Category: SIEM

Calculate and Configure Caches

Until someone invents a technology that guarantees one hundred percent uptime, we’ll need to accept that at some point in a SIEM environment there will an application or system failure. Additionally, we’ll need to take the application offline at least a few times per year for scheduled maintenance and upgrades. While most SIEM applications have caching capabilities built into them, it’s critical to ensure the environment has appropriate cache sizes configured and sufficient storage. Insufficient storage or misconfigured cache configurations can result in data loss.

Typically in SIEM environments, the Processing Layer (Connectors/Collectors/Parser Layer) is designed to send to the Analytics Layer via TCP, and if it’s unavailable, data will be cached locally on the Processing Layer servers until the Analytics Layer is available again. Thus, the Processing Layer servers will need sufficient local disk space to house the expected caches.

In order to determine what an appropriate cache size is, we need to look at your organization’s requirements, SLAs, and other factors that will help us determine how long an outage can last, and how long it typically takes to resolve issues within your IT department. If you’re certain an outage would last no longer than 3 days, then we need to ensure the Processing Layer servers can support 3 days’ worth of cached log data. Caches can also get large quickly as it’s typically raw, uncompressed data.

To calculate how much storage we’ll need for caching, we can simply take the Average Sustained 24h EPS rate, and then multiply it by the average event size and the amount of seconds per day. For example, if your Average Sustained 24h EPS is 5,000, and your normalized event size is 2,000 bytes, then we’ll need about 864 GB of space per day. So if we have 2 servers in the Processing Layer and we expect an outage to last no longer than 3 days, then we’ll need 1.3 TB of free storage per server to meeting the cache space requirements (864 GB/Day X 3 days = 2.6 TB, or 1.3TB across 2 Servers).

We’ll also need to ensure the application is configured to use the appropriate cache size as well. Many SIEM applications are configured with a default cache size, which may not be sufficient for your environment.

Log Source Verification

A critical function of any SIEM environment is verifying that the intended systems are logging successfully. Systems that are believed to be logging to a SIEM that aren’t pose a significant risk to an organization, creating a false sense of security and limiting the amount of data available for an investigation.

A common mistake that can be made while verifying if a particular system is logging to a SIEM is using the incorrect fields for confirmation. For example, most SIEMs have several IP address and hostname fields, ranging from source IP address, destination IP address, device IP address, and others. Given the multiple fields, it can be confusing to know which is used for what, especially for new staff. This leaves a possibility that staff are pulling incorrect data and providing inaccurate results when performing verification or searching in general.

As an example, Company A is implementing a new Linux server, and the SOC is being asked if they can see logs coming from it, 172.16.2.1. The SIEM application has three IP address fields: Device IP Address, Source IP Address, and Destination IP Address. The Device IP Address field contains the IP address of the server generating the log event. The Source IP Address is the source of the event, and the Destination IP Address field is the target of the event.

One of the SOC analysts performs the verification, searches for “172.16.2.1,” and gets one result:

Event Name: Accept
Source IP Address: 10.1.1.1
Source Port: 22
Device IP Address: 172.16.50.25
Destination IP Address: 172.16.2.1
Destination Port: 22

Without paying attention to the field names, the analyst mistakenly mentions that the new Linux server is logging, when in fact what he’s looking at is an accept traffic event generated from firewall 172.16.50.25, not an event from the new server. The project to implement the new server is now considered completed, and Company A now has a security gap.

While this can be a major issue and add risk to an organization, a simple process can be followed to show staff which fields to use for verification. Your SIEM vendor can also easily tell you which fields to use as well. Learning and education sessions on searching can also be used to address this and ensure staff know how to search effectively.

Alerting On Quiet Log Sources

Data sources that stop logging to your SIEM put your organization at risk. If one of your organization’s firewalls stops logging to the SIEM, your SOC will be blind to malicious traffic traversing it. If your endpoint protection application stops logging, your analysts won’t be able to see if malicious files are being executed on one of your billing servers.

In a perfect world, your SIEM should alert when any data source stops logging to your SIEM. While this is feasible in smaller organizations, it can become daunting in large organizations. It’s easier for your SOC to follow up with one system owner who sits a few cubicles over than with 100 system owners from different lines of businesses. The task of remediating several hundred systems not logging to a SIEM can easily consume an entire resource. In large organizations, network outages, system upgrades and maintenance windows can be a regular occurrence. Should you alert on any data source that stops logging to your SIEM in a predefined period, you could easily end up flooding your SOC, and in a worst case scenario, your analysts will develop a practice of ignoring these alerts.

As a best practice, especially in large organizations, a SIEM should be configured to alert when critical data sources stop logging. The data sources should at minimum include critical servers to the business (e.g. client-facing applications), firewalls, proxies, and security applications. A threshold of less than an hour in your organization may generate excessive alerts, as some sources that are file-based may be delayed by design, for example by copying the file to the SIEM every 30 minutes. However, data sources that haven’t logged in one hour may warrant an alert in your organization.

Another thing to consider when remediating systems not logging to the SIEM is that malware experts and threat intelligence specialists may not be the best resources to chase system owners down. While they may not mind the odd alert for this, they’re not likely going to have time to chase down and get 100 system owners to configure their systems properly, or have the patience to continuously follow up with them. Thus, in larger organizations, project management may be a good fit for this task.

Having all your systems log to your SIEM is a critical part of reducing your organization’s risk. Having a practical, manageable task for remediating systems that stop logging will ensure the process is followed and the risk is reduced.

Disable Unused Content

When building new SIEM environments or working with existing ones, one of the quickest ways you can improve the performance and stability of the environment is to remove unused content. While this may seem obvious to experienced SIEM resources, it’s common to find reports or rules running in the background that don’t serve a purpose. In some environments, unused content can be slowing the system down and contributing to application instability. Unused content is especially common in environments that don’t have enough staff to manage the SIEM.

Default rules provided by the vendor are often enabled but unused. The first indication that a rule is unused is if it doesn’t have an action or it isn’t used for informational purposes. If a rule isn’t alerted to the attention of an investigator or SIEM engineer, it may be that the rule is simply running in the background consuming system sources. A rule to trigger an alert when someone logs into the SIEM may be useful, but an ad-hoc report to obtain the same information may suffice. A significant amount of inefficient rules that match a large percentage of events can adversely affect the performance of the environment.

