Tag Archives: data processing

Protecting Data is Good. Protecting Information Generated from Big Data is Priceless

By E.G. Nadhan, HP

This was the key message that came out of The Open Group® Big Data Security Tweet Jam on Jan 22 at 9:00 a.m. PT, which addressed several key questions centered on Big Data and security. Here is my summary of the observations made in the context of these questions.

Q1. What is Big Data security? Is it different from data security?

Big data security is more about information security. It is typically external to the corporate perimeter. IT is not prepared today to adequately monitor its sheer volume in brontobytes of data. The time period of long-term storage could violate compliance mandates. Note that storing Big Data in the Cloud changes the game with increased risks of leaks, loss, breaches.

Information resulting from the analysis of the data is even more sensitive and therefore, higher risk – especially when it is Personally Identifiable Information on the Internet of devices requiring a balance between utility and privacy.

At the end of the day, it is all about governance or as they say, “It’s the data, stupid! Govern it.”

Q2. Any thoughts about security systems as producers of Big Data, e.g., voluminous systems logs?

Data gathered from information security logs is valuable but rules for protecting it are the same. Security logs will be a good source to detect patterns of customer usage.

Q3. Most BigData stacks have no built in security. What does this mean for securing Big Data?

There is an added level of complexity because it goes across apps, network plus all end points. Having standards to establish identity, metadata, trust would go a long way. The quality of data could also be a security issue — has it been tampered with, are you being gamed etc. Note that enterprises have varying needs of security around their business data.

Q4. How is the industry dealing with the social and ethical uses of consumer data gathered via Big Data?

Big Data is still nascent and ground rules for handling the information are yet to be established. Privacy issue will be key when companies market to consumers. Organizations are seeking forgiveness rather than permission. Regulatory bodies are getting involved due to consumer pressure. Abuse of power from access to big data is likely to trigger more incentives to attack or embarrass. Note that ‘abuse’ to some is just business to others.

Q5. What lessons from basic data security and cloud security can be implemented in Big Data security?

Security testing is even more vital for Big Data. Limit access to specific devices, not just user credentials. Don’t assume security via obscurity for sensors producing bigdata inputs – they will be targets.

Q6. What are some best practices for securing Big Data? What are orgs doing now and what will organizations be doing 2-3 years from now?

Current best practices include:

  • Treat Big Data as your most valuable asset
  • Encrypt everything by default, proper key management, enforcement of policies, tokenized logs
  • Ask your Cloud and Big Data providers the right questions – ultimately, YOU are responsible for security
  • Assume data needs verification and cleanup before it is used for decisions if you are unable to establish trust with data source

Future best practices:

  • Enterprises treat Information like data today and will respect it as the most valuable asset in the future
  • CIOs will eventually become Chief Officer for Information

Q7. We’re nearing the end of today’s tweet tam. Any last thoughts on Big Data security?

Adrian Lane who participated in the tweet jam will be keynoting at The Open Group Conference in Newport Beach next week and wrote a good best practices paper on securing Big Data.

I have been part of multiple tweet chats specific to security as well as one on Information Optimization. Recently, I also conducted the first Open Group Web Jam internal to The Cloud Work Group.  What I liked about this Big Data Security Tweet Jam is that it brought two key domains together highlighting the intersection points. There was great contribution from subject matter experts forcing participants to think about one domain in the context of the other.

In a way, this post is actually synthesizing valuable information from raw data in the tweet messages – and therefore needs to be secured!

What are your thoughts on the observations made in this tweet jam? What measures are you taking to secure Big Data in your enterprise?

I really enjoyed this tweet jam and would strongly encourage you to actively participate in upcoming tweet jams hosted by The Open Group.  You get to interact with a wide spectrum of knowledgeable practitioners listed in this summary post.

NadhanHP Distinguished Technologist and Cloud Advisor, E.G.Nadhan has more than 25 years of experience in the IT industry across the complete spectrum of selling, delivering and managing enterprise level solutions for HP customers. He is the founding co-chair for The Open Group SOCCI project, and is also the founding co-chair for the Open Group Cloud Computing Governance project. Connect with Nadhan on: Twitter, Facebook, LinkedIn and Journey Blog.

 

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#ogChat Summary – Big Data and Security

By Patty Donovan, The Open Group

The Open Group hosted a tweet jam (#ogChat) to discuss Big Data security. In case you missed the conversation, here is a recap of the event.

The Participants

A total of 18 participants joined in the hour-long discussion, including:

Q1 What is #BigData #security? Is it different from #data security? #ogChat

Participants seemed to agree that while Big Data security is similar to data security, it is more extensive. Two major factors to consider: sensitivity and scalability.

