You can’t prevent cybercrime – but you can stop it with network anomaly detection

Network anomaly detection is an analytical technique for identifying and stopping cyberattacks before your data has been compromised. Since it’s getting harder and harder to stop the network breaches, your best option is to catch the hackers before they can do any harm.

But let’s back up.

Network wiresWho’s at risk of a cyberattack? Hollywood studios. National retailers. Federal agencies. Large-scale insurers. Global banks. Mobile providers. To name a few.

The list of major organizations struck by cybercrime in the last year alone is astounding. When you stop to think about how many reputable organizations have had their networks breached recently, it becomes clear that even our best methods for preventing cyberattacks aren’t working.

The computer networks at most large organizations are so big and so wide reaching, it’s becoming harder and harder to put up walls to keep the cybercriminals out. There are just too many possible points of entry.

How to identify hackers with network anomaly detection

So what can you do? If stronger firewalls aren’t enough, should you simply raise your hands in resignation and admit that dealing with cybercrime is the price we pay in a modern, data-filled world?

Of course not. But you should admit that preventive methods aren’t enough, and start making plans now for catching the hackers once they’re inside your networks.

In fact, you have to assume they have already breached your network. This isn’t hyperbole, and it’s not meant to scare you. It’s simply true. But here’s the good news: It can work to your advantage to know they are in there.

So what does a network breach look like? Well, it’s hard to see because it can happen slowly and steadily over a period of months as the hackers are learning their way around your network and making steady progress to infiltrate deeper into your systems where the most secure data lives.

In fact, many of the compromises you’ve heard about in the news involved hackers who were in the network for months before being detected. You can think of the first few months of activity as a quiet, fact-finding mission.

We describe this as low-and-slow reconnaissance activity because the hackers are working so quietly and methodically that their movements inside your network often go undetected. The only way to know it’s happening is to have a system in place that tracks normal network behavior and compares it to current activity, searching continuously for new and unusual patterns. This is network anomaly detection.

Why analytics are key for network anomaly detection

Here’s the challenge, though: The computer networks of most large organizations are processing hundreds of thousands of events per second during normal business hours. It’s not unusual to see tens of billions of machine-to-machine records daily on an average network. Monitoring that activity, processing it in flight and identifying unusual patterns takes a lot of analytical power.

Network anomaly detection can do that and more. The method uses event stream processing to understand network activity within the appropriate business context. In other words, the system learns what abnormal activity looks like in your networks. For example, it might mean a series of IP addresses that are never active at night suddenly are. Or it might mean computers from the sales department that never transmit information to the HR organization suddenly are. These types of behaviors could be happening in the background without you ever knowing.

What else could you be missing, and how can you detect it? Find out in our latest cybersecurity news announcement, and watch the live coverage from The Premier Business Leadership Series now.

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What makes your corporate culture global?

As the borders of commerce disappear, some economists have argued that the global economy makes us all global companies. But how do you become a truly global organization? Does selling products overseas make you global? Does opening offices around the globe make you global? I think it’s more than that.

Happy employees in building QLike many technology companies, SAS has devoted itself to becoming a truly global organization. To us, this means more than simply opening up offices and selling software in multiple locations around the globe. It means extending the SAS culture and SAS values into each of those offices, from Argentina to Australia, Singapore to South Africa and everywhere in between.

Our corporate values have been well documented through many great workplace awards over the years. SAS has been ranked as one of the best places to work in the US since 1998, and this year SAS also ranked as a best place to work in Italy, Portugal, Spain, Greece, Germany, Brazil, the Netherlands, Ireland, China and Australia.

As a culmination to all these achievements, the world's best multinational workplace award was announced this week, and SAS is No. 2 for the third year in a row. Why does this matter? Because we have succeeded in replicating our corporate values around the globe, and it means that our employees, our partners and our customers throughout the world are benefiting from those values.

