SIEM vs Search Engine

The pervasiveness of Google in the tech world has placed the search function in a central locus of our daily routine. Indeed many of the most popular apps we use every day are specialized forms of search. For example:

  • E-Mail is a search for incoming msgs; search by sender, by topic, by key phrase, by thread
  • Voice calling or texting is preceded by a search for a contact
  • Yelp is really searching for a restaurant
  • The browser address bar is in reality a search box

And the list goes on.

In the SIEM space, the rise of Splunk, especially when coupled with the promise of “big data”, has led to speculation that SIEM is going to be eclipsed by the search function. Let’s examine this a little more closely, especially from the viewpoint of an expert constrained Small Medium Enterprise (SME) where Data Scientists are not idling aplenty.

Big data and accompanying technologies are, at present, more developer level elements that require assembly with application code or intricate setup and configuration before they can be used by typical system administrators much less mid-level managers. To leverage the big-data value proposition of such platforms, the core skill required by such developers is thinking about distributed computing where the processing is performed in batches across multiple nodes. This is not a common skill set in the SME.

Assuming the assembly problem is somehow overcome, can you rejoice in your big-data-set and reduce the problems that SIEM solves to search queries? Well maybe, if you are a Data Scientist and know how to use advanced analytics. However, SIEM functions include things like detecting cyber-attacks, insider threats and operational conditions such as app errors – all pesky real-time requirements. Not quite so effective as a search on archived and indexed data of yesterday. So now the Data Scientist must also have infosec skills and understand the IT infrastructure.

You can probably appreciate that decent infosec skills such as network security, host security, data protection, security event interpretation, and attack vectors do not abound in the SME. There is no reason to think that the shortage of cyber-security professionals and the ultra-shortage of data scientists and experienced Big Data programmers will disappear anytime soon.

So how can an SME leverage the promise of big-data now? Well, frankly EventTracker has been grappling with the challenges of massive, diverse, fast data for many years before became popularly known as Big Data. In testing on COTS hardware, our recent 7.4 release showed up to a 450% increase in receiver/archiver performance over the previous 7.3 release on the same hardware. This is not an accident. We have been thinking and working on this problem continuously for the last 10 years. It’s what we do. This version also has advanced data-science methods built right in to the EventVault Explorer, our data-mart engine so that security analysts don’t need to be data scientists. Our behavior module incorporates data visualization capabilities to help users recognize hidden patterns and relations in the security data, the so-called “Jeopardy” problem wherein the answers are present in the data-set, the challenge is in asking the right questions.

Last but not the least, we recognize that notwithstanding all the chest-thumping above, many (most?) SMEs are so resource constrained that a disciplined SOC-style approach to log review and incident handling is out of reach. Thus we offer SIEM Simplified, a service where we do the heavy lifting leaving the remediation to you.

Search engines are no doubt a remarkably useful innovation that has transformed our approach to many problems. However, SIEM satisfies specific needs in today’s threat, compliance and operations environment that cannot be satisfied effectively or efficiently with a raw big-data platform.