SIEM Use Cases: Automating Security for Comprehensive Protection
Knowing how to apply the analytical power of your security tool is key to achieving full visibility and efficiency. The flexibility of mission-critical tools like a Security Information and Event Management (SIEM) allows for unparalleled log management – but, the dense thicket of settings, rules, and options can make it unwieldy and difficult to define. To keep a SIEM highly functioning, it’s critical to define its precise use cases, and refine its performance from there. Done correctly, SIEM systems provide unparalleled insights into potential events, account activities, and regulatory requirements. This guide covers the myriad of in-depth SIEM use cases – and shows you how to create your own.
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How AI is Advancing SIEM
SIEM AI integration streamlines the ability to process and parse security information. From a security analyst perspective, embedding GenAI into SIEM solutions is starting to accelerate research and response tasks. For an in-depth exploration of how LLMs are complementing SIEM tools, see our guide here.
Much of this acceleration is within the central analysis engine of the SIEM: machine learning was already a core component to the SIEM’s ability to sort through and analyze the swathes of log data being ingested, but the current wave of AI-driven threat detection allows for far faster and more accurate approaches. This lets current SIEM tools automate more log file analysis with greater accuracy than ever before.
For Stellar Cyber, this process allows not just for core log analysis, but also for deeper incident examination. Our AI ingests the alerts caused by log anomalies and compares them against other alerts being generated in connected systems; these are then grouped into comprehensive incidents. One-off alerts are assessed for their likelihood of anomaly, and dropped completely if they’re false positives.
Of course, this demands that the log sources connected to the SIEM encompass the entirety of an enterprise’s devices, endpoints, and servers. This is where AI is also driving significant Mean Time to Detection (MTTD) improvements: by not just adding devices across networks, but also by normalizing the vastly disparate types of data that each produces. Collectively, SIEM automation and the big data architecture they’re based on are underscoring today’s big leaps in efficiency and threat prevention.
Let’s dive into the individual use cases that SIEMs are driving forward.
Key SIEM Use Cases
Centralized and Cost-Effective Log Management
Logs grant an enterprise’s security team deep visibility into the actions occurring across their attack surface. But since every action within every server, device, and firewall creates an individual log, the sheer quantity of these can make them overwhelmingly time-consuming to manually monitor. SIEMs ingest the entirety of this data with agents, or directly via syslogs, and then rely on an automated analysis process.
As the data flows down the log funnel, these hundreds of millions of log entries are whittled down to a handful of actionable security alerts. In Stellar Cyber, this process is driven by Graph ML. Further optimization within this use case has focused on storage, indexing, and prioritization of those logs. Big data architecture now allows for more cost- and performance-efficiency thanks to scalable cloud-based storage. With a next-generation SIEM like Stellar Cyber, this storage can also be varied depending on the urgency of specific logs. Hot data that needs to be used for real-time log management is hosted on high-performance storage, while forensic data that’s needed for compliance (more on that in a bit) can be kept in cold, low-cost storage.
With logs suitably managed, it’s important to establish what precisely your SIEM is doing with those logs.
Phishing Attack Detection
Phishing is one of the most popular attack vectors, as humans are the least-patchable component within an enterprise’s attack surface: SIEM’s visibility into endpoint devices makes it well-positioned to identify malicious communications and prevent them from reaching and affecting end-users.
This is achieved due to the vast mix of data being ingested: this can include email messages and their context, email gateway data, and domain analysis. At the individual message level, suspicious communications can be identified and prevented via logs that map out conversation history and an LLM that examines for malicious intent. Many successful phishing attacks rely on directing victims to typosquatted domains: network-level logs are able to assess the legitimacy and intended behaviours of webpages and applications before the user accesses these malicious sites.
Each individual aspect – a dodgy URL, a slightly-mistyped domain, and high-stress message – are all cross-referenced against one another, and build a risk score for the phishing use case.
Insider Threat Detection
SIEM solutions solve the problem of otherwise-indetectable insider threats by monitoring the activities of each user and identifying normal patterns of user behavior. For instance, Mark from sales usually spends most of his day interacting with the CRM, the VoIP system, and his emails. Should his device suddenly begin performing a high amount of port scans and repeatedly failing login attempts, the correct SIEM tool can rapidly alert the cybersecurity team of potential account compromise.
User-behaviour analytics within SIEMs can spot almost any sudden change in account activity: some of the more simple detection relies on log-on times, while others take running applications, data, and account activities into consideration.
Ransomware and Malware Protection
Alongside identifying stolen accounts, SIEM tools are able to identify attempted ransomware infections. This type of attack sees cybercriminals attempt to steal and encrypt an enterprise’s data, before demanding a ransom payment for its return.
