In this blog series, I will be discussing DFLabs IncMan management features to highlight the really powerful capabilities that have become available to IncMan users as part of our latest 188.8.131.52 SP release:
Today we focus on the creation of user groups. This useful feature allows the creation of groups of related users, for example, Tier 1 analysts or IT Operations teams. The benefit of this is that a defined group can be assigned specific tasks. This could be for a variety of different reasons:
- To assign a task or incident that require a specific skill set
- To assign task or incident to a specific stakeholder group for review or further investigation.
- To notify specific stakeholders about an incident or investigation
- To escalate an incident to the next tier
The Group functionality can be leveraged in many features across IncMan. We will now step through the process of adding a User Group.
- Let’s create a group. You will need administrator privileges and the required group creation permission to do this. Once you have verified this is the case, please head to User Management -> Groups
- In this section, you can view or modify existing groups and create additional groups of your own. Click the ‘+’ symbol above the user list to create a new group.
- Enter the name that you want to use to identify the Group. It is generally a good practice to assign the associated user profiles and general profiles to the group. For this example, we only need the group name, so please complete that.
- You will now be able to see the newly created group. You will also be presented with a number of additional options. For instance, adding users or editing the existing group information.
- Next, lets add users to the group that we have just created. You can select the users you wish to add to the group from the user list. If you have a lot of users, you can use the filter to quickly search for users. Then save and continue.
- Now that we have created our group and added our users we can begin assigning tasks to this group. Let’s head to an incident and into a playbook to start using this.
- Within the incident playbook, we can assign tasks to individual users. As you scroll down now, you will also notice that a new option is available, with the group name that we created.
- Having created our user group, we can assign Ownership and Authorization to our group instead of to a single user.
Last week, Anton Chuvakin from Gartner announced that Augusto Barros and himself are planning to conduct research in Q4 2017 on the topic of Security Orchestration, Automation and Response (SOAR), or Security Automation and Orchestration, depending on which analyst firms’ market designation you follow. At DFLabs we are very excited that Gartner is finally showing our market space some love and will be helping end users to better assess and differentiate SAO offerings.
Anton provided many questions that he wanted SAO vendors to prepare for. The questions immediately piqued our interest, with one question, in particular, standing out to us.
1.When is SOAR a MUST have technology? What has to be true about the organization to truly require SOAR? Why your best customer acquired the tools?
Anton also said that he had one main problem with Security Automation and Orchestration. In his own words, “For now, my main problem with SOAR (however you call those security orchestration and automation tools…if you say SOAPA or SAO we won’t hate you much) is that I have never (NEVER!) met anybody who thought “my SOAR is a MUST HAVE.”
The question is not entirely unwarranted. During my own time at Gartner covering the SOAR space, I spoke to many clients who were seeking an SAO solution without knowing that they were. Typical comments were, “I have too many alerts and false positives to be able to deal with them all”, or “We are struggling to hire enough skilled people to be able to respond to all of the incidents that we have to manage”. Another common comment was, “I am struggling to report operational performance to my executives?”. Often, these comments were followed by the question, “Do you know of any technology that can help?”.
Typically, these organizations had a mature security monitoring program, usually built around a SIEM. They often had critical drivers, such as regulatory requirements, or held sensitive customer data. We hear the same buying drivers from our own customer base.
To sum up the most common drivers for someone asking about Security Automation and Orchestration:
- A high volume of alerts and incidents and the challenge in managing them
- A large portfolio of diverse 3rd party security detection products resulting in a large volume of alerts
- Regulatory mandates for incident response and breach notification
- An overstretched security operations team
- Reporting risk and the operational performance of the CSIRT and SOC to an executive audience
One interesting thing is that when there is no external driver like regulatory compliance, deploying a Security Automation and Orchestration solution is often determined by maturity. Most organizations don’t realize that they will be unable to cope with the volume of alerts and the resulting alert fatigue until they have deployed a SIEM and a full advanced threat detection architecture.
The common misconception is that the SIEM can help to reduce the number of incoming alerts by applying correlation rules. This not entirely untrue, but correlation rules will only reduce a small percentage. They are essentially signature based. You need to know in advance what you want to correlate, and adding a correlation rule to cover all and every incoming alert is not a trivial task. Even with correlation rules, additional work will be required to qualify an incident. Gathering additional IoC’s, incident observables and context is still a very manual process. Lastly, detection is only one part of the entire incident response process – notifying stakeholders, gathering forensic evidence and threat containment will also have to be done manually. These are the areas where SAO solutions provide the greatest ROI – as a force multiplier.
Threat actors are increasingly adopting security automation and machine learning – security teams will have to follow suit, or risk falling behind.
Many organizations still conduct incident response based on manual processes. Many playbooks that we have seen in our customer base, for example, hand off to other stakeholders within the organization to wait for additional forensic data, and to execute remediation and containment actions.
While this may seem like good practice to avoid inadvertent negative consequences such as accidentally shutting down critical systems or locking out innocent users, it also means that many attacks are not contained in a sufficiently short time to avoid the worst of their consequences.
Manual Processes Cannot Compete with Automation
Reports are mounting about threat actors and hackers leveraging security automation and machine learning to increase the scale and volume, as well as the velocity of attacks. The implications for organizations should be cause for concern, considering that we have been challenged to effectively respond to less sophisticated attacks in the past.
Ransomware is a case in point. In its most simple form, a ransomware attack does not require the full cyber kill chain to be successful. A user receives an email attachment, executes it, the data is encrypted and the damage is done. At that point, incident response turns into disaster recovery.
