A SIEM use case library turns scattered detection rules into a structured, risk-aligned detection program. Instead of asking “What do we do with all these logs?”, the library documents what each rule detects, which business risk it supports, which log sources it requires, how it maps to MITRE ATT&CK, and what analysts should do when it fires.
The research sources are consistent on one point: detection quality depends less on having “more alerts” and more on having well-documented, tested, tuned use cases. This tutorial walks through how to build a practical SIEM use case library for enterprise threat detection, using documented fields, examples, and operational practices from SIEM use case resources, Hunto AI’s use case library guidance, public detection repositories, and real-world SIEM use case research.
1. What Is a SIEM Use Case Library?
A SIEM use case library is a structured catalog of detection scenarios that your security operations center uses to identify, investigate, and respond to threats.
A use case typically describes:
- What threat or behavior is being detected
- Why it matters to the business
- Which log sources are required
- What correlation logic or query powers the detection
- How the rule maps to MITRE ATT&CK
- What severity should be assigned
- What false positives are expected
- How the alert should be tuned
- Which response playbook analysts should follow
Hunto AI describes a SIEM use case library as pre-built detection use cases organized by MITRE ATT&CK tactic, including required log sources, correlation logic, suggested severity, and tuning recommendations. SIEMUseCases.com frames the same problem from a detection engineering perspective: many resources for building use cases are poor quality or vendor-driven, so teams need practical resources that help generate high-quality alerts.
A useful SIEM library is not just a list of rules. It is an operating model for detection engineering, SOC triage, incident response, and threat hunting.
A library can include vendor-provided detections, custom rules, Sigma rules, queries written for SIEM platforms, and internal scenarios based on incidents, audits, purple team exercises, or threat intelligence.
Public detection collections also show how flexible a library can be. One GitHub SIEM detection use case repository describes use cases as a working list of scenarios and events gathered from work experience, reading, public posts, threat reports, and other sources. It notes that applicable queries may be written in formats such as KQL, S1 STAR, Sigma, or Wazuh, and that table names and actions often need adjustment for each environment.
Common SIEM Use Case Categories
Based on Hunto AI’s library categories and the SIEM use case research data, common enterprise detection categories include:
| Use Case Category | Example Detection Scenarios | Typical Security Objective |
|---|---|---|
| Authentication anomalies | Brute force, credential stuffing, impossible travel | Detect compromised accounts and suspicious access |
| Privilege escalation | Unauthorized admin actions, privilege abuse, role manipulation | Identify misuse of elevated access |
| Lateral movement | Unusual RDP, PsExec, WMI, or SSH activity between hosts | Detect attackers moving through the environment |
| Data exfiltration | Large outbound transfers, cloud upload anomalies, DNS tunneling | Prevent unauthorized data loss |
| Malware indicators | Known malicious hashes, C2 communication patterns, beacon detection | Detect malware activity and command-and-control behavior |
| Persistence mechanisms | New scheduled tasks, registry autorun changes, service installation | Find attacker footholds |
| Cloud security | IAM policy changes, public bucket exposure, unauthorized API calls | Detect risky cloud control-plane activity |
| Insider threat | Mass file access, off-hours data movement, account sharing indicators | Identify misuse by authorized or compromised users |
2. Why Use Case Libraries Improve Detection Quality
A SIEM can collect logs from endpoints, networks, cloud applications, authentication systems, firewalls, and other security tools. But according to the SIEM use case research, the value comes from correlation: a login at 2 a.m. may not be suspicious by itself, but that same login combined with outbound data transfers and a new USB device changes the story.
A use case library improves detection quality because it gives teams a repeatable way to connect raw events to real risks.
Better Coverage Visibility
Hunto AI recommends mapping current detection rules against the MITRE ATT&CK matrix to identify coverage gaps. This gives security teams a way to see which tactics and techniques are covered and where blind spots remain.
The same source recommends using the ATT&CK Navigator to visualize detectable techniques and prioritize gaps.
Better Prioritization
Not every possible rule deserves equal attention. Hunto AI recommends focusing first on techniques commonly used in real-world attacks against your industry, especially techniques in:
- Initial access
- Execution
- Persistence
- Credential access
These tactics are valuable because they represent earlier stages of an attack, where detection can provide more time to respond.
