Tag Archives: data security

Takeaways from the Gartner Security and Risk Management Summit (2014)

Last week I had the opportunity to attend the Gartner Security and Risk Management Summit. At this event, Gartner analysts and security industry experts meet to discuss the latest trends, advances, best practices and research in the space. At the event, I had the privilege of connecting with customers, peers and partners. I was also excited to learn about changes that are shaping the data security landscape.

Here are some of the things I learned at the event:

  • Security continues to be a top CIO priority in 2014. Security is well-aligned with other trends such as big data, IoT, mobile, cloud, and collaboration. According to Gartner, the top CIO priority area is BI/analytics. Given our growing appetite for all things data and our increasing ability to mine data to increase top-line growth, this top billing makes perfect sense. The challenge is to protect the data assets that drive value for the company and ensure appropriate privacy controls.
  • Mobile and data security are the top focus for 2014 spending in North America according to Gartner’s pre-conference survey. Cloud rounds out the list when considering worldwide spending results.
  • Rise of the DRO (Digital Risk Officer). Fortunately, those same market trends are leading to an evolution of the CISO role to a Digital Security Officer and, longer term, a Digital Risk Officer. The DRO role will include determination of the risks and security of digital connectivity. Digital/Information Security risk is increasingly being reported as a business impact to the board.
  • Information management and information security are blending. Gartner assumes that 40% of global enterprises will have aligned governance of the two programs by 2017. This is not surprising given the overlap of common objectives such as inventories, classification, usage policies, and accountability/protection.
  • Security methodology is moving from a reactive approach to compliance-driven and proactive (risk-based) methodologies. There is simply too much data and too many events for analysts to monitor. Organizations need to understand their assets and their criticality. Big data analytics and context-aware security is then needed to reduce the noise and false positive rates to a manageable level. According to Gartner analyst Avivah Litan, ”By 2018, of all breaches that are detected within an enterprise, 70% will be found because they used context-aware security, up from 10% today.”

I want to close by sharing the identified Top Digital Security Trends for 2014

  • Software-defined security
  • Big data security analytics
  • Intelligent/Context-aware security controls
  • Application isolation
  • Endpoint threat detection and response
  • Website protection
  • Adaptive access
  • Securing the Internet of Things
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Posted in Big Data, CIO, Data Governance, Data Privacy, Data Security, Governance, Risk and Compliance | Tagged , , , , , , , , | Leave a comment

Data Obfuscation and Data Value – Can They Coexist?

Data Obfuscation and Data Value

Data Obfuscation and Data Value

Data is growing exponentially. New technologies are at the root of the growth. With the advent of big data and machine data, enterprises have amassed amounts of data never before seen. Consider the example of Telecommunications companies. Telco has always collected large volumes of call data and customer data. However, the advent of 4G services, combined with the explosion of the mobile internet, has created data volume Telco has never seen before.

In response to the growth, organizations seek new ways to unlock the value of their data. Traditionally, data has been analyzed for a few key reasons. First, data was analyzed in order to identify ways to improve operational efficiency. Secondly, data was analyzed to identify opportunities to increase revenue.

As data expands, companies have found new uses for these growing data sets. Of late, organizations have started providing data to partners, who then sell the ‘intelligence’ they glean from within the data. Consider a coffee shop owner whose store doesn’t open until 8 AM. This owner would be interested in learning how many target customers (Perhaps people aged 25 to 45) walk past the closed shop between 6 AM and 8 AM. If this number is high enough, it may make sense to open the store earlier.

As much as organizations prioritize the value of data, customers prioritize the privacy of data. If an organization loses a customer’s data, it results in a several costs to the organization. These costs include:

  • Damage to the company’s reputation
  • A reduction of customer trust
  • Financial costs associated with the investigation of the loss
  • Possible governmental fines
  • Possible restitution costs

To guard against these risks, data that organizations provide to their partners must be obfuscated. This protects customer privacy. However, data that has been obfuscated is often of a lower value to the partner. For example, if the date of birth of those passing the coffee shop has been obfuscated, the store owner may not be able to determine if those passing by are potential customers. When data is obfuscated without consideration of the analysis that needs to be done, analysis results may not be correct.