Automated attacks have been with us for a long time. Worms and Autorooters have been around since the beginning of hacking, with WannaCry and its worming capability only the most recent example. But these have only automated some aspects of the attack, still permitting timely and successful threat containment further along the kill chain.
Threat actors have also leveraged automated command and control infrastructure for many years. DDoS Zombie Botnets, for example, are almost fully automated. To sum it up, the bad guys have automated, the defenders have not. Manual processes cannot compete with automation.
With the increase in the adoption of automation and machine learning by cyber criminals, enterprises will find that they will have to automate as well. The future mantra will be “Automate or Die”.
Making the Cure More Palatable Than the Disease
But automating containment actions is still a challenging topic. Here at DFLabs we still encounter a lot of resistance to the idea by our customers. Security teams understand that the escalating sophistication and velocity of cyber-attacks means that they must become more agile to rapidly respond to cyber incidents. But the risk of detrimentally impacting operations means that they are reluctant to do so, and rarely have the political backing and clout even if they want to.
Security teams will find themselves having to rationalize the automation of incident response to other stakeholders in their organization more and more in the future. This will require being able to build a business case to justify the risk of automating containment. They will have to explain why the cure is not worse than the disease.
There are three questions that are decisive in evaluating whether to automate containment actions:
- How reliable are the detection and identification?
- What is the potential detrimental impact if the automation goes wrong?
- What is the potential risk if this is not automated?
Our approach at DFLabs to this is to carefully evaluate what to automate, and how to do this safely. We support organizations in selectively applying automation through our R3 Rapid Response Runbooks. Incident Responders can apply dual-mode actions that combine manual, semi-automated and fully automated steps to provide granular control over what is automated. R3 Runbooks can also include conditional statements that apply full automation when it is safe to do so but request that a human vet’s the decision in critical environments or where it may have a detrimental impact on operational integrity.
Let me start by saying that total prevention is not attainable with today’s technology. Whether through negligence or ignorance, any data stored on a network is subject to unauthorized access by 3rd parties. Instead, we must combine Prevention with Detect and Respond. We know we are going to get breached, so we must focus on the how we deal with that.
One significant activity that can improve cyber incident response and enable the timely mitigation of threats is the transfer of knowledge after an incident as part of a formalized “Lessons Learned” phase of the incident response life cycle. Integrating successful processes and procedures from previously successful incident response activities can play a critical role in determining whether a business will suffer in terms of operational integrity, reputation and legal liability. A publicized security breach will lower customer confidence in the services offered by an organization as well as call into question the safety of their sensitive 3rd party information. This impacts a business credibility and translates directly into lost revenue.
In regulated industries, increased regulatory scrutiny is an additional consequence of a breach. This involves evaluating if the tools and procedures used in responding to security threats were sufficient. Integrating lessons learned into existing and future incident response playbooks ensures that the proper technologies and processes are deployed, and avoids accusations of gross negligence, expensive and time-consuming investigations and regulatory demands.
Procedural improvements can be incorporated into incident workflows via incident playbooks and ensure that all stages of the incident response process have been acknowledged and addressed. It also ensures that required security measures and procedures are documented and relevant stakeholders informed of their roles in case of an incident.
This process can be augmented through machine learning. Applying machine learning to this problem requires that all relevant data associated with incidents are analyzed and automatically applied to future incidents. DFLabs recently released DF-ARK machine learning capability to do precisely this. Our patent-pending Automated Responder Knowledge (DF-ARK) module applies machine learning to historical responses to threats and recommends relevant runbooks and paths of action to manage and mitigate them. DF-ARK requires sufficient training data – it begins with no knowledge, but learns from the experience and actions of your security team, becoming more effective over time. DF-ARK implements supervised case-based reasoning machine learning.
It also involves combining automated workflows and manual procedures to keep a human in the loop. This can be constantly improved by applying new observations and data, to fine tune existing methods and procedures identified in the lessons learned phase.
IncMan offers the R3 Rapid Response Runbook engine and Dual Mode playbooks to facilitate this. R3 Runbooks are created using a visual editor and support granular, stateful and conditional workflows to orchestrate and automate incident response activities such as incident triage, stakeholder notification, data and context enrichment and threat containment. Dual Mode Playbooks support manual, semi-automated and automated actions, meaning that users can automate the action without automating the decision.
Adding all of this together, here are 5 best practices for increasing the effectiveness of incident response via lessons learned:
- Encourage feedback from responders at every level. First, second and third line SOC operators and incident handlers each have a unique perspective that must be incorporated into future response playbooks.
- Review all relevant documentation to ensure compliance. This includes organizational policies or regulatory mandates to ensure any disparities are addressed in future playbooks.
- Chronicle any unanticipated or unusual events to extend procedures to mitigate similar occurrences in the future
- Annotate enhancements to existing processes that were identified during the incident response cycle.
- Designate a business unit or individual to be responsible for making necessary changes to existing playbooks, processes or procedures and to distribute these to stakeholders.
Capitalizing on lessons learned during incident response provides immediate and long-term benefits that contribute crucial time savings necessary to successfully mitigate future threats. Deploying a platform designed to facilitate the rapid inclusion of identified improvements to the incident workflow, such as DFLabs’ IncMan, can not only reduce the time it takes to fully investigate an incident but also reduces the overheads required to do so. If you want more information please contact us at DFLabs for a no obligation demonstration of exactly how we can improve your response time, workflows and remediation activities.