Connect with us


AWS rolls out new security feature to prevent accidental S3 data leaks



Image: AWS

Amazon’s Web Services division has rolled out new security features to AWS account owners today that are meant to prevent accidental data exposures caused by the misconfiguration of S3 data storage buckets.

Starting today, AWS account owners will have access to four new options inside their S3 dashboards under the “Public access settings for this account” section.

These four new options allow the account owner to set a default access setting for all of an account’s S3 buckets. These new account-level settings will override any existing or newly created bucket-level ACLs (access control lists) and policies.

Account owners will have the ability to apply these new settings for S3 buckets that will be created from now onwards, to apply the new setting retroactively, or both.

Jeff Barr, Chief Evangelist for Amazon Web Services, said the new settings are meant to work as a master switch that prevents account owners or their employees/developers from accidentally opening S3 buckets and their data to the public by coding or misconfiguration errors at the app/bucket level.

These types of accidents (of misconfiguring S3 buckets) have been a major problem for AWS customers for the past few years, and a serious black eye for AWS itself. Many cyber-security experts have considered that Amazon did not do enough to warn AWS users about the dangers of exposing an S3 bucket or providing controls to prevent this from happening.

Amazon did act, in November last year, when it began displaying bright orange warnings in the AWS dashboard, next to each S3 bucket that allowed public access.


Image: AWS

Today’s updates come to address most of the criticism that the company has faced recently, and this update will provide the much-needed settings to prevent misconfiguration from exposing buckets, and not just tell account owners after they’ve already happened.

Just to put things in perspective and show how problematic the issue of accidental S3 bucket exposures has been, below is a (very incomplete) list of data breaches and data leaks that have been caused by a company or app that ran a misconfigured S3 bucket that allowed anyone to view its content and not just the server owner.

  • An unsecured S3 server exposed thousands of FedEx customer records
  • An AWS S3 error exposed GoDaddy business secrets
  • Accenture left a huge trove of highly sensitive data, including “keys to the kingdom,” on exposed servers
  • Customer records for at least 14 million Verizon subscribers, including phone numbers and account PINs, were exposed via an S3 bucket
  • A Verizon AWS S3 bucket containing over 100 MB of data about the company’s internal billing system was also found exposed online
  • An S3 database left exposed leaked the personal details of job applications that had Top Secret government clearance
  • Another S3 server exposed the details of 198 million American voters
  • National Credit Federation leaked US citizen data through unsecured AWS bucket
  • Nigerian airline Arik Air also leaked customer data via an exposed S3 bucket
  • Pocket iNet ISP exposed 73GB of data including secret keys, plain text passwords
  • An S3 leak at Alteryx left 123 million American households exposed to fraud and spam
  • AgentRun, an insurance startup, also leaked sensitive customer health data via amisconfigured Amazon S3 bucket
  • Donald Trump’s campaign website also leaked intern resumes via an S3 bucket
  • Spyware firm SpyFone also left customer data, recordings exposed online via an S3 server
  • Booz Allen Hamilton, a top DOD contractor, leaked 60,000 files, including employee security credentials and passwords to a US government system
  • An AWS S3 server leaked the personal details of over three million WWE fans who registered on the company’s sites
  • An auto-tracking company leaked over a half of a million car and car owner details.
  • Voting machine firm Election Systems & Software (ES&S) left an S3 bucket exposed online that contained the personal records of 1.8 million Chicago voters
  • Dow Jones leaked the personal details of 2.2 million customers
  • An S3 bucket leaked data of thousands of Australian government and bank employees
  • Password manager Keeper also exposed an S3 server

According to research published last year, Skyhigh Networks (now part of McAfee) found that around seven percent of all AWS S3 buckets were publicly exposed.

In addition to the new AWS S3 public access settings, Amazon also announced major news for DynamoDB, a high-load database engine, also part of the AWS suite. Starting today, Amazon said all data stored inside DynamoDBs will be encrypted by default.

“You do not have to make any code or application modifications to encrypt your data,” Amazon said in a press release. “DynamoDB handles the encryption and decryption of your data transparently and continues to deliver the same single-digit millisecond latency that you have come to expect.”

Related coverage:

Source link

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *


Cloud Data Security



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.

Continue Reading


GigaOm Radar for Data Loss Prevention



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.

Continue Reading


Key Criteria for Evaluating Data Loss Prevention Platforms



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.

Continue Reading