Better Alert Quality
A documented use case library forces teams to record false positive scenarios, tuning notes, ownership, and response actions. That matters because Hunto AI warns that a detection rule with a 90% false positive rate is “worse than useless” because it trains analysts to ignore it.
High-volume, low-quality alerts create analyst fatigue. A well-tuned use case that fires accurately is more valuable than dozens of noisy rules that nobody trusts.
Better Measurement
A library also makes detection engineering measurable. Hunto AI recommends tracking:
- Use case count and ATT&CK coverage percentage
- Mean time from use case creation to production deployment
- True positive rate per use case
- False positive rate and tuning effort per rule
- Use cases that never fire
- Quarterly goals aligned with threat intelligence priorities
These metrics help teams move from “we have alerts” to “we understand which detections work.”
3. Step 1: Define Business Risks and Priority Threats
The first step in building a SIEM use case library is not writing queries. It is defining the risks your organization needs to detect.
Start by identifying the business processes, systems, and data that matter most. Then map threats to those assets. The research data highlights several high-value enterprise threat areas: data exfiltration, lateral movement, insider threats, zero-day behavior, IoT or OT security exposure, and centralized log management.
Build a Risk-to-Use-Case Map
Use the following structure to connect business risk to detection needs:
| Business Risk | Priority Threat | Example SIEM Use Cases | Relevant Source Evidence |
|---|---|---|---|
| Sensitive data loss | Data exfiltration | Large outbound transfers, cloud upload anomalies, USB usage, personal email access | AIMultiple describes SIEM detection of abnormal data access, USB usage, personal email access, cloud storage transfers, and high data volumes |
| Account compromise | Credential abuse | Impossible travel, brute force, credential stuffing, unusual authentication sequences | Hunto AI lists authentication anomalies; AIMultiple describes compromised credential detection |
| Privilege misuse | Privilege escalation | Unauthorized admin actions, role manipulation, suspicious privilege escalation | Hunto AI lists privilege escalation and unauthorized admin actions |
| Internal network compromise | Lateral movement | Unusual RDP, PsExec, WMI, SSH activity between hosts | Hunto AI lists these lateral movement patterns; AIMultiple describes abnormal access to systems users have never accessed |
| Cloud misconfiguration or abuse | Cloud security incidents | IAM policy changes, public bucket exposure, unauthorized API calls | Hunto AI lists these cloud security use cases |
| Insider misuse | Malicious or compromised authorized user | Mass file access, off-hours movement, account sharing indicators | Hunto AI lists insider threat indicators; AIMultiple describes monitoring user activity logs, system logs, and network logs |
Prioritize Early-Stage Attack Detection
Hunto AI recommends prioritizing detections mapped to early attack stages such as initial access, execution, persistence, and credential access. In practice, that means your first library release should include detection scenarios such as:
- Suspicious authentication: Brute force, impossible travel, unfamiliar locations
- Credential misuse: Unusual access sequences, login patterns outside normal behavior
- Persistence: New scheduled tasks, registry autorun modifications, service installation
- Privilege escalation: Unauthorized admin actions or role manipulation
- Data movement: Unusual file access, large outbound transfers, cloud upload anomalies
Use Real-World Threat Context
AIMultiple’s SIEM use case research cites the CrowdStrike 2026 Global Threat Report, noting that 82% of attacks are malware-free. That finding supports an important design decision: do not rely only on signature-based malware detections.
For risks such as data exfiltration, insider threat, and zero-day behavior, your SIEM use case library should include behavioral and correlation-based detections, not just known hashes or static indicators.
4. Step 2: Map Use Cases to MITRE ATT&CK
Once business risks are defined, map each detection use case to MITRE ATT&CK. This makes the library easier to organize, measure, and improve.
SIEMUseCases.com states that its detection resources are mapped to MITRE ATT&CK where appropriate. Hunto AI also recommends organizing use cases by ATT&CK tactic and including tactic, technique, and sub-technique IDs in the template.
What to Capture in the ATT&CK Mapping
For each use case, document:
- Tactic: The adversary objective, such as credential access, persistence, or lateral movement
- Technique: The specific behavior being detected
- Sub-technique: The more precise behavior, if applicable
- Coverage status: Implemented, planned, blocked, testing, or retired
- Detection confidence: How reliable the rule is after testing and tuning
Avoid treating ATT&CK mapping as a checkbox exercise. The mapping should help answer operational questions:
- Which tactics are well covered?
