Security experts from Duo Security have developed a collection of open source tools and disclosed techniques that can be useful in identifying large Twitter botnet.

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How to enable trustworthy AI with the right data fabric solution

IBM Big Data Hub

Automated, integrated data science tools help build, deploy, and monitor AI models. Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards. It’s not just about granting proper access to data science teams.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. They are used in everything from robotics to tools that reason and interact with humans. Capture and document model metadata for report generation. Track models and drive transparent processes.

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Data Governance for Smart Data Distancing

erwin

On a business level, decisions based on bad external data may have the potential to cause business failures. In business, data is the food that feeds the body or enterprise. Better data makes the body stronger and provides a foundation for the use of analytics and data science tools to reduce errors in decision-making.

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Four use cases defining the new wave of data management

IBM Big Data Hub

As discussed in the previous section data virtualization and data cataloging help get the right data to the right people by making it easier to find the data that best fits their needs and access it. Automated metadata generation is essential in order to turn a manual process into one that is better controlled.