Hackers Dump 20GB of Intel's Confidential Data Online

Chipmaker investigates a leak of intellectual property from its partner and customer resource center.

More than 20 gigabytes of proprietary data and source code from chipmaker Intel Corp. was dumped online by a third party, likely the result of a data breach from earlier this year.

The announcement of the “first 20gb release in a series of large Intel leaks” was made by user and IT consultant Tillie 1312 Kottmann #BLM on Twitter, who called the information “Intel exconfidential Lake Platform Release.”

“Most of the things here have NOT been published ANYWHERE before and are classified as confidential, under NDA or Intel Restricted Secret,” according to the tweet.

Intel later confirmed the leak of the data—which was publicly available on BitTorrent feeds yesterday–in a published report on Ars Technica.

The data appears to be from the Intel Resource and Design Center, which hosts information for registered users who are typically Intel customers and partners, a spokeswoman said in the report. The information is provided to these users via the center under NDA.

Intel does not believe its network was breached, but rather that “an individual with access downloaded and shared this data,” she said. There also is a chance the information leaked is not current, something the company is currently trying to determine, the spokeswoman added.

It’s a very common practice for tech companies to share confidential information about forthcoming technology and product releases with their customers and partners before the information is publicly available.

Even with trusted relationships and NDAs in place, organizations still run the risk that this intellectual property (IP) will make it into the public forum before the company itself is prepared to publicize it, which is “often an unavoidable part of doing business,” said Erich Kron, a security awareness advocate at security firm KnowBe4.

“While this appears to be an issue related to a third party, it does underline the security concerns around intellectual property when working with business partners both up and down the supply chain,” he said in an email to Threatpost.

Indeed, while data breaches often are considered in the context of jeopardizing the privacy of clients or customers and the potential use of that data for financial gain by threat actors, a company’s IP can be just as valuable, and the results of it falling into the wrong hands just as damaging, Kron noted.

“This intellectual property can be very valuable to potential competitors, and even nation states, who often hope to capitalize on the research and development done by others,” he said.

Intel continues to investigate the incident, which is ongoing, as the attacker claims to have more data to release from the leak. This could actually help Intel “narrow down the source of the breach,” Chris Clements, vice president of Solutions Architecture at security firm Cerberus Sentinel, said in an email to Threatpost.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data