- Which techniques have no detection?
- Which high-risk techniques are blocked by missing logs?
- Which rules exist but have poor true positive rates?
Use ATT&CK to Identify Gaps
Hunto AI recommends using the ATT&CK Navigator tool to visualize detectable techniques and blind spots. This is especially useful when comparing your current rule set against priority threats for your industry.
A simple coverage workflow looks like this:
- Inventory existing rules in your SIEM.
- Map each rule to an ATT&CK tactic and technique where appropriate.
- Mark rules with missing log sources as blocked.
- Highlight high-priority techniques related to initial access, execution, persistence, and credential access.
- Create quarterly goals for new detection development based on threat intelligence priorities.
ATT&CK mapping is most useful when it drives decisions: what to build next, what data to onboard, and what low-value rules to retire.
5. Step 3: Identify Required Log Sources
A SIEM detection rule only works if the required data exists. That is why every SIEM use case library should include a log sources field.
Hunto AI’s template explicitly includes required data sources for each rule to function. Its guidance also says that when a use case requires a log source you do not have, you should document the gap and use it as evidence for new data source onboarding.
Common Log Sources by Use Case Type
The sources describe SIEMs correlating data across endpoints, networks, cloud applications, authentication systems, and security tools. Use that as the foundation for your log source planning.
| Use Case Type | Required or Common Log Sources Mentioned in Source Data | Detection Purpose |
|---|---|---|
| Authentication anomalies | Authentication systems, user activity logs | Detect brute force, credential stuffing, impossible travel, unfamiliar access |
| Data exfiltration | File access logs, network traffic, cloud storage activity, USB activity, email access | Detect unusual data movement and unauthorized transfer channels |
| Lateral movement | Endpoint logs, network connections, security systems, intrusion detection tools | Detect unusual host-to-host access and abnormal movement patterns |
| Insider threat | User activity logs, system logs, network logs, endpoint telemetry | Correlate authorized user behavior across systems |
| Zero-day behavior | Firewalls, endpoints, intrusion prevention systems, DNS logs | Detect anomalous behavior where signatures may not exist |
| Cloud security | Cloud API activity, IAM changes, bucket exposure events | Detect policy changes, public exposure, unauthorized API calls |
| IoT or OT security | Device traffic, asset discovery data, device behavior baselines | Detect unusual device behavior, DoS patterns, insecure protocols |
Document Blocked Use Cases
Not every desired detection will be immediately possible. Hunto AI recommends documenting use cases blocked by missing log sources instead of deleting them.
A practical status model:
| Status | Meaning | Action |
|---|---|---|
| Implemented | Detection is active in production | Monitor quality metrics |
| Testing | Rule is deployed in a test or limited mode | Validate logic and false positives |
| Planned | Use case is approved but not built | Assign owner and target date |
| Blocked: log source gap | Required data is not available | Use as evidence for onboarding |
| Retired | Rule was removed due to low value or replacement | Keep historical notes |
This turns the library into a roadmap. If ten high-value detections are blocked by one missing data source, that becomes a concrete business case for onboarding that source.
6. Step 4: Write Detection Logic and Tuning Notes
Detection logic is the rule, query, or correlation condition that turns log data into an alert. But logic alone is not enough. Your SIEM use case library should also document tuning notes, known false positives, thresholds, and assumptions.
Hunto AI’s template includes correlation logic, false positive scenarios, and tuning recommendations. The GitHub SIEM use case repository also notes that queries should be adjusted because table names and actions differ across environments.
Use Vendor-Neutral Logic First
Where possible, write the detection in plain language or pseudo-code before translating it into SIEM-specific syntax. This makes the use case easier to maintain across platforms.
For example, AIMultiple describes a scenario where a login at 2 a.m. becomes more suspicious when combined with outbound transfers and a new USB device. That can be represented as vendor-neutral logic:
IF user_login occurs outside normal hours
AND same user performs unusually high outbound data transfer
AND new USB device activity is observed
THEN generate data exfiltration alert
WITH severity based on asset sensitivity and data volume
This pseudo-code can later be implemented in the SIEM query language your environment uses.
