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GUEST ESSAY: 5 security steps all companies should adopt from the Intelligence Community

The Last Watchdog

And, in doing so, the IC has developed an effective set of data handling and cybersecurity best practices. Businesses at large would do well to model their data collection and security processes after what the IC refers to as the “intelligence cycle.” Related video: Using the NIST framework as a starting point.

Security 149
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China: Navigating China episode 16: New data lifecycle guidelines for financial institutions in China – detailed assessments, additional security measures and some data localisation introduced

DLA Piper Privacy Matters

This introduces a data lifecycle security framework, and represents the key guideline for handling personal and other financial information by financial institutions (i.e. similar to the PIS Specification, but focused on the banking and financial services industry). Level 1: public data.

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China’s PIPL has finally arrived, and brings helpful clarification (rather than substantial change) to China’s data privacy framework

DLA Piper Privacy Matters

This aligns with other recent guidance putting clearer parameters around use of biometric data in China). Purposes/Restrictions on Use Collection and processing of data must be directly related to the purpose of processing specified in the privacy notice. Excessive data collection must be avoided.

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California Consumer Privacy Act: The Challenge Ahead – The Interplay Between the CCPA and Financial Institutions

HL Chronicle of Data Protection

In such situations, a financial institution must comply with a wide array of CCPA obligations, including requirements to make certain disclosures to consumers and to provide certain rights to consumers, such as the right to stop “sales” of their personal information and the right to access data that a business has collected about them.

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Conversational AI use cases for enterprises

IBM Big Data Hub

Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. ML and DL lie at the core of predictive analytics, enabling models to learn from data, identify patterns and make predictions about future events.

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The most valuable AI use cases for business

IBM Big Data Hub

Gear up robotics AI is not just about asking for a haiku written by a cat. Smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress. Robots handle and move physical objects.

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Tuesday’s Relativity Fest 2019 Sessions: eDiscovery Trends

eDiscovery Daily

In the increasingly non-linear and fast-moving legal technology world in which we now live and work, where cloud-based solutions, analytics, machine learning, and AI are impacting the data-driven decisions being made, is it time to rethink the EDRM? 11:10 AM – 12:10 PM: Data Subject Access Requests in the Americas and Beyond.