There is away to provide data privacy for the customer while simultaneously monetizing enterprise data. To do so, organizations must allow trusted partners to define masking generalizations. With sufficient data masking governance, it is indeed possible for data obfuscation and data value to coexist.

Currently, there is a great deal of research around ensuring that obfuscated data is both protected and useful. Techniques and algorithms like ‘k-Anonymity’ and ‘l-Diversity’ ensure that sensitive data is safe and secure. However, these techniques have have not yet become mainstream. Once they do, the value of big data will be unlocked.

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Posted in Application ILM, B2B Data Exchange, Data masking, Data Privacy, Data Security, Telecommunications | Tagged , , , | Leave a comment

The Power and Security of Exponential Data

The Power and Security of Exponential Data

The Power and Security of Exponential Data

I recently heard a couple different analogies for data. The first is that data is the “new oil.” Data is a valuable resource that powers global business. Consequently, it is targeted for theft by hackers. The thinking is this: People are not after your servers, they’re after your data.

The other comparison is that data is like solar power. Like solar power, data is abundant. In addition, it’s getting cheaper and more efficient to harness. The juxtaposition of these images captures the current sentiment around data’s potential to improve our lives in many ways. For this to happen, however, corporations and data custodians must effectively balance the power of data with security and privacy concerns.

Many people have a preconception of security as an obstacle to productivity. Actually, good security practitioners understand that the purpose of security is to support the goals of the company by allowing the business to innovate and operate more quickly and effectively. Think back to the early days of online transactions; many people were not comfortable banking online or making web purchases for fear of fraud and theft. Similar fears slowed early adoption of mobile phone banking and purchasing applications. But security ecosystems evolved, concerns were addressed, and now Gartner estimates that worldwide mobile payment transaction values surpass $235B in 2013. An astute security executive once pointed out why cars have brakes: not to slow us down, but to allow us to drive faster, safely.

The pace of digital change and the current proliferation of data is not a simple linear function – it’s growing exponentially – and it’s not going to slow down. I believe this is generally a good thing. Our ability to harness data is how we will better understand our world. It’s how we will address challenges with critical resources such as energy and water. And it’s how we will innovate in research areas such as medicine and healthcare. And so, as a relatively new Informatica employee coming from a security background, I’m now at a crossroads of sorts. While Informatica’s goal of “Putting potential to work” resonates with my views and helps customers deliver on the promise of this data growth, I know we need to have proper controls in place. I’m proud to be part of a team building a new intelligent, context-aware approach to data security (Secure@SourceTM).

We recently announced Secure@SourceTM during InformaticaWorld 2014. One thing that impressed me was how quickly attendees (many of whom have little security background) understood how they could leverage data context to improve security controls, privacy, and data governance for their organizations. You can find a great introduction summary of Secure@SourceTM here.

I will be sharing more on Secure@SourceTM and data security in general, and would love to get your feedback. If you are an Informatica customer and would like to help shape the product direction, we are recruiting a select group of charter customers to drive and provide feedback for the first release. Customers who are interested in being a charter customer should register and send email to SecureCustomers@informatica.com.

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Posted in Big Data, Data Governance, Data Privacy, Data Security | Tagged , , , , , | Leave a comment

Data Security and Privacy: What’s Next?

DataSecurityData security breaches continue to escalate. Privacy legislation and enforcement is tightening and analysts have begun making dire predictions in regards to cyber security’s effectiveness. But there is more – Trusted insiders continue to be the major threat. In addition, most executives cannot identify the information they are trying to protect.

Data security is a senior management concern, not exclusive to IT. With this in mind, what is the next step CxOs must take to counter these breaches?