Detection Logic Examples by Use Case
| Use Case | Example Correlation Logic | Tuning Notes |
|---|---|---|
| Impossible travel | Alert when a user authenticates from locations that are not plausible within the observed time window | Account for VPN, travel patterns, and approved remote access |
| Large outbound transfer | Alert when a user or host sends unusually high volumes of data externally | Allowlist backup servers and legitimate large transfers |
| Unusual RDP, PsExec, WMI, or SSH | Alert when a user connects to systems they do not normally access | Account for approved admin jump hosts |
| New scheduled task | Alert when new scheduled tasks appear on endpoints | Allowlist deployment tools and known maintenance jobs |
| Cloud IAM policy change | Alert when IAM policies change unexpectedly | Tune for approved change windows and authorized administrators |
| Mass file access | Alert when a user accesses a much larger number of files than normal | Account for approved migrations, indexing, or backup activity |
Capture False Positive Scenarios
A false positive scenario is a known benign behavior that may trigger the rule. Hunto AI gives concrete examples of allowlist candidates:
- Scheduled tasks from deployment tools
- Legitimate large file transfers between backup servers
- Admin activity from approved jump hosts
Documenting these scenarios prevents future analysts from repeatedly rediscovering the same explanation.
Tune with Measured Observation
Hunto AI recommends monitoring the false positive rate for two weeks after deploying each use case. During that period, record what fired, why it fired, whether it was useful, and which changes were made.
Tuning options include:
- Allowlists: Known-good users, hosts, tools, servers, or destinations
- Thresholds: Adjust event counts, volume limits, or time windows
- Timing adjustments: Account for business hours, maintenance windows, and backup schedules
- Scope changes: Apply the rule only to sensitive assets or high-risk groups
- Severity changes: Lower severity for noisy but useful detections, or raise it for high-confidence events
Every tuning change should be documented. Hunto AI specifically recommends recording tuning changes so future analysts understand why the rule looks the way it does.
7. Step 5: Add Severity, Ownership, and Response Playbooks
A detection rule without ownership and response guidance can create confusion during an incident. Your library should tell analysts what the alert means, how urgent it is, who owns it, and what to do next.
Hunto AI’s use case template includes severity and a response playbook link. SIEMUseCases.com also notes crossover between SIEM use cases, SOC operations, incident response, and threat hunting.
Define Severity Consistently
Hunto AI’s template uses four severity levels:
| Severity | Suggested Meaning |
|---|---|
| Critical | High-confidence alert involving major business impact or active compromise |
| High | Serious threat requiring urgent investigation |
| Medium | Suspicious activity requiring triage and context |
| Low | Informational or low-confidence signal useful for enrichment or hunting |
The source data does not prescribe exact enterprise scoring formulas, so avoid inventing one. Instead, document the criteria your organization uses and apply them consistently.
Severity should consider:
- Asset criticality
- User privilege level
- Confidence of the detection
- Potential business impact
- Whether multiple suspicious behaviors are correlated
Assign Clear Ownership
Each use case should have an owner. Ownership does not need to be a named individual; it can be a role or team.
Examples:
- Detection Engineering: Rule logic, testing, tuning
- SOC Tier 1: Initial triage
- SOC Tier 2: Escalated investigation
- Incident Response: Containment and remediation
- Cloud Security: Cloud IAM, API, and storage detections
- Identity Team: Authentication and privileged access detections
This reduces ambiguity when an alert fires or when a rule becomes noisy.
Link Response Playbooks
A response playbook should explain what analysts do after an alert fires. At minimum, include:
- Initial triage steps
- Relevant enrichment sources
- Containment options
- Escalation path
- Evidence to collect
- Closure criteria
AIMultiple’s data exfiltration research gives examples of response actions such as isolating compromised devices and blocking malicious IPs. Its real-world examples also highlight the value of connecting detection to recovery workflows, such as verifying data integrity and restoring from clean backups in an integrated detection-and-recovery process.
For a data exfiltration alert, a playbook may include:
- Validate identity activity: Review authentication and user behavior logs.
- Confirm data movement: Check file access, transfer volume, email, USB, and cloud upload activity.
- Assess asset sensitivity: Determine whether sensitive data was accessed.