Let me be clear: Our corporate culture is not just for employees and their families. The culture here translates into value for partners and customers too. How? It means our employees can concentrate on work, because many of their day-to-day distractions are already resolved, and that results in stronger, quicker innovation for customers.

Spending money on a great place to work is a lot more gratifying than spending money on recruiting and onboarding, but it’s more than that.

Think about the disruption that turnover can cause in an organization. You lose a large part of your corporate memory when key employees leave, and you lose time having to retrain new employees. Not to mention the obvious morale issues when co-workers depart, and the instability that customers and partners feel when their company contact changes. The global turnover rate at SAS is around 5 percent, in an industry that consistently has about a 16 percent turnover rate.

In our business, where we help customers solve their toughest problems, success is not just about the technology we sell but also about the relationships we build over time. It’s our corporate culture – and all that comes with it – that allows us to build and maintain those relationships.

When we extend our culture around the globe, we are not only valuing our employees, we are also supporting innovation and helping to build and maintain stronger relationships with other companies around the globe.

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Securing the Internet of Things with analytics at the edge

The Internet of Things, that glorious futurescape in which billions of connected devices take much of the work and tedium out of daily living.

As human beings, we’re addicted to our stuff and what it does for us. So a world in which most of our cell phones and other devices are smart enough to make decisions on their own can’t help but be a better one. Right?

IoT3Well, partly. Our ability to use huge amounts of data from all sorts of gadgets will give us the ability to improve many areas of life, from commerce to medicine, and from transportation to government.

But as I wrote in my last blog, the world isn’t all sweetness and light, and plenty of people want to do us harm. Hackers can break into anything with an IP address, and recent news stories show it’s already happening. Wired magazine’s intentional hack of a Jeep Cherokee demonstrated just how easy it is to do.

So if you’re only considering the time-saving benefits of the IoT era, you might also ponder the flipside. What happens when your car gets hijacked? When your fridge or your air conditioning goes rogue? When your insulin pump turns on you?

As the IoT becomes a reality, and moves from industrial to consumer applications that reach deep into our daily lives, the time is right to ask these kinds of questions. Connecting devices just because we can is not a good enough reason to start bestowing them with intelligence. It’s imperative to pause, weigh the benefits against the risks and create security plans up front. It’s our responsibility to think a few moves ahead.

The tools to do so are already there. Thanks to analytics, we can fight all kinds of cybercrime. We can stream, collect and store data in low-cost environments. Advances in highly scalable in-memory analytics allow us to produce insights and predictions in near real time.

This ability to detect variances from the norm allows us to spot and catch the bad guys, from terrorists engaging in bank fraud schemes to someone trying to tamper with your Fitbit.

So that part is set. What I see missing, however, is a greater sense of foresight. Why didn’t the carmakers design their digital systems to be more secure? It’s a shame that we’re thinking of this after the fact.

So I call on all those who are designing for the IoT era to remember the need to future-proof. Because we know the hacker challenge is out there, it’s not acceptable to bring products to market unless they’ve been cyber-proofed. In our rush to achieve the noble goals of convenience and progress, we cannot leave the consumer open to harm or attack.

The way forward is by building in something called “analytics at the edge.” By the edge, we mean on or near the device as opposed to in some central storage location. Doing analysis at the edge makes sense when the flow of data is very fast, very dense or largely unchanging. In those cases, you don’t want to waste bandwidth sending it over the network for analysis, so you place the analytics “at the edge” of the device. Putting analytics on or near the device will help protect them, and us, by allowing us to spot abnormalities faster.

And as human beings, that is definitely our responsibility to do. They’re just devices, after all. Let’s use our superior brains to make them smart enough to know when something dumb is happening.

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Customers for life

Do you ever stop and think about why it’s so hard to get really great customer service? As consumers, most of us are making transactions all day long, but it’s rare that someone actually surprises or delights us. More often, we rack up negative experiences. Think of what it’s like to fly a US airline or wait for your telco company to come fix your cable, and you’ll be channeling the kind of pain I mean.