A new approach to Data Security

It is clear that a new approach is needed. This should focus on answering fundamental, but difficult and precise questions in regards to your data:

  1. What data should I be concerned about?
  2. Can I create re-usable rules for identifying and locating sensitive data in my organization?
  3. Can I do so both logically and physically?
  4. What is the source of the sensitive data and where is it consumed?
  5. What are the sensitive data relationships and proliferation?
  6. How is it protected? How should it be protected?
  7. How can I integrate data protection with my existing cyber security infrastructure?

The answers to these questions will help guide precise data security measures in order to protect the most valuable data. The answers need to be presented in an intuitive fashion, leveraging simple, yet revealing graphics and visualizations of your sensitive data risks and vulnerabilities.

At Informatica World 2014, Informatica will unveil its vision to help organizations address these concerns. This vision will assist in the development of precise security measures designed to counter the growing sophistication and frequency of cyber-attacks, and the ever present danger of rogue insiders.

Stay tuned, more to come from Informatica World 2014.

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Posted in Business/IT Collaboration, Data Privacy, Informatica World 2014 | Tagged , , , | Leave a comment

What types of data need protecting?

In my first article on the topic of citizens’ digital health and safety we looked at the states’ desire to keep their citizens healthy and safe and also at the various laws and regulations they have in place around data breaches and losses. The size and scale of the problem together with some ideas for effective risk mitigation are in this whitepaper.

The cost and frequency of data breaches continue to rise

The cost and frequency of data breaches continue to rise

Let’s now start delving a little deeper into the situation states are faced with. It’s pretty obvious that citizen data that enables an individual to be identified (PII) needs to be protected. We immediately think of the production data: data that is used in integrated eligibility systems; in health insurance exchanges; in data warehouses and so on. In some ways the production data is the least of our problems; our research shows that the average state has around 10 to 12 full copies of data for non-production (development, test, user acceptance and so on) purposes. This data tends to be much more vulnerable because it is widespread and used by a wide variety of people – often subcontractors or outsourcers, and often the content of the data is not well understood.

Obviously production systems need access to real production data (I’ll cover how best to protect that in the next issue), on the other hand non-production systems of every sort do not. Non-production systems most often need realistic, but not real data and realistic, but not real data volumes (except maybe for the performance/stress/throughput testing system). What need to be done? Well to start with, a three point risk remediation plan would be a good place to start.

1. Understand the non-production data using sophisticated data and schema profiling combined with NLP (Natural Language Processing) techniques help to identify previously unrealized PII that needs protecting.
2. Permanently mask the PII so that it is no longer the real data but is realistic enough for non-production uses and make sure that the same masking is applied to the attribute values wherever they appear in multiple tables/files.
3. Subset the data to reduce data volumes, this limits the size of the risk and also has positive effects on performance, run-times, backups etc.

Gartner has just published their 2013 magic quadrant for data masking this covers both what they call static (i.e. permanent or persistent masking) and dynamic (more on this in the next issue) masking. As usual the MQ gives a good overview of the issues behind the technology as well as a review of the position, strengths and weaknesses of the leading vendors.

It is (or at least should be) an imperative that from the top down state governments realize the importance and vulnerability of their citizens data and put in place a non-partisan plan to prevent any future breaches. As the reader might imagine, for any such plan to success needs a combination of cultural and organizational change (getting people to care) and putting the right technology – together these will greatly reduce the risk. In the next and final issue on this topic we will look at the vulnerabilities of production data, and what can be done to dramatically increase its privacy and security.

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Posted in Application ILM, Data Archiving, Data Governance, Data masking, Data Privacy, Public Sector | Tagged , , , , , | Leave a comment

No, Cloud Won’t Chase the Data Analytics Gremlins Away

Hosting Big Data applications in the cloud has compelling advantages. Scale doesn’t become as overwhelming an issue as it is within on-premise systems. IT will no longer feel compelled to throw more disks at burgeoning storage requirements, and performance becomes the contractual obligation of someone else outside the organization.

