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Microsoft Bing is back online in China

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Microsoft’s Bing could not be accessed in China on Thursday due to an accidental technical error rather than for censorship reasons, according to Bloomberg News sources.

Access to Microsoft’s Bing search engine has since been restored in China, a Microsoft spokesperson confirmed to ZDNet. The company did not provide further explanation for why the outage occurred.

Microsoft president Brad Smith told Fox Business on Thursday that its search engine was down. 

“People in China cannot access Bing, this is not the first time it’s happened. It happens periodically … we’re still waiting to find what this situation is about,” he said.

Smith also acknowledged that Microsoft had fewer legal rights in China than in other countries.

“There are certain principles that we think it’s important to stand up for,” he said. “And we’ll go at times into the negotiating room and the negotiations are sometimes pretty darn direct.”

With Bing, Microsoft has tried to play by China’s censorship rules. For example, the search engine filtered out both English and Chinese language search results of politically-sensitive terms such as “Dalai Lama” and “Tiananmen”, according to China-based freedom of speech advocacy blog GreatFire.org.

The temporary block of Microsoft’s Bing comes at a time when tensions between the US and China are running high, with the introduction of a bipartisan Bill in the US earlier this month to ban the sale of tech to Chinese companies Huawei and ZTE, and the US stating on Wednesday it’s intention to extradite Huawei CFO Meng Wanzhou.

Both the US and China have already levied tit-fot-tat tariffs on $34 billion worth of goods, and Trump made good on his pledge to escalate the trade war, directing Lighthizer to find another $200 billion worth of products to hit.

Google, Bing’s competitor, withdrew from China in 2010 in opposition to its censorship rules after revealing it had been hacked by the government. It has since moved from its policy of opposing censorship, having made plans for a censored version of its search engine — code-named Dragonfly — for China.

Following the project being made public, 1,000 Google employees signed a letter in August that called for the search company to abandon its efforts to create the censored Chinese search engine. Another open letter protesting against Dragonfly was sent to Google in November, signed by almost 300 of its employees.

“Providing the Chinese government with ready access to user data, as required by Chinese law, would make Google complicit in oppression and human rights abuses,” the November letter created by Google Employees Against Dragonfly states.

The internet is heavily censored in China. In 2017, China shut down over 128,000 so-called harmful websites, at the time, saying that it was part of efforts to maintain “social stability”, taking on “vulgar” and pornographic content as well as the unauthorised dissemination of news.

Related Coverage

US intends to formally extradite Huawei CFO from Canada

The US has informed the Canadian government that it will file a formal request to extradite Huawei CFO Meng Wanzhou on allegations of violating US sanctions.

China denies massive layoffs among internet firms

Since the fourth quarter of 2018, many Chinese reports have suggested that major internet firms in the country are either scaling back or freezing hiring, or axing staff due to lukewarm growth and unfavourable prospects.

China tech spending to hit $273B amidst US trade war, slowing economy

Chinese businesses are projected to spend US$256.61 billion on tech this year and another US$272.84 billion in 2020, focusing their investments on transforming operations and improving efficiencies as they brace themselves for an uncertain geopolitical climate, says Forrester.

Bipartisan Bill introduced to ban sale of US tech to Huawei and ZTE

US lawmakers introduce bipartisan Bill that, if passed, would ban the export of US chips and other components to the two Chinese tech companies.

Huawei looks up to Apple in terms of privacy: Founder Ren Zhengfei

History will judge whether Huawei adhered to its claims to not harm the interests of customers, its founder has said.

China vs. the US: How governments and markets influence tech innovations (TechRepublic)

Quantitative futurist Amy Webb discusses her experiences watching the rise of smartphones while living in China, and how the East’s approach to technology runs parallel to that of the US.

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Cloud Data Security

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Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.

This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:

  • Dynamic versus static
  • Scalability
  • Evolvability

In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.

This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.

Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.

The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.

Figure 1. Difference in Cumulative Policy Changes

The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.

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GigaOm Radar for Data Loss Prevention

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Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.

With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.

This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.

Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.

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Key Criteria for Evaluating Data Loss Prevention Platforms

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Data is a crucial asset for modern businesses and has to be protected in the same way as any other corporate asset, with diligence and care. Loss of data can have catastrophic effects, from reputational damage to significant fines for breaking increasingly stringent regulations.

While the risk of data loss is not new, the landscape we operate in is evolving rapidly. Data can leave data centers in many ways, whether accidental or malicious. The routes for exfiltration also continue to grow, ranging from email, USB sticks, and laptops to ever-more-widely-adopted cloud applications, collaboration tools, and mobile devices. This is driving a resurgence in the enterprise’s need to ensure that no data leaves the organization without appropriate checks and balances in place.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. Policy, people, and technology are critical components in a data loss prevention (DLP) strategy.

As with any information security strategy, technology plays a significant role. DLP technology has traditionally played a part in helping organizations to mitigate some of the risks of uncontrolled data exfiltration. However, both the technology and threat landscape have shifted significantly, which has led to a reevaluation of DLP tools and strategy.

The modern approach to the challenge needs to be holistic and intelligent, capable of applying context to data usage by building a broader understanding of what the data is, who is using it, and why. Systems in place must also be able to learn when user activity should be classified as unusual so they can better interpret signs of a potential breach.

This advanced approach is also driving new ways of defining the discipline of data loss prevention. Dealing with these risks cannot be viewed in isolation; rather, it must be part of a wider insider risk-management strategy.

Stopping the loss of data, accidental or otherwise, is no small task. This GigaOM Key Criteria Report details DLP solutions and identifies key criteria and evaluation metrics for selecting such a solution. The corresponding GigOm Radar Report identifies vendors and products in this sector that excel. Together, these reports will give decision-makers an overview of the market to help them evaluate existing platforms and decide where to invest.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:

Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.

GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.

Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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