  • @dustinkirkland At the core it’s the same – sensitive data – but the difference is in the size and the length of time this data is being stored. #ogChat
  • @jim_hietala Q1: Applying traditional security controls to BigData environments, which are not just very large info stores #ogChat
  • @TheTonyBradley Q1. The value of analyzing #BigData is tied directly to the sensitivity and relevance of that data–making it higher risk. #ogChat
  • @AdrianLane Q1 Securing #BigData is different. Issues of velocity, scale, elasticity break many existing security products. #ogChat
  • @editingwhiz #Bigdata security is standard information security, only more so. Meaning sampling replaced by complete data sets. #ogchat
  • @Dana_Gardner Q1 Not only is the data sensitive, the analysis from the data is sensitive. Secret. On the QT. Hush, hush. #BigData #data #security #ogChat
    • @Technodad @Dana_Gardner A key point. Much #bigdata will be public – the business value is in cleanup & analysis. Focus on protecting that. #ogChat

Q2 Any thoughts about #security systems as producers of #BigData, e.g., voluminous systems logs? #ogChat

  • Most agreed that security systems should be setting an example for producing secure Big Data environments.
  • @dustinkirkland Q2. They should be setting the example. If the data is deemed important or sensitive, then it should be secured and encrypted. #ogChat
  • @TheTonyBradley Q2. Data is data. Data gathered from information security logs is valuable #BigData, but rules for protecting it are the same. #ogChat
  • @elinormills Q2 SIEM is going to be big. will drive spending. #ogchat #bigdata #security
  • @jim_hietala Q2: Well instrumented IT environments generate lots of data, and SIEM/audit tools will have to be managers of this #BigData #ogchat
  • @dustinkirkland @theopengroup Ideally #bigdata platforms will support #tokenization natively, or else appdevs will have to write it into apps #ogChat

Q3 Most #BigData stacks have no built in #security. What does this mean for securing #BigData? #ogChat

The lack of built-in security hoists a target on the Big Data. While not all enterprise data is sensitive, housing it insecurely runs the risk of compromise. Furthermore, security solutions not only need to be effective, but also scalable as data will continue to get bigger.

  • @elinormills #ogchat big data is one big hacker target #bigdata #security
    • @editingwhiz @elinormills #bigdata may be a huge hacker target, but will hackers be able to process the chaff out of it? THAT takes $$$ #ogchat
    • @elinormills @editingwhiz hackers are innovation leaders #ogchat
    • @editingwhiz @elinormills Yes, hackers are innovation leaders — in security, but not necessarily dataset processing. #eweeknews #ogchat
  • @jim_hietala Q3:There will be a strong market for 3rd party security tools for #BigData – existing security technologies can’t scale #ogchat
  • @TheTonyBradley Q3. When you take sensitive info and store it–particularly in the cloud–you run the risk of exposure or compromise. #ogChat
  • @editingwhiz Not all enterprises have sensitive business data they need to protect with their lives. We’re talking non-regulated, of course. #ogchat
  • @TheTonyBradley Q3. #BigData is sensitive enough. The distilled information from analyzing it is more sensitive. Solutions need to be effective. #ogChat
  • @AdrianLane Q3 It means identifying security products that don’t break big data – i.e. they scale or leverage #BigData #ogChat
    • @dustinkirkland @AdrianLane #ogChat Agreed, this is where certifications and partnerships between the 3rd party and #bigdata vendor are essential.

Q4 How is the industry dealing with the social and ethical uses of consumer data gathered via #BigData? #ogChat #privacy

Participants agreed that the industry needs to improve when it comes to dealing with the social and ethical used of consumer data gathered through Big Data. If the data is easily accessible, hackers will be attracted. No matter what, the cost of a breach is far greater than any preventative solution.

  • @dustinkirkland Q4. #ogChat Sadly, not well enough. The recent Instagram uproar was well publicized but such abuse of social media rights happens every day.
    • @TheTonyBradley @dustinkirkland True. But, they’ll buy the startups, and take it to market. Fortune 500 companies don’t like to play with newbies. #ogChat
    • @editingwhiz Disagree with this: Fortune 500s don’t like to play with newbies. We’re seeing that if the IT works, name recognition irrelevant. #ogchat
    • @elinormills @editingwhiz @thetonybradley ‘hacker’ covers lot of ground, so i would say depends on context. some of my best friends are hackers #ogchat
    • @Technodad @elinormills A core point- data from sensors will drive #bigdata as much as enterprise data. Big security, quality issues there. #ogChat
  • @Dana_Gardner Q4 If privacy is a big issue, hacktivism may crop up. Power of #BigData can also make it socially onerous. #data #security #ogChat
  • @dustinkirkland Q4. The cost of a breach is far greater than the cost (monetary or reputation) of any security solution. Don’t risk it. #ogChat

Q5 What lessons from basic #datasecurity and #cloud #security can be implemented in #BigData security? #ogChat

The principles are the same, just on a larger scale. The biggest risks come from cutting corners due to the size and complexity of the data gathered. As hackers (like Anonymous) get better, so does security regardless of the data size.

  • @TheTonyBradley Q5. Again, data is data. The best practices for securing and protecting it stay the same–just on a more massive #BigData scale. #ogChat
  • @Dana_Gardner Q5 Remember, this is in many ways unchartered territory so expect the unexpected. Count on it. #BigData #data #security #ogChat
  • @NadhanAtHP A5 @theopengroup – Security Testing is even more vital when it comes to #BigData and Information #ogChat
  • @TheTonyBradley Q5. Anonymous has proven time and again that most existing data security is trivial. Need better protection for #BigData. #ogChat

Q6 What are some best practices for securing #BigData? What are orgs doing now, and what will orgs be doing 2-3 years from now? #ogChat

While some argued encrypting everything is the key, and others encouraged pressure on big data providers, most agreed that a multi-step security infrastructure is necessary. It’s not just the data that needs to be secured, but also the transportation and analysis processes.