Reports can be another source of unused content. In many environments, I find reports that were originally setup to be used temporarily, but are no longer being used by the recipient. It’s often for the recipient to forget to follow up with the SIEM staff to note the reports are no longer required. Over a period of several years, this can easily amount to several dozen reports running on a regular basis, putting a significant strain on the system for no benefit.

All SIEM environments are different, and there’s no set of content that must be enabled or disabled. But there’s very likely content in your environment that can be disabled, and the system resources can instead be used to provide security analysts better search response times. So on a regular basis or whenever there’s a complaint about search response times or application instability, determine if there’s any content that can be safely disabled.

Effective Searching

There are two critical reasons end users should learn how to search their SIEM effectively. Ineffective searching is a risk to your organization, where end users can produce inaccurate data, and thus provide inaccurate investigation results. Ineffective searching can also degrade the SIEM’s performance, increasing the amount of time required for analysts to obtain data, while affecting the overall stability of the system.

If a security analyst is asked to perform an investigation and searches incorrectly, the results for a query on malicious traffic may return null when in fact there are matches. A compromised user account may be generating significant log data, but your analysts can’t obtain logs for it because they are searching for “jsmith” instead of a case-sensitive “JSmith.” End users can also match on incorrect fields, believing they are finding the correct data when they are not.

Ineffective queries can lengthen the amount of time required to complete them, and increase the system resources used by the SIEM. Many queries can improved to significantly increase their performance, making the end user happier with a faster response time, and a healthier system that has more CPU and RAM to work on other tasks. A simple rule of thumb is to match as early in the query as possible to limit the amount of data the system searches through. Searching for data in particular fields rather than searching all fields is also a way to reduce the amount of processing the system must do. Additionally, some SIEM tools allow you to easily check for poorly performing queries. For example, Splunk’s Search Job Inspector can not only show you which queries are taking the longest, but even which parts of the query are taking longer than others, allowing you to optimize accordingly.

It’s also common for security analysts to get requests for excessive data. In many cases requestors will ask for more information than is required in order to let them drill-down into the information they need, instead of having to submit multiple requests for data. For example, there may be a request to pull log data on a user for the past two months, when all that is required is some proxy traffic for a few days. These types of requests can be resource-intensive on SIEMs, especially if there are multiple queries running simultaneously. The impact can be more severe when the queries are scheduled reports. Scheduling multiple, large, inefficient queries on a regular basis can consume a significant amount of system resources. With a few inquiries to the requestor, the security analyst may be able to significantly reduce the amount of data searched for.

While ineffective searching is a risk, it’s a simple one to reduce. Training sessions, lunch-and-learns, or workshops can significantly reduce the risks of analysts searching incorrectly and consuming unnecessary system resources. I find a simple three-page deck can provide enough information to assist analysts with searching, highlighting the tool’s case sensitivity, common fields, and sample queries. Nearly all SIEM vendors offer complimentary documentation that will show you how to search best with their product. Thus, a few hours of effort can reduce searching risks while optimizing your SIEM environment.

SSISS: A SIEM Requirements Gathering Case Study

Streamlined Seamless Integrated Security Solutions (SSISS): A SIEM Requirements Gathering Case Study

Implementing a SIEM is a challenge for any organization. As SIEMs can take years to implement, it’s critical to build them appropriately, as even minor changes can result in months of re-work. SIEMs can be resource-intensive applications, reading and writing several thousand events per second, and thus an inadequate amount of RAM, CPU, or slow storage can mean poor performance and application instability. SIEM environments can have many stakeholders, so changing them can require approvals from many departments and subject matter experts within an organization.

To reduce risks associated with a SIEM implementation, a thorough requirements gathering exercise should be performed. We need to know who is going to be using the tool, how often it will be used, what type of data it will collect, how long the data will be stored for, and more. This will allow solution designers to best architect a solution, determine what product is most suitable for your organization, select an appropriate licensing model, and in general eliminate any assumptions made in the design. We also need to provide the requirements to the vendors in order for them to provide an accurate, high-performing solution.

One of the major things to keep in mind during a requirements gathering exercise is to challenge all requirements. If a requirement significantly raises the cost of the solution or makes it more difficult to maintain, then there should be a justification for it. Common misconceptions with using log data as legal evidence and data retention periods can make solution designers unnecessarily increase the complexity and cost of a SIEM. For example, some organizations mandate that log data must be retained for seven years, but this is typically a requirement for transactional data, not system log data.

Let’s dive into a sample requirements gathering exercise to highlight some of the major items we need to capture in order to design an appropriate solution. SSISS (Streamlined Seamless Integrated Security Solutions, not a real company to my knowledge, but what a name), one of the industry’s leading IT security companies, has hired me to assist one of their clients, Company A. Numerous problems exist within the environment and Company A isn’t sure where to even start.

The following Monday I arrive at Company A and meet with the VP of Security Operations. I learned that the SIEM was implemented a couple of years ago by a provider Company A is no longer doing business with. Complaints about the SIEM range from slow search response times, data loss, application instability, to audit deficiencies. The VP has budget to build a new SIEM, and doesn’t want to spend efforts on fixing the existing one. I ask the VP for all stakeholders and a point of contact within each group that I can work with. The stakeholders are the security operations team, network team, server team, and the compliance team.

I first meet with the security operations team. Their main complaints with the system are that it’s unstable, slow, and overall difficult to get work done. Searches and reports time out, and the system generally stops working at least once a week that forces a reboot. As I note the issues down, I ask if there’s anything else the system doesn’t do today. They reply that the data sources they need are there and they can create the required correlation rules, it’s just that the system is slow and unstable. Log data growth of 20% over the next 5 years seems reasonable, and they don’t currently forward any of the log data to another application, nor do they plan to. They agree to send me an email with their documented requirements.

