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A Unifying Viewpoint for Security
Automation of the easy stuff improves your information security efforts and makes the work that’s left far more interesting for those who do it.
Jesse Trucks
Minister of Magic, Splunk
hurdles that make information security especially
challenging. First, no matter what type of institution you’re in — small or large, public or private — you will always have more work to do than you have people to do it. Second, churn will set you back. The human element plays a bigger role in technology success than any of us on the vendor side likes to admit. When people leave, you have to hire anew, which, in universities, is commonly those in early career. Once they’ve learned the ropes, they’ll head off to somewhere else, often for more exciting work.
Automation of the easy security work — known threats, known responses, malware detection, cleanup — addresses both problems, and everybody wins. The campus gains better operational success. And when humans don’t have to intervene with the ordinary, they’re free to do more interesting work. They grow in their positions, because they’re not just clicking buttons all day.
Automation is especially important in an era of remote status quo and zero-trust. IT has to assume that there’s a high probability of any authentication request being nefarious. And that means being able to look at data in context: Is this person at a higher risk? Is the laptop or smartphone compromised? Should we let them on the network today? Have we scanned this device in the last three days? Then let’s not allow them access to this HR data. If they get their machine scanned, then they can come back and try again.
While higher ed has long been predicated on allowing open access, now that can only happen when it’s the appropriate thing to do. Users have to be classified
— student, researcher, staffer — and access has to be controlled. When everything looks normal, they get unfettered access. But when their machine or account is compromised, the access should be denied.
Easier said than done, right?
Complex Uses for Simple Data Analytics
Splunk started out as a data analytics platform for IT 6 | SPONSORED CONTENT
operations and software development that is really simple to use. Users can shove anything they want in without having to predefine how the data is structured. It serves as a unifying viewpoint. Once the data is in Splunk, users can do various searches and analytics. But what really distinguishes the program is that users can also change data definitions on the fly and have the change retroactively apply to anything they’ve ever loaded. Because the data isn’t structured in
a particular way, users aren’t limited by what they were thinking about the data at the time they loaded it.
What we as a company have learned is that our users are much more creative with uses for Splunk than we could ever have imagined. This data analytics platform for DevOps has found a ready application as a security information and event manager, handling all kinds of information security use cases — privileged user monitoring, detection of zero-day attacks, stopping data exfiltration, using DNS data to identify malware.
Splunkbase, our app exchange, offers more than 500 security applications from us, our many partners and the community at large, to extend the core functionality of Splunk and provide shortcuts for getting work done. That was the thinking for Phantom, a Splunk-created security automation and orchestration platform that integrates with existing security technologies in order to provide a layer of “connective tissue” between them.
Establishing a Security Nerve Center
Phantom streamlines security operations through the execution of digital “playbooks,” which enable IT to achieve in seconds what might take minutes or hours to accomplish with all of those security products they use every day. Phantom doesn’t replace existing security products; it makes the investment smarter and faster.
The program works like this: It gathers data from any and all sources, does deep analysis with predictive analytics, logs the results, and then transforms that information into actions it will perform. For example, based on the data it has

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