It’s funny that customer alienation happens so often, given that most organizations are sincerely trying to build a good brand and want nothing more than to create customers who stay for life. So why is there a disconnect? Where do they go astray?

I’d argue that’s because most organizations lack sufficient knowledge. To wow someone, you have to know them. And knowing customers is hard. Understanding what drives them is even harder.

Fortunately we have analytics, the sure-fire way for organizations to uncover insights that make for happy customers. If you want to turn your customer data to business advantage, there’s no better way than applying analytics to make data-based decisions. But building an analytics culture doesn’t just happen naturally. It takes time, persuasion and investment.

How exactly to get there is the subject of my upcoming talk at EMEA Analytics 2015 in Rome. You’ll have to wait for my presentation to hear my complete six-step checklist for building an analytics culture, but I can share one key point with you now, and that is that you have to make a distinction between analyzing your customers and engaging your customers.


Let me give you an example. Say you want to work on customer retention. You build a model to tell you which people are most at risk of leaving. You generate a list of those at risk and send it over to marketing. And then marketing targets those customers with an offer to retain them. That’s analyzing your customers. Engaging your customers involves a different approach.

Engagement means taking a step back and saying, “I’ve built this model and it tells me who’s at risk. But I want to know more. Why do we even have customers at risk in the first place? What are the underlying causes that are putting them there? And instead of accepting their departure as inevitable, what could we do differently to eliminate the things that make them want to leave?”

Under this approach, at the same time you send out an offer to the customers at risk of leaving, you also take a step back and consider how to improve your CRM process. Because looking at that bigger picture is the only way you’re going to delight the customer. And that’s the kind of mental shift you have to make if you want to create customers for life.

But that’s just the beginning of the discussion. I can’t wait to see you in Rome to describe more.

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Doing good with data and analytics

You don’t have to look any further than your smartphone to know you can use data and analytics for some pretty frivolous things. Consider something as simple as your socks. The Sock Sorter app uses sensors in your socks to help you find matches for every sock. Likewise, helps identify when your black socks are faded and ready to replace. Then, of course, you can sign up for a sockscription to have new replacement socks delivered every three months.

Child and grandfather holding handsOn the other end of the spectrum are the humanitarian uses of data that prove how much good analytics can do in the world. Socks are important, but I’d much rather hear about analytics helping provide shelter and water for earthquake victims.

At SAS, we get to hear stories of data doing good almost every day. I’ve told you about some of these heartwarming stories before – from the disaster recovery efforts mentioned above to the data-sharing projects that could help cure cancer.

For every one of these stories, there’s a technology angle and there’s the people angle. The people who are helped, and the people who are helping. The stories of these people are always interesting to me, and recently I’ve read three stories with a common theme: how can analytics be used to help assist society’s most vulnerable populations – and make sure that help is going where it is needed most? The three examples:

  • Foster youth in New York City are receiving the services they need to become functioning adults, partly due to a study showing that support programs for young adults can reduce homelessness and jail time.
  • Children in abusive homes in Florida are becoming easier to identify and assist, thanks to new research and assessment tools being used by the Department of Children and Families to reduce childhood deaths.
  • Young adults on public assistance are getting special attention in New Zealand, where research shows that investing in support at a young age can reduce the occurrence of lifelong welfare dependence.

From Florida to New Zealand, these programs show how analytics can make a difference in the world – and in the lives of many young people. But these are just three examples. Open data initiatives and data sharing platforms are making it easier than ever before to get involved with do-good analytics projects.

If you’re an analyst or data scientist with an interest in using analytics to improve the quality of our lives, consider volunteering your skills for organizations like DataKind and DataLook. Or seek out humanitarian projects on sites like Kaggle.

You can also follow the Twitter hashtags #dataforgood to find opportunities for citizen data scientists.

Many of you are using analytics at work to prevent fraud, assess risk or improve marketing programs. These are worthy efforts, but if you want to do more, there are many options available for giving back.