Next is a session with the network team. They use the SIEM mainly to troubleshoot firewall and router issues, and note the searching is slow. They are mandated to log all of their devices to the SIEM, but there have been no integration issues to date. Log data growth of 20% over the next five years seems reasonable to them as well. I ask that they send me an email documenting what they need the SIEM to do along with the quantity of devices they intend to integrate.

I next meet with the server team. They generally don’t use the SIEM, but they are mandated to configure all servers to log to it. The only output they get from it is a monthly report to verify that all systems are logging. When asked about growth, they by chance give the same number used by the security operations and network teams. They agree to send me a note documenting the type and quantity of servers that they own.

Finally, a session with the compliance team lands me with some large requirements: seven years of log data and encryption of data at rest. I reply that most industry standards mandate one year of log data, and that seven is typically for transactional data, which is not stored in the SIEM. Additionally, there is no personally identifiable or financial data within the logs, so while many SIEMs offer masking capabilities, it can decrease performance. The compliance team replies that as long as there is no financial data, one year of data suffices, and access to data needs to be restricted, but not encrypted or masked. They agree to send me their documented requirements.

The next day, I receive responses from all teams. Everything looks okay with the security operations requirements:

I note that since we’ll be using Syslog for many data sources, the logs from the source to the Processing Layer will be unencrypted, but will be from the Processing Layer to the Analytics layer. The team confirms this will not be an issue. I also ask if there are any opportunities for filtering data, and discover the team doesn’t need any of the events I’m proposing to drop in the new solution. The team does a quick count and finds that dropping these events will reduce EPS rates by 20%. They also note that their current SIEM doesn’t do any aggregation.

The only additional request I have for them is to compile the distinct amount of systems logging to the SIEM, each device type’s average sustained and peak sustained EPS rates, and the total amount of correlation rules they intend to have in production. This will help me provide an accurate architecture and storage requirements.

Requirements for the network team look good with the quantity of network devices:

The server team requirements are documented as expected with the total quantity of servers:

Fortunately, the conversation with the compliance team worked in my favour, and the only requirement from them is to retain log data for one year.

The security operations team responds to my request a few days later, and I’m now able to populate my SIEM Architecture Sizing, Storage and Infrastructure Costs Calculator spreadsheet, which I’ll be sending to each vendor.

The first tab lists the data source requirements:

The two most important numbers in the above table are the Total Average Sustained 24h EPS (events per second) rate, which is the total amount of events received in a day divided by the amount of seconds in a day, and the Peak Sustained EPS rate, which is the maximum amount of EPS processed by the system in a day, typically during business hours. The Total Average Sustained 24h EPS rate will be used to determine storage requirements and licensing costs depending on the product licensing model, while the Peak Sustained EPS rate will be used more to size an appropriate architecture. One of the biggest mistakes many SIEM consultants commit is using the sustained EPS rates to size an architecture. This results in the system being undersized during peak hours. For example, proxy traffic at night can be a mere fraction of what it is during the day. Thus, an architecture designed using the sustained rate will cause the system to slow down and cache data during the day, when it’s dealing with 10,000 peak EPS rather than the sustained 1,500 EPS it was designed for.

The second table lists the functional requirements (what the system must do):

The third table lists the Environment Parameters, which are other design requirements that need to be built into the architecture.

The first item is the Processing Layer Forwarding Factor, which is how many copies of each event the Connectors/Collectors/Forwarders will forward to the Analytics Layer. Many SIEM products forward a second copy of each event to two destinations to make the solution highly available. If I want a highly available solution, I’m going to need to enter a value of two or greater. The second item is the Analytics Layer Replication Factor, which will indicate how many copies of each event will be copied to another server for high availability. Given that these two items can vary per SIEM product, I’m going to let the vendor enter these numbers.

The third item is the Analytics Layer Forwarding Factor, which indicates how many systems the Analytics Layer will need to forward to. As confirmed by the Security Operations team, we will not be forwarding data from the SIEM to another system. This is a critical design consideration, as missing this requirement can cause the architecture to be severely undersized.

The fourth item, Filtering Benefit, will reduce the amount of data sent from the Processing Layer to the Analytics layer by the entered percentage. I’m going to enter a value of 20%.

The fifth item, Aggregation Benefit, will depend if the SIEM product can aggregate. I’ll leave that value for the vendor as well.

The Processing Layer Spike Buffer and Analytics Layer Spike Buffer are designed to prevent caches from being formed when there are surges of log data. Why this is an important consideration is detailed in the article A SIEM Odyssey: How Albert Einstein Would Have Designed Your SIEM Architecture. I’m going to use a value of 25% for the Processing Layer and 15% for the Analytics Layer.

Finally, as discussed with many Company A teams, log data growth of 20% will be assumed.

After all the parameters are input, I get the following table from the Architecture Requirements tab. The two key values are the Total Processing Layer EPS Requirement and the Total Analytics Layer EPS Requirement. The SIEM architecture will need to be sized based on these numbers. However, these values may change per vendor depending on how their product works.

I’m now ready to send the SIEM Architecture Sizing and Storage Costs Calculator spreadsheet to the three shortlisted vendors.

While we should be focused on meeting Company A’s requirements, we shouldn’t forget to be equally focused on meeting the vendor’s requirements. We need to ensure the proper system requirements are met, including CPU, RAM, and storage. Failure to meet these requirements can result in numerous complications, including reduced search response times, application instability, data loss, operational nightmares, and ultimately increased risk to the organization.

The first vendor to respond is Vendor 1. Their SIEM product doesn’t structure logs during ingestion, doesn’t have the ability to aggregate data, but retaining the logs in raw format should keep the message size low at 700 bytes. Their product provides high availability by replicating data across multiple Analytics Layer servers. For the Non-HA solution, the Processing Layer will need to process 15,000 EPS, while the Analytics Layer will process 11,000 EPS. The average event size of 700 bytes produces offline storage costs of $21,300/year, while the online storage will be provided by the local disks on the server. For the HA solution, the Processing Layer requirement stays the same, but Analytics Layer Requirement jumps to 22,000 EPS, and storage requirements double to $42,600/year.

