We'll learn how Digital Guardian in Waltham, Massachusetts analyzes both structured and unstructured data to predict and prevent loss of data and intellectual property (IP) with increased accuracy.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
To learn how data recognition technology supports network and endpoint forensic insights for enhanced security and control, we're joined by Marcus Brown, Vice President of Corporate Business Development for Digital Guardian. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: What are some of the major trends making DLP even more important, and even more effective?
Brown: Data protection has very much to come to the forefront in the last couple of years. Unfortunately, we wake up every morning and read in the newspapers, see on television, and hear on the radio a lot about data breaches. It’s pretty much every type of company, every type of organization, government organizations, etc., that’s being hit by this phenomenon at the moment.
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In terms of data protection, there are a couple of key trends at the cyber-security level. People have been aware of the so-called insider threat for a long time. This could be a disgruntled employee or it could be someone who has been recruited for monetary gain to help some organization get to your data. That’s a difficult one, because the insider has all the privilege and the visibility and knows where the data is. So, that’s not a good thing.
Then, you have employees, well-meaning employees, who just make mistakes. It happens to all of us. We touch something in Outlook, and we have a different email address than the one we were intending, and it goes out. The well-meaning employees, as well, are part of the insider threat.
Outside threats
What’s really escalated over the last couple of years are the advanced external attackers or the outside threat, as we call it. These are well-resourced, well-trained people from nation-states or criminal organizations trying to break in from the outside. They do that with malware or phishing campaigns.
About 70 percent of the attacks stop with the phishing campaign, when someone clicks on something that looked normal. Then, there's just general hacking, a lot of people getting in without malware at all. They just hack straight in using different techniques that don’t rely on malware.
People have become so good at developing malware and targeting malware at particular organizations, at particular types of data, that a lot of tools like antivirus and intrusion prevention just don’t work very well. The success rate is very low. So, there are new technologies that are better at detecting stuff at the perimeter and on the endpoint, but it’s a tough time.
There are internal and external attackers. A lot of people outside are ultimately after the two main types of data that companies have. One is a customer data, which is credit card numbers, healthcare information, and all that stuff. All of this can be sold on the black market per record for so-and-so many dollars. It’s a billion-dollar business. People are very motivated to do this.
In general, the world has become more mobile and spread out. There is no more perimeter to stop people from getting in. Everyone is everywhere, private life and work life is mixed, and you can access anything from anywhere. It’s a pretty big challenge.
Gardner: Even though there are so many different types of threats, internal, external, and so forth, one of the common things that we can do nowadays is get data to learn more about what we have as part of our inventory of important assets.
While we might not be able to seal off that perimeter, maybe we can limit the damage that takes place by early detection of problems. The earlier that an organization can detect that something is going on that shouldn’t be, the quicker they can come to the rescue. How does the instant analysis of data play a role in limiting negative outcomes?
Can't protect everything
Brown: If you want to protect something, you have to know it’s sensitive and that you want to protect it. You can’t protect everything. You're going to find which data is sensitive, and we're able to do that on-the-fly to recognize sensitive data and nonsensitive data. That’s a key part of the DLP puzzle, the data protection puzzle.
We work for some pretty large organizations, some of the largest companies and government organizations in the world, as well as lot of medium- and smaller-sized customers. Whatever it is we're trying to protect, personal information or indeed the IP, we need to be in the right place to see what people are doing with that data.
Our solution consists of two main types of agents. Some agents are on endpoint computers, which could be desktops or servers, Windows, Linux, and Macintosh. It’s a good place to be on the endpoint computer, because that’s where people, particularly the insider, come into play and start doing something with data. That’s where people work. That’s how they come into the network and it’s how they handle a business process.
So the challenge in DLP is to support the business process. Let people do with data what they need to do, but don’t let that data get out. The way to do that is to be in the right place. I already mentioned the endpoint agent, but we also have network agents, sensors, and appliances in the network that can look at data moving around.
