In Part 1, we explored how incident response (IR) is still in a primitive state. IR is primitive because it has not fully adopted knowledge-driven measures. Security professionals manually sift through disparate data sources, make decision slowly, and those decisions are limited. In this section, we’ll discuss the steps organizations can take to step into a new paradigm, a paradigm guided by intelligence and knowledge.
First, let us consider how incident response is presently conducted. Initially, events get coughed out of our systems, these alerts carry various pieces of information, and based on those variables, responders determine whether action should be taken. This seems pretty straightforward.
However, it is important to note that this process of decision making is a highly manual process. When we look closely at the process, we’ll find countless links in the decision chain that require an analyst and data scientist to “stitch” together each data element. I spoke about this in our 2017 Outlook.
But if organizations are to become knowledge-driven, it requires automating that stitching work and appropriating the decision aspects to humans. There are three essential qualities involved to automate stitching:
1) Natural Language Extraction
In the consumer world, we have Google. Since manually searching the web with accurate precision from page to page is a near impossible task, Google takes the work of mining the Internet and gives consumers a useful way to find anything. Google crawls billions of pages, indexing each page’s content, and intelligently presents those to the user. All I have to do is type in a few phrases and it brings back all the relevant content from the web. Secondly, further interaction with the results (along with millions of others) sees Google’s search function get tighter and tighter.
Security must take the same approach with Natural Language Extraction (NLE). Any solution that will automate the stitching process, must have an NLE function.
By deconstructing messages and finding the direct and implied content, data stitching becomes an automatic process. Crawling through datasets and identifying actions (e.g. allow, deny, block, fail), subjects (e.g. address, username), metadata (data about data), and nuanced expressions (e.g. direct objects, prepositions, and adjectives) all give rise to a coherent picture buried in mountains of security data. If the Internet can be tamed, security data can as well.
2) Associations and Clusters – Triage
Exhaustive search is not feasible, so we need to put infinite sets into “chunks.” Chunking creates associations and clusters – a triage. Triage can happen when Natural Language Extraction (NLE) reduces variables to discrete clusters based on associative relationships. This is analogous to healthcare triage. Imagine 500 patients dropped off at the emergency room at once, each with various presenting illnesses and symptoms. Based on the combination of discrete variables, medical staff can carve up the group into associations with each group receiving specific treatments.
However, this discrete and combinatorial process is taxing on human reasoning faculties. For a computer, it is inherent.
Continuous and routine processing of metadata is serial, systematic, deliberate, and often mundane. Humans get tired, lazy, and apathetic – computers do not. This combinatorial optimization is algorithmic in nature, and we humans cannot compare with computers for speed and accuracy.
By coalescing relationships based on any number of variables, incident responders can find the needle in a stack of needles. With associations, you can search for categories and their endless combinations with other categories (e.g. applications, geolocations, users, addresses, unknown entities).
The final element to automating the stitching process is collaboration. An integrated “social” framework enables operators to tag interesting data, inject context, and work together directly with machine and human generated data. This social framework produces continuously enriched data that becomes another variable for associative clustering. My interaction with an interesting event adds to your knowledge and your tagging a separate piece informs my investigations in turn. This ongoing recursive function gives more attribution to the data we’re each mining; delivering greater intelligence and crucially, providing context.
Collaboration can become a new factor for situational awareness – my work within the data becomes part of the data itself. Social media platforms have leveraged this, and now it’s possible for security. Facebook takes my activity, my interactions and connections, then has an uncanny ability to show me content that is likely to interest me. Using automated collaboration can do the same with security intelligence.
Incident response, as a discipline, is primitive because all the work that leads to a decision to take action (or not), investigate (or not) is done manually. Information is pulled, sliced, and pieced together by hand. Immediate Insight from FireMon eliminates that waste, taking data from separate sources and composing a coherent picture; giving responders the necessary information to make the right decision and avoid false positives.
Knowledge is often hidden in the data we collect. That data is unstructured, loose and disconnected. In order for incident response to mature, we need to automate the stitching process to help guide us away from making reactive and uninformed decisions to a knowledge- oriented future.
The fastest, easiest way to incident response maturity is Immediate Insight from FireMon. We invite you to see for yourself. Contact us to learn more >>