  • @dustinkirkland Q6. #ogChat Encrypting everything, by default, at least at the fs layer. Proper key management. Policies. Logs. Hopefully tokenized too.
  • @dustinkirkland Q6. #ogChat Ask tough questions of your #cloud or #bigdata provider. Know what they are responsible for and who has access to keys. #ogChat
    • @elinormills Agreed–> @dustinkirkland Q6. #ogChat Ask tough questions of your #cloud or #bigdataprovider. Know what they are responsible for …
  • @Dana_Gardner Q6 Treat most #BigData as a crown jewel, see it as among most valuable assets. Apply commensurate security. #data #security #ogChat
  • @elinormills Q6 govt level crypto minimum, plus protect all endpts #ogchat #bigdata #security
  • @TheTonyBradley Q6. Multi-faceted issue. Must protect raw #BigData, plus processing, analyzing, transporting, and resulting distilled analysis. #ogChat
  • @Technodad If you don’t establish trust with data source, you need to assume data needs verification, cleanup before it is used for decisions. #ogChat

A big thank you to all the participants who made this such a great discussion!

patricia donovanPatricia Donovan is Vice President, Membership & Events, at The Open Group and a member of its executive management team. In this role she is involved in determining the company’s strategic direction and policy as well as the overall management of that business area. Patricia joined The Open Group in 1988 and has played a key role in the organization’s evolution, development and growth since then. She also oversees the company’s marketing, conferences and member meetings. She is based in the U.S.

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Data Governance: A Fundamental Aspect of IT

By E.G. Nadhan, HP

In an earlier post, I had explained how you can build upon SOA governance to realize Cloud governance.  But underlying both paradigms is a fundamental aspect that we have been dealing with ever since the dawn of IT—and that’s the data itself.

In fact, IT used to be referred to as “data processing.” Despite the continuing evolution of IT through various platforms, technologies, architectures and tools, at the end of the day IT is still processing data. However, the data has taken multiple shapes and forms—both structured and unstructured. And Cloud Computing has opened up opportunities to process and store structured and unstructured data. There has been a need for data governance since the day data processing was born, and today, it’s taken on a whole new dimension.

“It’s the economy, stupid,” was a campaign slogan, coined to win a critical election in the United States in 1992. Today, the campaign slogan for governance in the land of IT should be, “It’s the data, stupid!”

Let us challenge ourselves with a few questions. Consider them the what, why, when, where, who and how of data governance.

What is data governance? It is the mechanism by which we ensure that the right corporate data is available to the right people, at the right time, in the right format, with the right context, through the right channels.

Why is data governance needed? The Cloud, social networking and user-owned devices (BYOD) have acted as catalysts, triggering an unprecedented growth in recent years. We need to control and understand the data we are dealing with in order to process it effectively and securely.

When should data governance be exercised? Well, when shouldn’t it be? Data governance kicks in at the source, where the data enters the enterprise. It continues across the information lifecycle, as data is processed and consumed to address business needs. And it is also essential when data is archived and/or purged.

Where does data governance apply? It applies to all business units and across all processes. Data governance has a critical role to play at the point of storage—the final checkpoint before it is stored as “golden” in a database. Data Governance also applies across all layers of the architecture:

  • Presentation layer where the data enters the enterprise
  • Business logic layer where the business rules are applied to the data
  • Integration layer where data is routed
  • Storage layer where data finds its home

Who does data governance apply to? It applies to all business leaders, consumers, generators and administrators of data. It is a good idea to identify stewards for the ownership of key data domains. Stewards must ensure that their data domains abide by the enterprise architectural principles.  Stewards should continuously analyze the impact of various business events to their domains.

How is data governance applied? Data governance must be exercised at the enterprise level with federated governance to individual business units and data domains. It should be proactively exercised when a new process, application, repository or interface is introduced.  Existing data is likely to be impacted.  In the absence of effective data governance, data is likely to be duplicated, either by chance or by choice.

In our data universe, “informationalization” yields valuable intelligence that enables effective decision-making and analysis. However, even having the best people, process and technology is not going to yield the desired outcomes if the underlying data is suspect.

How about you? How is the data in your enterprise? What governance measures do you have in place? I would like to know.

A version of this blog post was originally published on HP’s Journey through Enterprise IT Services blog.

NadhanHP Distinguished Technologist and Cloud Advisor, E.G.Nadhan has more than 25 years of experience in the IT industry across the complete spectrum of selling, delivering and managing enterprise level solutions for HP customers. He is the founding co-chair for The Open Group SOCCI project, and is also the founding co-chair for the Open Group Cloud Computing Governance project. Connect with Nadhan on: Twitter, Facebook, LinkedIn and Journey Blog.

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