One of the most pressing challenges facing cyber security professionals nowadays is probably the sheer number of security incident alerts, which is becoming too high to cope with even for the most expansive and well-equipped security teams. The increased number of alerts is a result of two factors at play, with the exponential boost in cyber attacks in recent years being the more obvious and straightforward one, the other is certainly much more complex and might also seem a bit ironic and surprising, as it arises from the growing use of different tools and devices within an organization, whose original function is to detect and mitigate incidents in the first place.
Security Operations Centers (SOCs) are now utilizing more devices designed to alert security analysts of cyber attacks than ever before, with the side-effect being too many alerts for the security teams to handle. Consequently, some of the most credible threats go by undetected or are simply not acted upon.
Addressing the Threat Noise Issue
With so many systems monitoring potential security threats and incidents creating alerts, and also taking into consideration that in many cases SOCs are severely understaffed, it comes as no surprise that analysts have a hard time staying on top of every single alert and responding to them appropriately and in a timely fashion. Since they don’t have the time or sufficient human resources to handle all alerts, SOCs often choose to disregard some and try to focus on those they deem to be credible, which understandably can lead to real threats slipping through the cracks and inflicting serious and irreparable damage to organizations.
In an effort to address the issue of threat noise, some SOCs opt for either reducing the number of devices generating alerts or expanding their number of staff, but while seemingly simple and straightforward, these options can be both counterproductive and quite costly. However, these are not the only possible solutions to this challenge standing at the disposal of SOCs, as there is another alternative, which would neither allow alerts to go undetected, nor require hiring additional security analysts.
Automating the Most Time-Consuming Parts of the Process
While the number of alerts generated by monitoring devices in some cases doesn’t necessarily have to be a reason for concern for SOCs in itself, the fact that alerts take a significant amount of time to analyze and handle efficiently often makes them an insurmountable challenge for understaffed security teams. One potentially very promising tactics to tackle this challenge effectively, is by enabling an automated response to some specific types of alerts, in an approach that is thought to be able to yield a wide range of benefits to organizations.
The idea is to automate the routine tasks that are repetitive and that do not require a lot of human expertise, but do usually take a lot of time to respond to and handle. By automating the response to these types of alerts, SOC analysts get more time to handle the alerts that pose a greater risk to their organizations, which must be analyzed in a more focused and comprehensive manner.
As noted in a recent SANS Spotlight paper titled “SOC Automation – Disaster or Deliverance”, written by Eric Cole: “The rate at which organizations are attacked is increasing, as is the speed at which those attacks compromise a network – and it is not possible for a human to keep up with the speed of a computer. The only way to beat a computer is with a computer”.
However, it must be noted that the implementation of incident response automation itself brings a certain degree of risk to organizations, as it might produce false positives, with analysts not being able to determine whether specific alerts are legitimate threats or not. This means that if automation is not properly implemented with predetermined processes and procedures in place, they may end up spending much of their time analyzing alerts that aren’t actual attacks and don’t pose any foreseeable danger. Having said that, organizations should not shy away from automation because of these potential drawbacks, but should instead implement it in a balanced and well thought out manner. The key is to manage and control false positives as oppose to simply eliminating them. It is therefore important to only automate the low-risk alerts that are not expected to have a major impact on an organization and leave the more serious threats to be handled by security professionals who can apply their expertise to resolve them.
When deciding whether to adopt automation or not, organizations need to be aware of its pros and cons, and if this assessment is carried out correctly, they will inevitably realize that the advantages of this approach clearly outweigh the disadvantages, that can also be easily controlled and managed to minimize any potential negative impact.
Looking at the pros and cons of automation, it’s easy to see that the most important benefit is the fact that it allows SOCs to monitor and analyze many more incidents than doing it manually, opening up the security team’s bandwidth to focus on the high-risk and high-impact alerts. Other key benefits also include: a more consistent response to alerts and tickets, a higher volume of ticket closure and response to incidents, as well as coverage of a larger area and larger number of tickets. On the other hand, automation can yield false positives that for their part can lead to directing time and resources towards resolving alerts that are not legitimate attacks, consequently leading to organizations potentially shutting down operations, having an impact on their business and their bottom line.
All said and done, automated incident response has the potential to bring significant benefits to organizations, provided that it’s implemented properly and cautiously, with a well-thought out strategy. Overall it should be a serious consideration for any SOC that has to handle large volumes of alerts on a daily basis.
We released our Machine Learning Engine PRISM in our most recent 4.2 release. The first capability that we developed from PRISM is our Automated Responder Knowledge (ARK). This capability will change the way incident responders and SOC analysts respond to incidents, and how they share and transfer their entire knowledge to the rest of the team. The key to this capability is that it learns from your own analyst’s responses to historical incidents to guide the response to new ones.
We are not re-inventing the wheel with this feature. SOC and Incident Response teams have been doing this the old-fashioned way for a long time – through 6-12 months training. What we’re doing is providing a GPS and Satellite Navigation, guiding the wheel and giving you different paths to choose from according to the terrain you are in.
We do this by analyzing incidents and their associated attributes and observables, to work out how closely they are related. Then we can suggest actions and playbooks based on your organizations’ historical responses to similar threats and incidents.
Using Automated Responder Knowledge (ARK) in IncMan
Step 1: Not really a step – as it’s done automatically by Automated Responder Knowledge (ARK), but this occurs in the background for every incoming incident. Every Incident possesses a feature space1 that contains all the information related to it, composed of every attribute, associated observable and attached evidence. ARK analyses the feature spaces associated with every incident ever resolved. When a new incident is opened, it is scored and ranked and then compared by ARK to the historical model to identify related incidents or actions based on similar and shared attributes. The weighting of the ranking can be customized by analysts.
Step 2: Open the incident, selecting the applicable incident type. To save time, you can create an incident template to prepopulate some of the contexts automatically in future.
Step 3: Select Playbooks, and PRISM.
In the next screen, you will see a variety of suggested related actions and related incidents based on the feature space that your incident type is matched with. The slider at the top is used to determine the weighting in ranking for actions that are suggested. For example, if I move the slider to the left, the entire feature space actions appear, then if I move the slider to the far-right only a few actions appear from highly ranked incidents.
Step 4: Determine which automation and actions you want to use from the suggestions. After saving, you will be presented with options such as Auto-Commit, Auto-Run, Skip Enrichment, Containment, Notification or Custom Actions. You have the ability to select only the actions you want to automate. If you are concerned about running containment automatically, for example, you just deselect those options.
Step 5: The automated actions are executed, resolving the incident, based on prior machine-learning generated automated responder knowledge.