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Creating customer experience, one contact at a time

FBlog3I’m still old school enough to enjoy reading the local News & Observer on weekends. I even like flipping through pages that leave my fingers smudged from newsprint. I was reading an editorial piece a while back, and a phrase leapt out at me. A correspondent wrote, “We are in the next phase of consumer demand called the experience economy.”

The experience economy. I’ve heard the term before, but this time something clicked. In my job, I get to do some pretty cool things, like think about ways we can make our customers happier. Or how to get better at how we support them. That’s why the words “experience economy” caught my attention. Because you can have an outstanding product and efficient service, but to really make customers happy, to give them a great experience, it takes people. People either make or break the experience.

A friend of mine flew on Southwest Airlines recently. She told me about waiting in long check-in lines where the mood was almost jovial. People chatted with each other and were basically good sports about the whole waiting thing. She noticed that the five or so Southwest employees set the upbeat tone, doing their best to make it a pleasant experience, talking with the travelers and connecting with them in small but human ways.

Across the room, a rival airline had the same long lines, but the employees looked miserable, frustrated and harried, and so did the line of passengers. Through that experience, her respect for Southwest went up a few notches. That’s the difference people make. It’s not just about eliminating lines, although that would be nice too. It’s about making the experience okay despite the lines.

Impact of one

Here’s the thing: We’re connected to each other more today than we’ve ever been. Our actions influence, impact and even change the experience of those we come in contact with. The attitude of those five airline employees mattered. How they treated Southwest customers that day influenced and maybe even changed their customers’ perception of the entire company.

So, if I’m running a company, an important question I have to ask is, what is the frame of mind of my employees? When someone answers a phone call, sends an email or ships a product, what do their actions say about the company? It’s on me to make sure the workplace supports employees and gives them the leeway to delight my customers. Otherwise, they will pass their frustration through to customers. In the experience economy, that’s bad business.

As employees, we need to remember that we are a physical representation of the company. We might as well wear a T-shirt every day proclaiming, “I am Company X.” The daily contact we have with a customer, no matter how small, could be pivotal. One action by one person could be the very thing that causes a customer to think that the company is either great to work with or one to avoid.

I’m still smarting from an encounter I had with one person. I’m a “diamond member” at a hotel chain that shall go unnamed, racking up 50 to 60 stays a year. I had a stay booked, but my travel plans changed on the day of my arrival. When I called to ask about cancelling, the hotel reservationist read me the policy: “No cancellations within 24 hours.” It didn’t matter how I pleaded my case or that I had 50 stays on record. And that diamond status? Worthless. I gave the employee every chance, even asked if a supervisor or manager might be able to help. The unapologetic answer was: no cancellations, no exceptions. Period.

I paid the hotel bill for that night. But that one employee lost the hotel chain the 50 stays I would have booked in the future – and my referrals.

Golden rule

The scary thing is, you might have that kind of impact and not even know it. Most companies have several streams of business going on at all times, and those streams touch customers. The unreasonable customer you are dealing with today in contracts might be the ideal customer that product development needs for a beta project tomorrow. It’s the old golden rule, to treat a person the way you would like to be treated. Try to do right by the customer, and you’ll never be wrong.

It’s a mindset. Our customers are human beings, and we need to keep them at the center of what we do. After all, they’re buying more than our products. They can buy similar products from other vendors in many cases. Today, they are buying the whole experience. The good news is that technology plus people is a winning combination for creating an experience to be proud of. If you want to find out more about how technology can help make the right connections between people, we’ve got customer experience management info about that, too.

Thank you

It only seems fitting to wrap this up by expressing my gratitude to and for SAS’ customers. The Temkin Group 2015 report is out, and our customers gave SAS top marks for customer experience, scores I don’t take lightly.

To SAS customers: Thank you for your confidence in us. You have my commitment that we will do all we can to continue to earn your trust and to give you the experience you want and deserve.