- Contain if needed: Disable account, isolate device, or block destination where supported.
- Escalate: Engage incident response and business data owners.
- Document outcome: True positive, false positive, tuning required, or unresolved.
8. Step 6: Test, Tune, and Retire Low-Value Rules
A SIEM use case library is not a one-time spreadsheet. It is a living detection engineering system.
Hunto AI recommends tracking true positive rates, false positive rates, tuning time, deployment time, and use cases that have never fired. It also recommends purple team exercises where a red team executes known techniques and the organization evaluates whether use cases detect them.
Test Before Production
Before promoting a rule to production, validate:
- Required logs are present
- Fields are parsed correctly
- Correlation logic works as intended
- Expected benign activity does not overwhelm analysts
- Severity matches investigation urgency
- Playbook steps are usable
If you use shared detection content, such as Sigma rules or public repositories, expect customization. SIEMUseCases.com suggests the Sigma ruleset as a strong starting point for teams that have inherited or acquired a SIEM and need to decide what to do with logs. Hunto AI explains that Sigma is a vendor-agnostic detection rule format that can be converted to queries for most major SIEM platforms.
The GitHub SIEM use case repository adds an important practical warning: table names and action names may differ from your environment, so queries often need adjustment.
Monitor After Deployment
For each newly deployed use case, follow Hunto AI’s recommendation to monitor false positives for two weeks. During that period, capture:
| Metric | Why It Matters |
|---|---|
| Alert volume | Shows whether the rule is operationally manageable |
| False positive rate | Identifies noisy rules that need tuning |
| True positive rate | Shows whether the rule provides security value |
| Time spent tuning | Helps estimate maintenance cost |
| Time to production | Measures detection engineering throughput |
| Never-fired status | May indicate missing data, bad thresholds, or rare behavior |
Retire or Rework Low-Value Rules
Not every rule should live forever. Hunto AI recommends reviewing use cases that have never fired and assessing whether the data source is missing or the threshold is wrong.
A rule may be retired if:
- It produces excessive false positives
- It duplicates a better detection
- The required log source is no longer available
- The threat scenario is no longer relevant
- The alert does not lead to useful action
- Analysts consistently ignore it due to poor quality
Retirement should be documented. Keep the use case ID, retirement reason, date, and replacement rule if one exists. This prevents the same low-value detection from being rebuilt later.
How Many Use Cases Should You Have?
Hunto AI states that there is no magic number, but most mature SOCs run 200 to 400 active use cases. The same guidance emphasizes that quality matters more than quantity.
A smaller library of accurate, well-tuned detections is more useful than a large library full of noisy alerts.
9. Example SIEM Use Case Library Template
The following template is based on Hunto AI’s documented use case fields, expanded with operational fields commonly needed for enterprise lifecycle management.
Use this as a starting point for your own SIEM use case library.
| Field | Description | Example Entry |
|---|---|---|
| Use Case ID | Unique identifier for tracking and reference | UC-EXFIL-001 |
| Name | Descriptive name of the detection | Large Outbound Transfer After Suspicious Login |
| Description | What the rule detects and why it matters | Detects unusual data transfer following suspicious authentication behavior |
| Business Risk | Business impact the use case supports | Sensitive data loss |
| Use Case Category | Detection family | Data exfiltration |
| MITRE ATT&CK Mapping | Tactic, technique, and sub-technique IDs where appropriate | Map to the relevant ATT&CK tactic and technique during implementation |
| Log Sources | Required data sources for the rule to function | Authentication logs, network traffic, file access logs, USB activity where available |
| Correlation Logic | Query or rule logic in pseudo-code or SIEM syntax | Suspicious login + unusually high outbound transfer + optional USB activity |
| Severity | Critical, High, Medium, or Low | High |
| False Positive Scenarios | Known benign behaviors that may trigger the rule | Backup transfers, approved admin activity, scheduled data movement |
| Tuning Recommendations | Allowlists, thresholds, and timing adjustments | Allowlist backup servers; tune thresholds by environment size |
| Owner | Responsible team or role | Detection Engineering |
| Response Playbook | Link or reference to incident response procedure | Data Exfiltration Investigation Playbook |