Vendor 1 recommends four servers in the Processing Layer, which is more than enough to handle the anticipated 15,000 EPS. This should prevent any caching or log data delays when there’s a surge of log data. For a highly available Processing Layer, they recommend to double the amount of Processing Layer servers, but note that if one of the servers is unavailable, the remaining three should still be more than sufficient to handle the 15,000 EPS. They also note a load balancer can be used in front of the Processor servers to provide balanced traffic and high availability. The 500GB of local storage on each server should provide 3.5 days of cached data in the event the Analytics Layer is unavailable, but can be bumped up if desired.

For the Analytics Layer, Vendor 1 recommends a large physical server with 48 cores, 256GB of RAM, and highly recommends a combination of solid state and local hard disks for the storage medium. Should all the server requirements be met, they have no concerns with meeting the search speed and correlation rule requirements. And to meet the high availability requirement, a second server can be added, and the primary Analytics server will replicate a copy of each event to the secondary server. Vendor 1 can also provide the entire solution as a cloud-based service.

The licensing model for Vendor 1 is based solely on stored GB per day. You can add as many servers or end users as needed without any additional costs.

Vendor 2 responds next, which provides a SIEM product that structures data as it’s ingested, can provide significant aggregation benefits, but will produce an average event size of 2,000 bytes. The product can provide high-availability by the Processing Layer sending a copy of each event to another destination, commonly known as “dual-feeding.” For the non-HA environment, the Processing Layer will need to process 15,000 EPS, but given the product’s aggregation functionality, the Analytics Layer will only need to process 5,500 EPS. However, the average event size of 2,000 bytes will bump up the offline storage costs to $30,400/year. For the HA solution, the Processing Layer requirement doubles to 30,000 EPS, Analytics Layer requirement to 11,000 EPS, and storage costs to $60,800/year.

For the non-HA solution, Vendor 2 claims four servers in the Processing Layer will be more than adequate to handle the expected 15,000 EPS, and can be augmented with a load balancer and additional servers for better performance and high availability.

The Analytics Layer is a single server with significant resources. Vendor 2 claims it should provide all required functionality and as well provide fast search response times for end users. To make the solution highly available we can simply add another server and configure the Processing Layer to “dual-feed.”

Vendor 2’s licensing model is a combination of CPU cores, peak EPS rates, and the amount of end users logged in simultaneously.

Last in line is Vendor 3, who provides an appliance-only SIEM solution that structures data as it’s ingested, can provide aggregation benefits, and produces the highest event size of 2,300 bytes due to the large field set the product uses. Like Vendor 2, they also anticipate an aggregation benefit of 50%, which will produce a non-HA Processing Layer requirement of 15,000 EPS and Analytics Layer requirement of 5,500 EPS, but the increased message size will bump up offline storage costs to $35,000/year. For HA, the values can simply be doubled.

Vendor 3 only proposed two servers in the Processing Layer, which they are certain can handle the proposed EPS rates, and can be scaled horizontally for high availability.

The Analytics layer is a single appliance the vendor claims can meet all the searching, reporting and analytics requirements. The high availability architecture is similar to Vendor 2, where a second Analytics Layer server can be added and the Processing Layer can be configured to “dual-feed.”

The licensing model is simply based on the average daily EPS rates on the Analytics Layer. Vendor 3 supports the entire appliance, from the hardware, OS, to the application, and issues patches for all of it. Patches and upgrades seem simple with the single files the vendor provides that can be uploaded at the click of a button.

The appliance-based solution sticks out in my mind, given the statements the VP of SecOps made about the revamping of the server teams. Previously, server issues could take months to resolve, and patching was practically non-existent due to the shortage of resources within the team. Additionally, there have been cases where the server resources promised were not delivered.

While I haven’t discussed cloud options with the VP of SecOps, I’m going to follow up with him to see if this will fit into his roadmap and verify if sufficient bandwidth is available to an external site. Vendor 1’s track record of managed Cloud SIEM environments is also impressive and will likely be the quickest to deploy. I’m also going to confirm that there are no other stakeholders that we missed, such as data scientists, to ensure the solution can handle any mega calculations if necessary.

All solutions appear to meet Company A’s requirements, so I’ve got some more work to do to see which will be the best fit. I like Vendor 1’s simple licensing model, architecture, low storage costs, and think the cloud solution may be the best bet for Company A. I like Vendor 2’s blazing fast searches, as there was nothing that frustrated me more as an analyst than slow search response times, making a simple investigation take hours. However, I’m confused with their licensing model, and I’ll have to follow up with them to understand it better. I like Vendor 3’s solution as well, similar to Vendor 2 but appliance-based with a simpler license model, but I’m not sure two servers in the Processing Layer will suffice.

When I look at the operational costs of all vendors, Vendor 1 produces the lowest storage costs. Vendor 3 produces the lowest support cost, but that’s due to the two servers in the Processing Layer compared to the four for the other vendors, which again I’m not convinced will be sufficient.

As you can see, selecting a SIEM product for your organization can be a complex task. There are many high-quality SIEM products on the market, but depending on your organization and how it operates, one product may be more suitable than another, and the cost can differ significantly. Minor functionality from how the solution will process the required data sources to the amount supported parsers can drastically make one solution more feasible than others.

A thorough requirements gathering exercise can reduce the risks associated with a SIEM implementation. It will help your organization select a product that maps best to your requirements. It will allow vendors to provide an accurate, high-performing solution. Ultimately, it will pave the way for an efficient and effective solution that lowers implementation and operational costs, reduces waste and rework, and provides end users with a tool that increases your organization’s security posture while providing business value.

What is SIEM and how it differs from other security tools

Now that we understand what log data is, let’s discuss the technology that will allow your organization to collect and use it.

Security Information and Event Management is a technology that will process log data from your various systems, analyze it, make it available for searching, and store it. SIEM itself is a combination of two more abbreviations: Security Information Management (SIM) and Security Event Management (SEM). SIM is focused on the collection of log data for investigative and compliance purposes. SEM is focused on alerting and analytics: threat detection, pattern anomalies, and correlating different data sources.

SIEM tools can vary in architecture, but generally have two layers: A Processing Layer and an Analytics Layer. The Processing Layer is where data is structured, aggregated, and forwarded to the Analytics Layer. The Analytics Layer is where data is stored, made available for searching, and where security analytics is performed.