Cloud may help clear up some of the costlier and thornier problems of attempting to manage Big Data environments, but it also creates some new issues. As Ron Exler of Saugatuck Technology recently pointed out in a new report, cloud-based solutions “can be quickly configured to address some big data business needs, enabling outsourcing and potentially faster implementations.” However, he adds, employing the cloud also brings some risks as well.

Data security is one major risk area, and I could write many posts on this. But management issues also present other challenges. Too many organizations see cloud as an cure-all for their application and data management ills, but broken processes are never fixed when new technology is applied to them. There are also plenty of risks with the misappropriation of big data, and the cloud won’t make these risks go away. Exler lists some of the risks that stem from over-reliance on cloud technology, from the late delivery of business reports to the delivery of incorrect business information, resulting in decisions based on incorrect source data. Sound familiar? The gremlins that have haunted data analytic and management for years simply won’t disappear behind a cloud.

Exler makes three recommendations for moving big data into cloud environments – note that the solutions he proposes have nothing to do with technology, and everything to do with management:

1) Analyze the growth trajectory of your data and your business. Typically, organizations will have a lot of different moving parts and interfaces. And, as the business grows and changes, it will be constantly adding new data sources.  As Exler notes, “processing integration or hand off points in such piecemeal approaches represent high risk to data in the chain of possession – from collection points to raw data to data edits to data combination to data warehouse to analytics engine to viewing applications on multiple platforms.” Business growth and future requirements should be analyzed and modeled to make sure cloud engagements will be able “to provide adequate system performance, availability, and scalability to account for the projected business expansion,” he states.

2) Address data quality issues as close to the source as possible.  Because both cloud and big data environments have so many moving parts,  “finding the source of a data problem can be a significant challenge,” Exler warns. “Finding problems upstream in the data flow prevent time-consuming and expensive reprocessing that could be needed should errors be discovered downstream.” Such quality issues have a substantial business cost as well. When data errors are found, it becomes “an expensive company-wide fire drill to correct the data,” he says.

3) Build your project management, teamwork and communication skills. Because big data and cloud projects involve so many people and components from across the enterprise, requiring coordination and interaction between various specialists, subject matter experts, vendors, and outsourcing partners. “This coordination is not simple,” Exler warns. “Each group involved likely has different sets of terminology, work habits, communications methods, and documentation standards. Each group also has different priorities; oftentimes such new projects are delegated to lower priority for supporting groups.” Project managers must be leaders and understand the value of open and regular communications.

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Posted in Big Data, Cloud Computing, Data Quality | Tagged , , , | Leave a comment

Informatica Wins Award for Best Security Software

Last night Informatica was given the Silver award for Best Security Software by Info Security. The Best Security Software was one of the most competitive categories—with 8 finalists offering technologies ranging from mobile to cloud security.
Informatica won the award for its new Cloud Data Masking solution. Starting in June of last year, Informatica has steadily released a series of new Cloud solutions for data security. Informatica is the first to offer a comprehensive, data governance based solution for cloud data privacy. This solution addresses the full lifecycle of data privacy, including:

  •   Defining and classifying sensitive data
  •   Discovering where sensitive data lives
  •   Applying consistent data masking rules
  •   Measuring and monitoring to prove compliance

The Cloud Data Masking adds to Informatica’s leading cloud integration solution for salesforce.com includes data synchronization, data replication, data quality, and master data management.

 
Why is Cloud Data Masking important?

 

Sensitive data is at risk of being exposed during application development and testing, where it is important to use real production data to rigorously test applications. As reported by the Ponemon Institute, a data breach costs organizations on average $5.5 million dollars.

 

What does Cloud Data Masking do?