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Beyond “back-to-school”: Giving everyone the Power to Know® through education

“Back-to-school” is a common theme this time of year, but learning isn’t something that is relegated to a certain point on the calendar or even a particular point in life – it’s a lifelong journey.

Whether you are in early education using mobile technology for learning, a student or adult learner looking for free SAS® software, or an educator looking for new ways to teach, SAS has something to offer. We support education because it is an investment in the future, not just for our company, but for the world.

Learn how SAS can help you on your lifelong education journey (click image)

Learn how SAS can help you on your lifelong education journey (click image)

The Internet of Things and the continued growth of big data will create millions of jobs for data scientists in the coming years. But before that data scientist comes knocking at our door – or the doors of our customers – he or she will have had to take college-level courses in computer science and/or analytics.

Before even getting into college, though, kids need to be comfortable and familiar with subjects all throughout K-12. We can and we must support learning at all levels.

There are many SAS education programs and initiatives making a difference in K-12 and higher education. More than 26,000 teachers and students signed up in August to use SAS Curriculum Pathways free digital learning resources, bringing the total number of users to more than 550,000 around the world. More than 350,000 professors, students and independent learners are taking advantage of the free SAS software and training  offered through SAS Analytics U. I encourage you to check those out and think about how they can help you and/or your children along their journey.

The accompanying infographic shows how those programs and others map to a lifelong commitment to learning.

I find it particularly rewarding to bring students and teachers to SAS to learn about how we can support their interests and goals.

Last week, I met with three budding data scientists, ages 10-11, who used analytics to learn more about their passions. Two focused on sports, one on pet adoption. We arranged a special day where they presented their research, met with a sports analytics expert as well as experts who analyze data on service dog breeding and endangered species preservation.


Kids can get excited about data and analytics if we can help them understand the relevance to their lives. This can put them on a path to rewarding careers in analytics or other STEM disciplines.

SAS hosts other events such as the annual Math Summit for teachers, various STEM days for students and numerous professor trainings where we help them integrate SAS into their instruction.

Next week, we will co-host a Data & Analytics Summit with Achieving the Dream, where more than 160 community and technical college representatives will learn how data and analytics can improve enrollment, course availability and student outcomes. Those schools are critical to building a talented workforce with the skills employers need.

From pre-K to the workforce and beyond, we should never stop learning. What’s the next step in your journey?

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Why you need thick data and thin data

137957211I’ve been running across the term “thick data” lately and even came across a definition earlier this week from Word Spy, an online glossary that highlights new pop culture terms before they’re cool.

So, what is thick data and why does it matter? It’s what we’ve traditionally thought of as qualitative data, and it could become even more important in the future as a balancing point to the thin data at the edges of the Internet of Things. Why? Because thick data can provide deeper meaning – the context, if you will.

After all, you can’t learn everything from 1s and 0s. Quantitative data can help you a lot, but how do you incorporate the softer stuff too? The human stuff that holds the meaning behind the numbers – answering questions such as why the numbers are what they are, and all the other stuff that’s not obvious from hard numbers alone.

A more practical example: Mobile marketing

Now that you have an understanding of thick data, why should you care about it for your business? Let’s look at mobile marketing. What I so often see as a consumer are brands targeting me with things that are absolutely ridiculous.

I can understand how they’re making assumptions about me based on the limited data they have access to, but it doesn’t make sense if you really know me. There are all kinds of possibilities when looking at that limited scope of data, but I might fall out on the wrong side of a decision tree, and rot there on the ground.

How can brands advance traditional data mining and statistical analysis to improve some of these digital promotions? Maybe you look at marrying structured data with unstructured data, including both qualitative and quantitative insights – creating a soup of both thick and thin data, thus using different types of data to improve the way you practice your art.