Using the above diagram as an example, the data sources are your various systems that will produce log data and send (push) to your Processing Layer, or your Processing Layer will reach out to (pull) via a database or API call. Depending on the SIEM product, the Processing Layer will structure the log data, normalizing it into a standard format, aggregate it by combining similar events into one, or may simply add an index to it and forward to the Analytics Layer. The Processing Layer is strictly used for processing; the only data that is typically retained are caches when the Analytics Layer is unavailable.

The Analytics Layer is where end users will search for data, create reports and use cases. Depending on the SIEM product, it may structure log data, and may act as a long-term retention repository.

While SIEM is defined as a security application, it differs significantly from your other security tools. Your SIEM will process log data, while your proxy, IDS/IPS, and some malware detection tools will typically process network traffic. Packets going over your network use the same protocols, so you don’t need to customize your firewall or IPS to detect TCP traffic. Your SIEM will need to process log data in various formats, many which may not be supported by your SIEM vendor.

Your IDS/IPS and malware tools provide you with a list of signatures that will be automatically updated on a regular basis. Some IDS/IPS tools allow you to implement custom signatures, but for the most part your analysts won’t have to write custom signatures for known vulnerabilities, exploits, and attacks. Your SIEM staff will need to create custom use cases and update them regularly, as you’ll unlikely be using much of your SIEM vendor’s default content.

SIEM vendors support many log sources, but your engineers will need to ensure the right parsers are being used, update them regularly, and write any that are not supported by your SIEM vendor. This is in stark contrast to your network devices and other security tools that only have to work with limited protocols such as TCP/IP, HTTP, and HTTPS.

Staff will be logging into your proxy, firewall, IDS/IPS, and malware tools often, but it will mostly be for administrative purposes. Your SIEM will have many end users, ranging from admins to users searching for data. In large environments, it’s common to have several users searching for data simultaneously. For MSPs, your customers may be logging in to search for data as well.

While most of your other security tools can block a malicious host from egressing your network or block users going to an uncategorized site, SIEMs don’t have the capability to block.

The following table summarizes the differences between SIEM and your other security products.

As you can see from the above table, a SIEM differs significantly from your black-boxed IPS or malware tools. While it may seem that it’s simply a log aggregator, a SIEM is a complex tool that will need significant customization. The environment can have many stakeholders from security analysts, to compliance and access management teams. Ultimately, it will need to be implemented, operated, and maintained differently.

What’s the big deal with log data?

So what’s the big deal with all this log data, and why on earth should I spend a large chunk of my budget to collect it? Aren’t the other security tools I have good enough? What exactly is in all this log data, anyway?

Log data is one of today’s most valuable assets: data. Google, Twitter and Facebook collect enough data on people to detect flu outbreaks faster than medical professionals can. Without owning a single taxi, GPS data gave a software company the opportunity to become the world’s largest taxi service. A computer algorithm can recommend a movie you’d like to watch and spare you from having to read reviews of movie critics. Amazon can tell you what book you’d like to read next or what household products you may be running low on.

In the context of cyber security, log data contains records of activity from your various IT systems. These records can help you understand what goes on inside your network. They can show you which user accounts are being used. They can show you which users are consistently visiting blocked websites. They can show you the suspicious files being blocked by your endpoint protection application. They can highlight suspicious processes running on your servers. They can tell you which exploits your web servers are vulnerable to and if anyone is trying to attack them. Ultimately, they can uncover activity in your network that is adding risk to your organization.

Log data is typically output to a file or database, where it was traditionally used for troubleshooting purposes. If someone couldn’t log into a particular application, the system admins would check the log files to see if they could find out why. If a customer application was down, the support team would check the log files to see if they could find out the cause of the crash.

As the amount of log data grew, many saw that the files sitting on their servers contained invaluable data. Many applications were born to manage all of this data, helping organizations search through it and assist in detecting issues before they became outages. In the early 2000’s, some programmers with a security mindset thought of creating an application that would act as a centralized repository of log data for security investigators, and be able to alert in real-time when particular values or suspicious patterns were detected in the log data. The result of this was the birth of SIEM, Security Information and Event Management.

Let’s take a quick peek at some log data. Here’s a small sample of authentication activity, which is a user failing to login, and then successfully logging into their workstation.

-May 1 2018 1:00PM, IP=10.1.1.1, User=Bob, Message=login failure
-May 1 2018 1:01PM, IP=10.1.1.1, User=Bob, Message=login failure
-May 1 2018 1:02PM, IP=10.1.1.1, User=Bob, Message=login success

Most log files will at minimum answer who, what, when, where, why, and how. Given the advent of SIEMs, most vendors now provide detailed logging for their applications, and some even allow you to customize what is output.

Here you can see a couple of punctual users logging into their company network in the morning, generating VPN login data:

-May 1 2018 8:50AM, IP=23.91.128.44, User=John, message=VPN Login Success
-May 1 2018 8:54AM, IP=23.95.148.12, User=Bob, message=VPN Login Success

Log files can also be specific to an application. Here we have some startup activity on the billing server:

-May 1 2018 9:54AM, hostname=billingserver01, message:NOTICE: Application starting
-May 1 2018 9:55AM, hostname=billingserver01, message:NOTICE: Running startup scripts

That’s great, you may think, but why should you devote resources to collect and manage this data? Let’s expand the above entries and see what the big deal is.

Using the authentication activity again:

-May 1 2018 1:00PM, IP=10.1.1.1, User=asmith, Message=login failure
-May 1 2018 1:01PM, IP=10.1.1.1, User=bsmith, Message=login failure
-May 1 2018 1:02PM, IP=10.1.1.1, User=csmith, Message=login failure
-May 1 2018 1:03PM, IP=10.1.1.1, User=dsmith, Message=login failure
-May 1 2018 1:04PM, IP=10.1.1.1, User=esmith, Message=login failure
-May 1 2018 1:05PM, IP=10.1.1.1, User=fsmith, Message=login failure
-May 1 2018 1:06PM, IP=10.1.1.1, User=gsmith, Message=login failure
-May 1 2018 1:07PM, IP=10.1.1.1, User=hsmith, Message=login failure

These log entries become interesting now that someone is trying to log into the billing server using an incremental version of “smith.” This small story could be many things, from a developer testing something, a script running in the background, or it could be indicative of someone trying to guess a username, attempting to gain unauthorized access to the server.