 

Based on Informatica’s market leading Data Masking technology, Informatica’s new Cloud Data Masking enables cloud customers to secure sensitive information during the testing phase by directly masking production data used within cloud sandboxes, creating realistic-looking, but de-identified data. Customers are therefore able to protect sensitive information from unintended exposure during development, test and training activities; streamline cloud projects by reducing the time it takes to mask test/training/development environments; and ensure compliance with mounting privacy regulations.

 

What do people do today?

 

Many organizations today will hand the masking efforts over to IT. This inevitably lengthens development cycles and delays releases. One of Informatica’s longtime customers and current partners, David Cheung of Cloud Sherpas, stated “Many customers wait days for IT to change the sensitive or confidential data, delaying releases. For example, I was at customer last week where the customer was waiting 5 days for IT to mask the sensitive data.”
Others use scripting or manual methods to mask the data. One prospect I spoke to recently said he manually altered the data but missed a few email addresses. So during a test run, the company accidentally sent emails to customers. These customers called back to demand what was going on. Do you want that to happen to you?

Visit Informatica Cloud Data Masking for more information.

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Posted in Application ILM, Data masking, Data Privacy | Tagged , , , , , | Leave a comment

Ready for Your Data Security Audit?

In a recent survey of Informatica customers,
• Over 60% of companies had a security audit in the last year
• 35% of the companies had an internal security audit
• 16% of the companies had both an internal security audit and one performed by an external auditor
• In addition, many of these organizations saw that another company in their same industry suffered a data breach.

These results are reinforced by the discussions I had with Audit and Compliance IT owners from various industries. Audits are on the rise as more customers require these audits before purchase. Compliance IT requires reports at a database or system level showing that the data has been protected. And they want to see these reports on a regular basis as data, including test data pulled from production environments, changes frequently.

Driving these audits and Informatica projects to protect data were the following top regulatory drivers (as reported by customers):
• SOX
• PCI
• PII
• PHI

These results are reinforced by the increasing use of Informatica’s regulatory and industry packs (containing pre-built rules and metadata), including PCI, PHI and PII. In addition to these areas, organizations I’ve spoken to are implementing projects to also protect non-public information, or confidential company information. For example, last week I spoke to a company about how they share detailed financial information about their company as part of the data they said to an outsourced partner. This financial information could be easily used to estimate company’s revenues and profits for any given quarter—before that information is released to the street, if at all.

In this same survey, the top benefits customers said that Informatica’s solution addressed included:
• Increasing productivity by leveraging pre-built masking techniques, accelerators and purpose-built tools
• Reducing the time it took to identify and capture optimal test cases, therefore reducing overall testing time
• Reducing the risk of data breach

Are you ready for your data security audit?

For more information on Informatica’s data security solutions for non-production environments, please join us for an upcoming webinar:

http://bit.ly/W5IciG

For more information on Informatica’s data security solutions in general, please see:

http://bit.ly/PGcJkq

 

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Posted in Application ILM, Data masking, Data Privacy, Uncategorized | Tagged , , , , | Leave a comment

Informatica Recognized By Gartner as a Leader in Data Masking and by Infosecurity for Best Security Software

Informatica was named as a leader in the 2012 Gartner Magic Quadrant for Data Masking. A couple of weeks ago, Infosecurity named Informatica as a finalist for Best Security Software for 2013.
Both the Gartner Magic Quadrant for Data Masking and Infosecurity Products Guide recognized Informatica for continued innovation:

  •  Gartner states, “The data masking portfolio has been broadening. In addition to SDM technology… the market is beginning to offer dynamic data masking (DDM)… ” (more…)
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Posted in Application ILM, Big Data, Data masking, Data Privacy, Uncategorized | Tagged , , , , , , | Leave a comment

The Next Frontier of Data Security: Minimizing the Threat of an Internal Data Breach

Adam Wilson, General Manager of ILM at Informatica talks about the next frontier of data security. The more data that is passed around internally, the more risk your company runs for a data breach. Find out why auditors are taking a closer look at the number of internal data copies that are floating around and what it means for your company’s risk of a data leak.

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