Big Data. Big Decisions
InformationWeek
Special Coverage Series


Diebold Virtualizes ATMs To Secure Banking Data

Diebold seeks to close vulnerabilities by moving customer data off physical machines onto virtualized ATMs on protected data center servers.

Automatic teller machine maker Diebold has taken a novel approach to protecting bank customer data: virtualization. Virtualized ATMs store all customer data on central servers, rather than the ATM itself, making it difficult for criminals to steal data from the machines.

In places including Brazil, customer data has been at risk when thieves pulled or dynamited ATMs out of their settings and drove off with them. With threats increasing worldwide at many retail points of sale, such as supermarket checkout counters and service station gas pumps, Diebold needed to guarantee the security of customer data entered at the 50,000 ATMs that it manages.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

"From the entrance to Death Valley in California to a Mount Everest base camp, we must maintain them all," said Mark Kropf, head of Deibold's emerging technologies group, in an interview.

[ Want to learn more about how hackers can attack ATM machines? See ATM Hack Demo Planned for Black Hat. ]

Diebold produces Opteva ATM machines and supplies Agilis software to run on its own and other manufacturers' ATMs. Diebold ATMs have been among those targeted by hackers. Westfield Bank, Palmetto Bank, and BellCo Credit Union are among the U.S. institutions that use Diebold ATMs, ATM management, or ATM security services. In addition, Diebold supplies BancoStato in Switzerland and Banco Santander in Brazil.

Diebold last year partnered with VMware to produce a zero-client ATM. An inaccessible zero-client component causes the ATM screen to render the results of interactions with a virtual machine running on a central server in a bank or Diebold data center. No customer data is captured and stored on the ATM itself, and all data storage devices have been removed. The zero client can relay customer entries to central servers and has sufficient smarts to display the text, graphics, and video that a financial institution wishes to send to customers.

Diebold is currently trying to maintain security at each physical ATM, a user endpoint that is running either Microsoft's Windows XP or IBM's OS/2 operating system. These operating systems, although hardened, provided "a larger attack surface" for hackers than the zero client, which has no operating system and no data storage, Kropf said. He said he knew of no other ATM system running virtual machines in place of local device software.

Hackers--in some cases, company insiders--have put card readers on gas station service pumps and customer checkout machines in supermarkets, storing customer data in an encrypted file that can be downloaded by the hackers. Virtualization combats such attempts by making such a reading device an instantly identifiable interloper, since the endpoint device no longer needs any memory-equipped accessory.

"Virtualization will fundamentally change the way Diebold--and its customers--deploy solutions to the marketplace," said Frank Natoli, VP and CTO at Diebold, as the firm announced its virtualized ATM prototype on the eve of VMworld in September. "This technology is a game changer for our industry," he said at the time.

By virtualizing ATM applications, Diebold could move data security and customer privacy protection into a Diebold or bank data center. Large banks use their own data centers to run their ATM networks. "Second- and third-tier banks" use Diebold's data centers and ATM network, Kropf said.

Kropf noted that the virtual machine approach will also allow financial institutions to change customer applications more readily on central servers, then have the changes deployed automatically through the ATM network.

Even so, the primary motivation for going with virtualized ATMs was to prevent the theft of customer data. Likewise, the security features of end-user virtual machines may eventually become the compelling argument for virtualizing end-user desktops. Data stored on central servers with in-depth protections has tended to be more secure than data stored on end-user devices.

When the end-user device--whether a desktop, laptop, or tablet--is virtualized, a thief can still be prevented from accessing user data. Once the device is reported stolen, it can be wiped clean of any data that might still be resident on it through commands from the data center. And central servers hosting virtual machines can be staging grounds for establishing defenses at multiple logical boundaries, such as rechecking a user's credentials at each step of a process. For example, is this user authorized to access this particular data set?

ATMs, fortunately, were not among InformationWeek's Six Worst Data Breaches In 2011. Diebold is looking for a partner in 2012 with whom to deploy virtualized ATMs. It believes it's a step toward keeping the technology off 2012's top breaches list.

In today's uncertain and highly scrutinized financial services industry, achieving effective risk management is vital for survival. The report examines the need for enterprise risk management, the benefits of holistic data management, and ERM best practices. Download the report now. (Free registration required.)



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.