The endpoint is really in the middle of the business process. Someone is working, they're working with different applications, getting data out of those applications, and they're doing whatever they need to do in their daily work. That’s where we sit, right in the middle of that, and we can see who the user is and what application they're working with it. It could be an engineer working with the computer-aided design (CAD) or the product lifecycle management (PLM) system developing some new automobile or whatever, and that’s a great place to be.
We rely very heavily on the HPE IDOL technology for helping us classify data. We use it particularly for structured data, anything like a credit card number, or alphanumeric data. It could be also free text about healthcare, patient information, and all this sort of stuff.
We use IDOL to help us scan documents. We can recognize regular expressions, that’s a credit card number type of thing, or Social Security. We can also recognize terminology. We rely on the fact that IDOL supports hundreds of languages and many different subject areas. So, using IDOL, we're able to recognize a whole lot of anything that’s written in textual language.
Our endpoint agent also has some of its own intelligence built in that we put on top of what we call contextual recognition or contextual classification. As I said, we see the customer list coming out of Salesforce.com or we see the jet fighter design coming out of the PLM system and we then tag that as well. We're using IDOL, we're using some of our technology, and we're using our vantage point on the endpoint being in the business process to figure out what the data is.
We call that data-in-use monitoring and, once we see something is sensitive, we put a tag on it, and that tag travels with the data no matter where it goes.
An interesting thing is that if you have someone making a mistake, an unintentional, good-willed employee, accidentally attaching the wrong doc to something that it goes out, obviously it will warn the user of that.
We can stop that
If you have someone who is very, very malicious and is trying to obfuscate what they're doing, we can see that as well. For example, taking a screenshot of some top-secret diagram, embedding that in a PowerPoint and then encrypting the PowerPoint, we're tagging those docs. Anything that results from IP or top-secret information, we keep tagging that. When the guy then goes to put it on a thumb drive, put it on Dropbox, or whatever, we see that and stop that.
So that’s still a part of the problem, but the two points are classify it, that’s what we rely on IDOL a lot for, and then stop it from going out, that’s what our agent is responsible for.
Gardner: Let’s talk a little bit about the results here, when behaviors, people and the organization are brought to bear together with technology, because it’s people, process and technology. When it becomes known in the organization that you can do this, I should think that that must be a fairly important step. How do we measure effectiveness when you start using a technology like Digital Guardian? Where does that become explained and known in the organization and what impact does that have?
Brown: Our whole approach is a risk-based approach and it’s based on visibility. You’ve got to be able to see the problem and then you can take steps and exercise control to stop the problems.
You have engineers putting the source code onto thumb drives. It could all be well-meaning, they want to work on it at home or whatever, or it could be some bad guy.
One the biggest points of risk in any company is when an employee resigns and decides to move on. A lot of our customers use the monitoring and the reporting we have at that time to actually sit down with the employee and say, "We noticed that you downloaded 2,000 files and put them on a thumb drive. We’d like you to sign this saying that you're going to give us that data back."
That’s a typical use case, and that’s the visibility you get. You turn it on and you suddenly see all these risks, hopefully, not too many, but a certain number of risks and then you decide what you're going to do about it. In some areas you might want to be very draconian and say, "I'm not going to allow this. I'm going to completely block this. There is no reason why you should put the jet fighter design up on Dropbox."
Gardner: That’s where the epoxy in the USB drives comes in.
Warning people
Brown: Pretty much. On the other hand, you don’t want to stop people using USB, because it’s about their productivity, etc. So, you might want to warn people, if you're putting some financial data on to a thumb drive, we're going to encrypt that so nothing can happen to it, but do you really want to do this? Is this approach appropriate? People get a feeling that they're being monitored and that the way they are acting maybe isn't according to company policy. So, they'll back out of it.
In a nutshell, you look at the status quo, you put some controls in place, and after those controls are in place, within the space of a week, you suddenly see the risk posture changing, getting better, and the incidence of these dangerous actions dropping dramatically.
Very quickly, you can measure the security return on investment (ROI) in terms of people’s behavior and what’s happening. Our customers use that a lot internally to justify what they're doing.