Thin data might be a burst of information with limited context. It’s not all the information that may affect a given scenario, like what offer might be relevant to me when I’m in my local grocery store late at night on a weekday. It may just be one piece of the equation. Look at iBeacon data streams, for example. This geo-targeted data knows when consumers are close by, or inside, a specific location. It’s proximity-based data and it’s valuable, but there isn’t a lot of marketing insight that can be derived just from that dimension of data, except that you’re there. Or, your device was there. We have to combine that information with something else that’s more robust to make smarter use of the data.

Thin data is valuable to collect and explore in large quantities, but the danger in using thin data alone comes with making direct offers without context. As marketers, let’s not blow it. Let’s recognize that, while all this stuff is coming at us, we still have to be good stewards of the data.

Make sure you know something about who you’re talking to before you send a message to the consumer. Learn to listen to all the data before you speak or initiate a conversation. If initially you come out with an offer just because you have a device that’s within a geo-fenced area, that communication might come back to haunt you. And, naturally, make sure you’re not invading privacy or misusing the data you have.

Data streams generated in the Internet of Things are giving us access to more and more data every day, but our thick data from posted videos, photos, notifications and conversations is growing too. How can we use both the thick and the thin to benefit the consumer and the brand?  If you can get that right, your opportunity to innovate and approach your marketing campaigns differently will be huge.

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3Q 2015 Intelligence Quarterly: How to lead the digital transformation

IQ_3Q_2015_cover_internationalThere it is, staring us in the face: the answer that will forever transform banking as we know it and propel the industry into the digital age. 

As a leader of the bank, you now have two options:

  1. The first is to put big data into the liability or even the risk bucket, and fight it with primitive tools such as costly, rigid and complex core banking and RDBMS systems.
  2. The second is to embrace big data and come out in front of the regulatory-driven compliance tsunami.

Embracing big data will empower the front office staff with the controls they need to make decisions at the point of the transaction and, at the same time, eradicate the complexity fueled by silo-based point solutions.

What is your choice?

To date, 90 percent of your colleagues are choosing option one and only dipping their toes into option two out of plain fear. The latest issue of Intelligence Quarterly focuses on the 10 percent who are boldly embracing option two.

How are they doing it? Many are taking a factory approach to big data and analytics, constantly trying out new ideas in a big data lab, and then taking what works and repeating it with precision. In particular, the factory approach processes information and turns the raw material (data) into something useful.

More often than not, banking executives will point out that they have big data projects started, and they are working to cut through the complexity. With very few exceptions, however, they struggle to embrace the digital age and attempt to solve growing data-intensive problems with yesterday’s tools and approaches.

When it comes to big data, who is in charge?

Any given person, department or function alone cannot change the bank. To really change the bank, it will take the efforts of IT, risk, retail and other departments all working together.

IT does automation and infrastructure, not optimization, nor does it industrialize the model management. It’s not unusual to find dozens of custom-built solutions with millions of incompatible rules and hundreds of copies of the data floating around in the bank. Combine the short-term focus with these silo-based point solutions, and we have a picture of true complexity.

Find out how banks are moving away from this level of complexity and shifting to a strategic state. Part of this process involves moving away from vendor consolidation and asking vendors to take on more by providing software as a service or even as an appliance. Instead of asking the vendors that contributed to the problems in the first place to do more of the same, banking leaders are asking: What will it take for you to replicate results to larger parts of our increasingly complex value chain?

The way that banks look at IT is changing. Indeed, a major banker I respect recently asked, ”If I spend $300 million annually on AML alone, is it not reasonable to think that I should be receiving AML as an appliance?”

How can the risk department help lead the change? Throwing manpower at the problem will not break the growing wave of regulatory-driven compliance. Instead, we must let go of our investigative warehouses and the idea of offshore back offices. For an example, read how the chief model risk officer of Discover Financial Services embraced an analytical factory concept to cut through CCAR requirements with little effort. Others banks are using the analytical factory approach to empower front office staff to take ownership and responsibility of decisions at the point of the transaction.