Let’s take a look at the VPN log again:

-May 1 2018 8:50AM, IP=23.91.128.44, User=Bob, message=VPN Login Success
-May 8 2018 8:55AM, IP=23.91.128.44, User=Bob, message=VPN Login Success
-May 15 2018 8:52AM, IP=23.91.128.44, User=Bob, message=VPN Login Success
-May 22 2018 8:59AM, IP=23.91.128.44, User=Bob, message=VPN Login Success
-May 29 2018 8:44AM, IP=23.91.128.44, User=Bob, message=VPN Login Success
-May 29 2018 9:30PM, IP=62.176.64.51, User=Bob, message=VPN Login Success

Nothing unusual about Bob being his punctual self logging into work, except that “he” logged in from Bulgaria at about 9:30PM on May 29. Scenarios like this could be John on a business trip, or not John at all.

Finally, let’s take a look at some file executions in a log file. Here is a sample system updating itself, but for some reason the last file executed doesn’t seem to be a standard update file, which could be indicative of a malicious file being executed.

-May 4 2018 1:10AM, hostname=billingserver01, msg=file “update_01.exe” executed
-May 4 2018 1:13AM, hostname=billingserver01, msg =file “update_02.exe” executed
-May 4 2018 1:15AM, hostname=billingserver01, msg =file “update_03.exe” executed
-May 4 2018 1:50AM, hostname=billingserver01, msg =file “A2.exe” executed

As you can see, log data can contain invaluable data that can help your organization investigate suspicious activity and detect attacks in real time. Log data can indicate issues brewing in your systems that can be caught in advance before an outage or breach occurs. SIEM is a technology that centralizes log data, makes it available for searching, allows staff to alert on suspicious activity, and ultimately enhance the efficiency and effectiveness of your organization’s security operations.

If Milton Friedman Created Your SIEM Team

When you mix an economist with the Godfather, you get an offer you can’t understand. But when you mix the philosophy of a famous economist with your SIEM team, you can create a high-performing team that continuously improves the environment, plans accordingly, creates better use cases, and ultimately reduces the probability of your phone ringing on a Friday afternoon for a SIEM issue.

Milton Friedman was one of the 20th Century’s most influential economists. Without going into detail or starting a debate on economic policy, he argued that a single owner would take better care of something than multiple entities or an unclear entity. The single owner likely has a direct interest in the value of it and will maintain it better than an entity that doesn’t. And thus his famous quote:

“When everybody owns something, nobody owns it, and nobody has a direct interest in maintaining or improving its condition.”
– Milton Friedman

A SIEM is likely one of your more complicated security products to manage, and needs extensive customization over the other black-boxed security applications your vendors manage for you. Not only do you need to manage the content and use cases, you need to manage the data feeds, ensure data is parsing correctly, troubleshoot issues with the application, support SIEM end-users, and plan for growth. All this effort requires input from various teams within your organization. Given the multiple teams involved, it’s critical to establish accountability and know who is responsible for what part of the environment.

SIEM Environment Requirements

The first requirement of any SIEM solution is clear, single ownership; an entity that has a direct interest in improving and maintaining the overall SIEM environment, and is ultimately accountable for its entire operation. Without clear ownership, staff and end users will be discouraged from escalating issues. Teams will not have a dispute mechanism, and instead of resolving issues, they will point the finger at each other. Those issues will then be brushed under the rug, and will result in a major outage or security issue down the road for leadership to deal with. Work will not be distributed accordingly, and highly-skilled staff that are overworked will leave, taking valuable knowledge and training investments with them. Relationships between the teams will be strained, and ultimately entropy will overrun your environment, in which significant investment will need to be made in order to turn it around.

The second requirement of a SIEM solution is a healthy, teamwork-oriented environment. Given that many teams are involved in the implementation and operation of your organization’s SIEM, positive and open communication between the teams is required for issues to be raised, work to be assigned to the appropriate teams, and for knowledge to be shared. Healthy teams will raise pertinent issues to leadership and resolve them quicker than teams that don’t. Healthy teams share valuable knowledge and train each other. All of this contributes to a work environment that retains staff, and attracts new talent into the team.

The third requirement of a SIEM solution is a strong skillset. SIEM environments are complicated, and you’ll need many skills to manage it from architecture and design planning, parser development, rule logic development, to social skills required to obtain and maintain data from other teams. Before making investments in your SIEM skillset, the first two requirements should be met, or else you risk losing highly skilled staff that are hard to find and retain.

The fourth requirement of a SIEM solution is documented roles and responsibilities. Many mistake this as the first requirement, but a RACI, for example, will not be followed or enforced if the first three requirements are not met. If your staff don’t have the proper skillset, one or two employees may end up doing everyone else’s work, and leave when they burn out. If your teams have poor communication with each other, issues may end up going unresolved and unnoticed by leadership, leading to an outage down the road.

Where practical, entire SIEM teams should be under one VP or line of business. Having one VP accountable for the implementation and operation of your SIEM gives the VP incentive to ensure the solution isn’t rushed into production, and that it has adequate resources for operations. The single VP will have more of an incentive to ensure the health of the SIEM environment than another organization that makes one VP accountable for the implementation only, and another VP for the support of it. Such a situation can incentivize the implementer VP to get it in as soon and cheap as possible and leave the support VP with a mess. Given that SIEMs can take years to fully implement, this should be avoided at all costs. The single VP also acts as a single escalation point and can’t deflect the issue to another VP or line of business. When there are 2 VPs and the roles and responsibilities aren’t clear, disputes can arise or the issue can be ignored. Again, it’s ideal to have your entire SIEM environment under a single VP, but in organizations with a good working environment, different parts of it owned by different VPs or lines of business can work out well. There are also some roles and responsibilities, such as server and storage administration, that are common to be outside of your security organization.

