Big Data – You Need It, But How Do You Secure It
Big Data. It’s here to stay, and it’s making its way to the forefront of Data Security conversations. Though by nature, it expands your attack surface, the analytics Big Data supports are far too essential in understanding the day-to-day goings-on, to do without. Pulling from a multitude of datasets and sources– including text analytics, data mining, prediction analysis, statistics, etc., properly organized Big Data is a thing of beauty, conveying every last detail of what is happening in your organization and why. What types of competitive advantages can you gain as an organization using Big Data for analytics? Why are cyber criminals after your Big Data? How do you properly secure Big Data? What happens when you fail to secure your Big Data?
Why Is Big Data So Important?
Consisting of strings of numbers from multiple sources, Big Data in and of itself is just information. To the trained eye, however, these numbers tell all– just about anything you could want to know about your organization, in fact. What’s coming, what’s going. Early gains, unanticipated losses. Any tool offering that kind of clarity is going to support more informed, timely decisions– and after all, aren’t good decisions at the base of every company who consistently takes the lead? Intelligence in the right hands is everything when you are trying to lead a company, because it helps you to see and anticipate organizational opportunities that could otherwise go unseen, and anyone at the helm wants to make informed decisions that will bring about growth, development, and strength for everyone involved in the organization. The bottom line is that numbers don’t lie, and properly gathered, organized, and displayed business intelligence can help you stay ahead of the curve.
Big Data Is a New Way of Thinking
For years, analytics have relied on inductive reasoning. “Times, they are a changing” though, and organizations can no longer get the levels of detailed insight they need into their platforms by looking at them from a top-down perspective. This means that in the modern context, everything falls into a requirements-based environment where rigidity of governance reigns supreme. Further, once it is organized, data is meant to be shared. How else can teams glean the maximum understanding of how people are interacting in your data environment if they can’t see and share it– from those who touch it first to analysts, committees, and decision makers? Big Data collaboration monitors the opportunities, allowing for further A-B testing or straight experimentation that helps leadership hone operations and create a better business environment. Hadoop is the main platform utilized for structuring and formatting the Big Data and making it available to your teams, which then supports collaboration.
Hadoop’s open source approach has been transformative for the business intelligence landscape in truly powerful ways. Yet, with so much powerful data being pulled together from so many sources and connected to multiple endpoints, one has to consider the vastly increased attack surface in environments using Big Data. So the question arises, Who all has access to our environment? How can we track what they do once they gain that access? Unfortunately, Big Data is also powerful even when it falls into hands outside of your organization. Such granular data has heavy risk attached to it. If not properly secured, Big Data can be leaked to ill-intentioned hands, leaving cyber criminals, or even competitors, to wreak havoc on the inner-workings of your environment. Even worse, it can leave you responsible for cyber criminals’ intrusion into the live and privacy of your customers.
Big Data is Used in Fraud Prevention
Intrusion detection and intrusion prevention are 2 fraud prevention techniques that play a key role in helping detect threats at the earliest stage, and both are derived from Big Data. Logs are crucial to understanding both good and bad behavior internally. Unfortunately, most organizations only pay attention to their logs when they realize there has been an intrusion. This approach, however, is inconsistent with all the trends associated with Big Data, which call for ongoing analysis of real-time information that enables informed decisions, based in forward-thinking. Who knows your organization better than you? Why not use the logs to create a very sophisticated fraud prevention solution that goes beyond retrospective analysis?
The detailed logs are perhaps Big Data’s greatest contribution to the field of data security, enabling fraud detection and intrusion prevention. Though, even when employing them both properly in a multi-layered approach, it can still be extremely difficult to secure every possible attack vector. Big Data simply has too many entry and exit points. Further, how can one be sure of the absolute security of every vector in the supply chain? So the question becomes, what else can I do? Is there a more secure way to maintain my organization’s analytics and collaboration without compromising operability?
Understanding the Risk of Big Data
The most important thing to understand when handling Big Data is that it expands your attack surface. Your organization is manipulating mountains of data at warp speed from every department, from both inside and outside of your environment, and while most think only of payment information (PCI) or financials when securing their data, the reality is that now Big Data includes customer IDs, buying patterns, customer demographics, etc. This means that much of what you are handling is not only worth protecting from your competitors, but it falls under definitions of customer PII (Personally Identifiable Information). Risking exposure of that PII means risking customer trust, potential law suits, and even violation of federally regulated privacy laws.
Securing Big Data
The best way to secure PII is tokenization, period. Even layering security won’t necessarily keep each and every attack vector your environment, plus each and every endpoint or attack vector in your supply chain, from being accessed by cyber criminals. The best way to protect your data is to give thieves nothing to steal, and after the dataset is tokenized, the only thing that you store in your environment is a token, that if breached, reveals no sensitive information from your organization. Using tokens in your business systems, you get all of the power of Big Data analytics with a minimal amount of risk. Proper tokenization empowers Big Data management with the final goal of enabling multiple members inside your organization to collaborate with these datasets in a safe manner.