The granularity introduced by full log visibility allows ransomware to be broken down into three key stages, and a number of prevention mechanisms implemented for each phase. The first is the distribution phase, where the ransomware exists as a sneaky executable bundled in with a malicious file download. SIEMs are able to detect and automatically prevent many distribution attempts – like phishing – but new distribution methods are always evolving. So, the following stage is the infection phase. This is where, if the ransomware used a dropper to stay under the radar, this dropper establishes a connection with the command-and-control server. A SIEM is also able to detect malicious indicators of compromise by both discovering unexpected connections and decoding associated files.
The final stage is reconnaissance and encryption: this incorporates file copying, extraction, and finally encryption. Discovering these behaviors is, once again, SIEM ransomware detection: if the SIEM discovers excessive file deletion and creation, or spots a suspicious quantity of files being moved – then there is a high chance of ransomware, and the security team is alerted immediately and malicious actions shut down.
Compliance Management
Industry standards demand a lot from their corresponding companies: a continuous theme is the amount of time that logs need to be held for. PCI DSS, SOX, and HIPAA all require that logs are held for anywhere between 1 and 7 years. A typically expensive and resource-hogging requirement, advanced SIEMs are much smarter about their log storage strategies.
For one, syslog servers are able to compress logs and therefore retain a lot of historical data for less cost. Alongside this are suitable deletion schedules, where outdated data is deleted automatically. Finally, SIEMs are able to filter out the logs not explicitly required by your own industry compliance.
Cloud Security Monitoring
When cloud services come into play, one of the biggest differences is the sheer number of different types of data sources that can exist – especially if you leverage platform-as-a-service (PaaS) and software-as-a-service (SaaS) offerings. Stellar Cyber allows for SIEM cloud monitoring regardless of the specific data types being generated.
Identity and Access Management (IAM) Monitoring
IAM and SIEM are slightly different forms of security: the former places a key focus on identifying who has access to different resources, while the latter is primarily a tool for monitoring the ongoing activities of every software component. However, by integrating the two systems, it becomes possible to strengthen them.
Take the specific use case of identifying malicious account creation: a very common component to most attacks, if your IAM system can identify an ‘account add’ action, your SIEM tool is given a better chance at quickly distinguishing malicious account creation.
Stellar Cyber achieves SIEM IAM monitoring by tight integration with IAM providers, and therefore ingesting advanced user access management and visibility. Services like Azure Active Directory (now Microsoft Entra ID) are used to enrich Incident profiles and provide deeper User Behavior Analytics. User-by-user rules are enforceable, helping automate SIEM insider threat detection.
Collectively, these use cases cover large swathes of attack surface within different enterprises and industries. The next part is establishing precisely which use cases your organization needs to hone in on – particularly when first getting set up.
How to Build a Clear SIEM Use Case
Stellar Cyber SIEM takes a threefold approach to these challenges: firstly, it establishes a baseline of universal visibility; it then feeds alerts into an analysis engine, and correlates the genuine attack indicators into ‘cases’. Finally, threats can be responded to within the dashboard itself, both manually and via automated playbooks. These integrated analyses, visualizations, and responses make Stellar Cyber a Next-Gen SIEM.
Universal Sensors for Peak Security Visibility
Building SIEM use cases rely on three core components:
- Rules: These detect and trigger alerts based on targeted events.
- Logic: This defines the way in which events or rules are analyzed.
- Action: This identifies the outcome of the logic: if its conditions are met, then this defines what the SIEM does with it – either sending an alert to the team, interacting with firewalls and preventing data transfer, or simply monitoring well-behaved actions.
Individual use cases need to be led by these three guiding processes. From there, however, SIEM implementation takes some imagination and analysis to identify the most important use cases that your organization will need. Consider the types of attacks you may face. This involves identifying business threats that are relevant to your organization and – for each attack – linking it to the corresponding resources. By the end of this process, you’ll have a clear map that connects business risks to specific attack vectors.
Then, establish how and where these attacks should be addressed by categorizing the identified attacks within the selected framework. For example, an external scan attack might fall under reconnaissance or targeting in your framework.
Now, connect the two relationships: high-level use cases will correspond to identified business threats, and they can be broken down into more specific low-level use cases. If your high-level use case is data loss, the low-level use cases could include server compromise, data exportation, or unauthorized administrator activity.
Each low-level use case will be logically tied to specific attack types, which will assist in defining technical rules. These rules may overlap across multiple low-level use cases, and each use case could involve several rules. Defining this structure is crucial, as it will clarify the connection between log sources and the technical rules needed to implement them effectively.
By the time you’ve sat down and worked through this, you’ll be perfectly positioned to define technical rules. Each granular use case could fit multiple rules, meaning it’s important to keep a map of the rules you’re establishing. This fuels your SIEM risk prioritization ability.
Once these rules are in place, they require continuous development: some SIEMs aid this process more than others. For Stellar, the outcome of currently-deployed rules is immediately accessible and able to be filtered via the alert and status panels. With trend information showing criticality, tenants, and playbooks, the next step toward greater SIEM efficiency is always clear.
How Stellar Cyber Automates Your Use Cases