RACI Matrix Overview

One of the industry’s most common roles and responsibilities document is a RACI Matrix, which stands for Responsible, Accountable, Consulted, and Informed. The goal of a RACI is to list all stakeholders involved in the solution and the required tasks, and then assign one of the following values to a stakeholder(s) for each task.

While a RACI is designed to document roles and responsibilities, it has another valuable benefit: quantifying work efforts. Once you see all the various tasks involved in your SIEM environment, you can see how much work effort the various stakeholders are assigned. For example, if Engineering is responsible for Parser Management, and they spend 20 hours per week maintaining the 40 custom parsers, they can justify the half of an FTE they’re requesting.

It’s easy for a SIEM RACI to span several hundred lines given the amount of tasks and teams involved, and I’d thus recommend to keep it as high level as possible. The objective should be to assign tasks to the teams, and then leave the teams responsible for figuring out how work is managed. This avoids the SIEM Owner having to resolve disputes within teams. The SIEM owner should have a single point of contact within each of the teams to work with directly.

A SIEM environment should have at minimum an overall RACI that defines the roles and responsibilities of all stakeholders. Additionally, each team may want to create an internal RACI that clarifies who within the team is doing what. This is optional, but highly recommended, as it can help employees understand their tasks, assist management in understanding the required tasks and work efforts, and most important establishes accountability. For example, if you have 100 correlation rules and leave it up to “the team” to manage it, you may find that the task of keeping the rules relevant is being ignored. When you break up the rules, the first 40 to be “owned” by Bill, the next 40 “owned” by Bob, and the final 20 to be “owned” by Joe, who also gets to own reporting, you may find rule updates happening more frequently. There is accountability and you can follow up with Bill, Bob, and Joe to check the status of the tasks. If there isn’t progress, you can further narrow down the issue, whether it’s a skillset gap or work overload, and then coach the employee accordingly.

Many argue that assigning work to an individual rather than a team introduces a skillset gap when that employee leaves. The advantages of assigning it to an individual are a better understanding of the task via specialization and repetition, better documentation of the task as a result of the understanding, and ultimately a position for the individual to improve the condition of the SIEM relating to the task, for example correlation rule updates. Having a group manage something that is not well understood leads to the team ignoring the task, something they can do when no one is accountable for it. A group that doesn’t understand the task cannot document it properly or improve its condition. There’s nothing more frustrating working on something you don’t understand.

An overall RACI is a requirement for any SIEM environment, but as all organizations are different, how a team manages tasks within itself should be at the discretion of leadership.

Sample SIEM RACI

We’ll walk through a sample SIEM RACI to give you an idea on what it may look like in your organization. The RACI will be divided into subsections below by Category and commented on individually. A link to the full RACI Matrix is available at the end of the article.

The Stakeholders in this sample RACI are the SIEM Owner, Engineering, the Content Team, and Incident Response, who all fall under the Security Operations team. The Server Support and Storage Support teams fall under a different line of business, Infrastructure Services.

The first Category is Governance, and you can clearly see how the SIEM Owner is both Accountable and Responsible for the overall SIEM solution, dealing with the vendor, and internal escalations from any stakeholders.

The second Category is Architecture and Design, in which the SIEM Owner is also Accountable and Responsible, but Consults the Engineering, Content, and Incident Response teams. The SIEM Owner needs to work with them to make sure their requirements are met, the search speeds are adequate, the required data sources are available, and that the SIEM solution adequately meets all these requirements, and if not, are built into future versions.

For the Logging Configuration category, the SIEM Owner needs to make sure not only are the required log sources logging to the SIEM, but that they are logging the correct data. Engineering needs to be Consulted to ensure correct parsing, and the Content and Incident Response teams need to make sure the data they need is available within the logs.

The SIEM Owner is also Accountable and Responsible for leading all new projects, and ensuring the SIEM solution is compliant with the organization’s compliance and governance standards. You can also see at this point the SIEM Owner isn’t a mere decision maker; he or she will be active in the management of your company’s SIEM.

The Engineers are Accountable and Responsible for the health and stability of the SIEM solution, and to ensure data feeds are integrated into the SIEM correctly. They do everything from application support to patching. The only two support-related tasks that they are not Accountable and Responsible for are Server and Storage Support, but will be Consulted when necessary.

The Content Team are the SIEM end users, and are strictly Accountable and Responsible for developing and maintaining rules and reports. They are also active in providing input for new use cases, but the Accountability and Responsibility for that task falls on the SIEM owner.

The Incident Response Team is Accountable and Responsible for responding to the alerts generated by the correlation rules, and reviewing reports. They are also Accountable and Responsible to provide tuning recommendations for the rules and reports based on their investigations and observations.

The Engineers tried to get the Content Team to manage user accounts, but they lost the battle and ended up getting the task.

 

As you can see, a RACI is a simple document that can clarify who is responsible for what part of the SIEM environment. It can also be used by leadership to quantify work efforts, assist in understanding the various tasks employees do, and identify areas that require improvement. Issues can be raised and be visible to leadership on Monday morning instead of Friday afternoon, or during a breach.

A RACI is not practical without three other major requirements: clear ownership, a teamwork-oriented environment, and a strong SIEM skillset. Clear ownership gives the owner an incentive to maintain and improve the SIEM, and prevents issues from being ignored or assuming they’re the responsibility of another entity. A high-performing team maintains and improves the environment, retains highly-skilled staff, and attracts new talent into the team. A strong SIEM skillset allows staff to execute the required tasks. All of this contributes to a better return on investment the SIEM will provide your organization, and ultimately a better security posture.

 

Link to a sample SIEM RACI Matrix: Sample_SIEM_RACI

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A SIEM Odyssey: How Albert Einstein Would Have Designed Your SIEM Architecture

Albert Einstein taught us that there are four dimensions: the three physical dimensions plus time. The light being generated by the sun exists, but it will take about eight minutes to reach the earth before it exists in our environment. Many of the lights you see in the sky at night were generated by stars millions of years ago, and may no longer exist today.