Generally, you can get rid of a very large amount of the risk, say 90 percent, with an initial pass, or initial first two passes of rules to say, we don’t want this, we don’t want that. Then, you're monitoring the status, and suddenly, new things will happen. People discover new ways of doing things, and then you’ve got to put some controls in place, but you're pretty quickly up into the 90 percent and then you fine-tuning to get those last little bits of risk out.
Gardner: Because organizations are becoming increasingly data-driven, they're getting information and insight across their systems and their applications. Now, you're providing them with another data set that they could use. Is there some way that organizations are beginning to assimilate and analyze multiple data sets including what Digital Guardian’s agents are providing them in order to have even better analytics on what’s going on or how to prevent unpleasant activities?
Brown: In this security world, you have the security operations center (SOC), which is kind of the nerve center where everything to do with security comes into play. The main piece of technology in that area is the security information and event management (SIEM) technology. The market leader is HPE’s ArcSight, and that’s really where all of the many tools that security organizations use come together in one console, where all of that information can be looked at in a central place and can also be correlated.
We provide a lot of really interesting information for the SIEM for the SOC. I already mentioned we're on the endpoint and the network, particularly on the endpoint. That’s a bit of a blind spot for a lot of security organizations. They're traditionally looking at firewalls, other network devices, and this kind of stuff.
We provide rich information about the user, about the data, what’s going on with the data, and what’s going on with the system on the endpoint. That’s key for detecting malware, etc. We have all this rich visibility on the endpoint and also from the network. We actually pre-correlate that. We have our own correlation rules. On the endpoint computer in real time, we're correlating stuff. All of that gets populated into ArcSight.
At the recent HPE Protect Show in National Harbor in September we showed the latest generation of our integration, which we're very excited about. We have a lot of ArcSight content, which helps people in the SOC leverage our data, and we gave a couple of presentations at the show on that.
Gardner: And is there a way to make this even more protected? I believe encryption could be brought to bear and it plays a role in how the SIEM can react and behave.
Seamless experience
Brown: We actually have a new partnership, related to HPE's acquisition of Voltage, which is a real leader in the e-mail security space. It’s all about applying encryption to messages and managing the keys and making that user experience very seamless and easy to use.
Adding to that, we're bundling up some of the classification functionality that we have in our network sensors. What we have is a combination between Digital Guardian Network, DOP, and the HPE Data Security Encryption solution, where an enterprise can define a whole bunch of rules based on templates.
We can say, "I need to comply with HIPAA," "I need to comply with PCI," or whatever standard it is. Digital Guardian on the network will automatically scan all the e-mail going out and automatically classify according to our rules which e-mails are sensitive and which attachments are sensitive. It then goes on to the HPE Data Security Solution where it gets encrypted automatically and then sent out.
It’s basically allowing corporations to apply standard set of policies, not relying on the user to say they need to encrypt this, not leaving it to the user’s judgment, but actually applying standard policies across the enterprise for all e-mail making sure they get encrypted. We are very excited about it.
Brown: Exactly.
Gardner: For those organizations that are increasingly trying to be data-driven, intelligent, taking advantage of the technologies and doing analysis in new interesting ways, what advice might you offer in the realm of security? Clearly, we’ve heard at various conferences and other places that security is, in a sense, the killer application of big-data analytics. If you're an organization seeking to be more data-driven, how can you best use that to improve your security posture?
Brown: The key, as far as we’re concerned, is that you have to watch your data, you have to understand your data, you need to collect information, and you need visibility of your data.
The other key point is that the security market has been shifting pretty dramatically from more of a network view much more toward the endpoint. I mentioned earlier that antivirus and some of these standard technologies on the endpoint aren't really cutting it anymore. So, it’s very important that you get visibility down at the endpoint and you need to see what users are doing, you need to understand what your systems are running, and you need to understand where your data is.
So collect that, get that visibility, and then leverage that visibility with analytics and tools so that you can profit from an automated kind of intelligence.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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