Most heads of retail banking also are automating their customer interactions instead of optimizing the customer experience across touch points and channels. Why embrace hyperfragmentation through rigid, channel-based structures and systems when you could drive consistency across channels? Instead, you could use analytics to calculate risk-adjusted performance per client while creating personalized experiences and taking the costs out of the system.

Cut through the complexity

The best way to improve client performance while personalizing services to each client is to embrace the technology required to cut through the complexity. Unlike the rules-based technology of the past, ownership of the analytical factory calls for a new set of skills, be it social, demographic, economic or any other faculty conceivable. For example, SAS is working with one global, systemically important bank (G-SIB) that hired a team of astronauts to help find the extreme outliers in financial investigation data.

My favorite case study is about a UK bank that empowered the front office staff to make credit decisions. It asked, why not equip the customer-facing banker to make the credit decision that can be better for both the client and the bank? It makes sense, especially compared to the alternative of reducing the time it takes the risk department to process an internal request.

Why, then, are other banks not copying this approach? My guess: They applied the wrong skills to the job. The answer to too many databases isn’t to create another and call it an enterprise warehouse. The answer is a factory approach, as one insurance firm discovered when it separated risk models from the data, through the creation of a model factory. What do you know about your customer? Probably much less than this major insurer, since they now model customer behavior and analyze client perceptions to improve the customer experience and measure risk-weighted performance at a reduced cost.

Who should lead the digital transformation? It touches all aspects of the bank and needs a true champion at the top. The CEO should personally lead the way and change the bank — because the future of the bank is at stake.

And the best way for the CEO to lead the bank into the digital age is with a factory approach that automates, scales and manages data to support collaboration between departments and streamline analytics projects throughout the bank.

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Cybersecurity and the doomsday case for analytics

cybersecurity_imageTechnology has brought the world a great deal of good, but the downside is that we’re increasingly vulnerable to some seriously scary stuff:

  • Terrorists taking control of airplanes through the in-flight entertainment system.
  • Governments breaking into secure systems and stealing identities.
  • Thugs messing with the steering of self-driving cars.

When everything is connected, everything can be attacked. I’m talking about the unique brand of mayhem caused by the really bad guys – the kind of people who want to bring down a stock exchange, tamper with nuclear weapons or spoil a city’s water supply. If it sounds like the plot of a James Bond movie, it should, because cybercriminals can and do create chaos on a cinematic level.

Whenever I hear these stories, I have mixed emotions. As a citizen concerned with protecting everything I hold dear, I share the very same concerns that you do. But I also have a front-seat view of developments in the field of cybersecurity, so I understand the amazing power of analytics to address what is surely one of the most complicated computer science challenges of our times. And that makes me optimistic.

Cybersecurity to the rescue

The way that we will thwart the evil masterminds is analytics. We’ll fight technology with technology.

The key to winning is prevention. The nature of the cyberthreat means it’s no longer enough to reinforce the perimeter. Stopping data breaches means assuming that criminals are already inside. In this new reality, analytics serve not as a barricade to keep criminals out, but an alarm that sounds when the virus they’ve implanted awakes.

Today the combination of event stream processing, Hadoop, in-memory analytics and visual analytics make it possible to react in near real time, helping you spot the bad guys and foil their attempts. SAS has recently unveiled a cybersecurity solution to do just that, which will be available this fall. It works by searching hundreds of thousands of records per second and billions per day to spot the inevitable threats.

Plenty of companies try to build fences to keep people out, but we don’t do that. As soon as the virus wakes up and begins to do its thing, SAS will find it immediately and alert security teams – before the doomsday scenario plays out. The thing about villains is, they never seem to rest. They’re wily, they’re malicious, and they’re going to keep coming at us. The stakes are very high.

When it comes to foiling intricate plots, we’re going to need serious brainpower and cutting-edge analytics. That’s why I’m so passionate about bringing SAS’ expertise in cybersecurity to customers around the world.

Learn more

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