The four dimensions of spacetime can teach us a lot about the universe, and now a good lesson on SIEM architecture design. In SIEM environments, log data is sent through various layers, introducing a delay between the data source and destination. In a properly designed SIEM architecture, the delay between the source and destination should be minimal, a few minutes at most. But in an undersized SIEM architecture, delays between the source and destination can be high, and in worst cases, data may not reach the destination at all.

SIEM environments have three main layers. The first is the data sources, the various Windows servers, firewalls, and security tools that will either send data to your SIEM, or your SIEM will pull data from. The second layer is the Processing Layer, which consists of applications (Connectors/Forwarders/Ingestion Nodes) designed to process and structure log data, and forward it. The final layer is the Analytics Layer, which is where log data is stored, security analytics is performed, and end users search for data.

To highlight the risk introduced into your organization by an undersized SIEM architecture, we’ll use a DDoS attack against your organization as an example. The Bad Guyz Group has found a clever way to funnel millions out of your organization. Before they initiate the fraud scheme, they want to distract your organization from what is actually going on in order to buy themselves time, and thus launch a large-scale DDoS against your web servers.

Your DDoS protections begin sending out alerts, notifying your SOC of the attack and that there’s no concern at the moment. The amount of traffic directed at your web servers seems to be increasing, but is not near a level that will void your DDoS protections. Your SOC notifies leadership that even though there’s an active attack in progress, there’s nothing to be worried about.

While your DDoS protection is working as expected, your SIEM Processing Layer is being flooded with a 400% increase in firewall and proxy traffic. Your SIEM Processing Layer was only designed to process 10,000 events per second (EPS), and is now struggling to process a surge of 40,000 EPS. Cache files start to appear within minutes, growing at a rate of 100GB per hour, which will exhaust cache space within eight hours.

Hours later, SOC Analysts notice that the timestamps on most of the log data are several hours behind. They send an email to the SIEM Engineers, asking to see if there’s anything wrong with the SIEM application. When the SIEM Engineers get out of their project meeting a few hours later, they login to the servers and notice the cache files, extremely high EPS rates, and maxed-out RAM/CPU usage. They then notice that the surge in data is being caused by firewall and proxy logs. After a conversation with the SOC, the SIEM Engineers are then informed of the DDoS attack that happened earlier in the day.

Later in the evening, the SIEM Engineers warn leadership that the Processing Layer is dropping cache files of log data and is refusing new connections, resulting in data loss. The average log data delay now stands at eight hours as the DDoS attack continues.

Fortunately, the DDoS attack stopped the following morning, and the SIEM Processing Layer began reducing the amount of cache files on the servers. The SIEM Engineers anticipate that cache files should be completely cleared by 5PM.

Later that morning, the SOC Manager gets a call from the Fraud team, asking if they can see traffic to several IP Address. The SOC Analysts begin searching, but the response times are very slow and the latest data available is from last night. Just as the SIEM Engineers expected, by 5PM all cache files were cleared, and analysts were searching data in real-time again. They found only one hit from one of the IPs provided. The Fraud team insists there should be more than that, but the SIEM Engineers note that the other hits may have been dropped when the Processing Layer was refusing new connections during the surge in data. Leadership isn’t happy, and calls for an immediate review of the SIEM environment.

The bad news is that many SIEM environments are not sized appropriately to deal with such scenarios, or with legitimate data surges in general. These situations can leave your organization blind to an attack in progress, as the data required for an investigation exists, but is not yet available to your analysts, or worse is being dropped from existence.

The good news is that you can significantly reduce the probability and severity of this scenario. While SIEM environments can be expensive, the costly part is typically the Analytics Layer, and for many organizations over-sizing this layer isn’t an option. However, the Processing Layer tends to be much less expensive, and in some cases would simply result in the cost of the physical or virtual servers required.

A SIEM Processing Layer should be significantly larger than your sustained average event per second rate. While this number is a requirement to determine SIEM application licensing costs, many organizations make the mistake of sizing their SIEM according to this metric. In addition to spikes, the amount of traffic received by your SIEM during the day is likely to be much higher than at night. If your sustained EPS rate is 20,000 EPS, then it can be possible for your EPS during the day to be 30,000, and EPS at night to be in the 5,000-10,000 EPS range. If you receive a spike in traffic during the day, the 30,000 EPS can turn into 60,000 EPS. In many SIEM environments, this would cause the Processing Layer to quickly exhaust caches and begin dropping data. Supporting a large spike in traffic could simply be done by adding more devices (Connectors/Forwarders/Ingestion Nodes) within the Processing Layer. The increase in processing power and overall cache availability would reduce the risk of log transmission delays and data dropping.

In addition to reducing the probability of data delay and loss, an over-sized Processing Layer brings high availability benefits, and as well can make migrations and upgrades easier. If you have a single point of failure, you can lose your Processing Layer entirely if the device fails. If you have enough Connectors/Forwarders only to process your sustained EPS rate, you risk the above scenario when one of the devices within the Processing Layer fails, as the others have to make up for the extra EPS rates. If you need to upgrade your Processing Layer, the extra devices can make the upgrade smoother and transparent to any operational issues.

While we may have solved the issue with the Processing Layer, the surge in data can also result in transmission delays, data loss, slower end-user search response times, and system stability issues on the Analytics Layer. However, while it may seem logical to build an Analytics Layer to support double the sustained EPS rates, it can be cost prohibitive for many organizations. An adequately-sized Processing Layer can assist during surges by aggregating data (combining similar events into one, for SIEM products that support aggregation), caching it locally, and limiting the EPS-out rates to the Analytics Layer. Your SIEM Engineers can also control what data is sent over others, so if there’s a dire need for a particular data source, your staff can limit other data sources to be sent to the Analytics Layer so that the pertinent sources can be sent in priority.

In summary, there’s a strong return on investment for building an adequate SIEM Processing Layer given the low costs, risk reduction, and invaluable security benefits. Even with a minimal Analytics Layer, a properly-sized Processing Layer will be sufficiently able to process a surge of data, cache data that can’t be forwarded, prevent data loss, and reduce risks caused by large increases in log data. Make the investment and leave spacetime issues for the physicists!

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