| Status | Implemented, Testing, Planned, Blocked, Retired | Testing |
| Last Reviewed | Review date or cycle marker | Current quarterly review |
| Effectiveness Metrics | True positives, false positives, tuning effort | Track after two-week observation period |
| Notes | Additional analyst or engineering context | Validate whether cloud upload logs are available |
Sample Library Rows
| Use Case ID | Name | Category | Log Sources | Severity | Status |
|---|---|---|---|---|---|
| UC-AUTH-001 | Brute Force Authentication Attempts | Authentication anomalies | Authentication systems, user activity logs | Medium or High based on context | Implemented |
| UC-AUTH-002 | Impossible Travel Login | Authentication anomalies | Authentication logs, location context where available | High | Testing |
| UC-LAT-001 | Unusual RDP, PsExec, WMI, or SSH Activity | Lateral movement | Endpoint logs, network connections, security system logs | High | Planned |
| UC-EXFIL-001 | Large Outbound Data Transfer | Data exfiltration | Network traffic, file access logs, cloud upload activity | High | Implemented |
| UC-PERSIST-001 | New Scheduled Task Creation | Persistence mechanisms | Endpoint and system logs | Medium | Testing |
| UC-CLOUD-001 | Unexpected IAM Policy Change | Cloud security | Cloud API and IAM activity logs | High | Planned |
| UC-INSIDER-001 | Mass File Access After Hours | Insider threat | User activity logs, file access logs, network logs | High | Testing |
Simple Markdown Template
You can also store each use case as a Markdown file in a repository:
# Use Case ID: UC-EXFIL-001
## Name
Large Outbound Transfer After Suspicious Login
## Description
Detects unusual outbound data movement following suspicious authentication behavior.
## Business Risk
Sensitive data loss.
## MITRE ATT&CK Mapping
Map to the relevant ATT&CK tactic, technique, and sub-technique during implementation.
## Required Log Sources
- Authentication logs
- Network traffic logs
- File access logs
- USB activity logs where available
- Cloud upload activity where available
## Correlation Logic
IF suspicious login occurs
AND unusually high outbound data transfer follows
AND optional USB or cloud upload activity is present
THEN generate alert.
## Severity
High
## False Positive Scenarios
- Approved backup transfers
- Scheduled file movement
- Admin activity from approved jump hosts
## Tuning Recommendations
- Allowlist known backup servers
- Adjust thresholds based on environment size
- Document every tuning change
## Owner
Detection Engineering
## Response Playbook
Data Exfiltration Investigation Playbook
## Status
Testing
## Metrics
- False positive rate
- True positive rate
- Time spent tuning
- Review after two-week observation period
Bottom Line
A strong SIEM use case library helps enterprise security teams move from raw log collection to structured, measurable threat detection. The most useful libraries map rules to business risks, MITRE ATT&CK tactics and techniques, required log sources, detection logic, false positive scenarios, tuning notes, severity, ownership, and response playbooks.
Start with high-priority risks such as credential compromise, data exfiltration, lateral movement, persistence, privilege escalation, cloud security, and insider threat. Use Sigma rules and vendor content as starting points where appropriate, but customize them for your environment. Then test, tune, measure, and retire rules based on real alert quality—not rule count alone.
FAQ
What is a SIEM use case library?
A SIEM use case library is a structured catalog of detection rules and scenarios. It documents what each rule detects, which log sources it needs, how it maps to MITRE ATT&CK, what severity it should carry, which false positives are expected, and what response playbook analysts should follow.
How many SIEM use cases does a mature SOC need?
Hunto AI states there is no magic number, but most mature SOCs run 200 to 400 active use cases. The same guidance emphasizes that quality matters more than quantity; one accurate, well-tuned rule is more valuable than many noisy detections.
Should we use vendor-provided SIEM use cases or build custom rules?
Hunto AI recommends starting with vendor-provided content packs and industry-standard detections such as Sigma rules, then customizing them for your environment. A mix of vendor-provided, community, and custom detections is usually more practical than relying on only one source.
What should we do when a use case requires logs we do not have?
Document the use case as blocked by a log source gap. Hunto AI recommends using those blocked use cases as evidence for business cases to onboard new data sources, especially when one source would enable multiple high-value detections.
How do we measure SIEM detection effectiveness?
Track true positive rate, false positive rate, ATT&CK coverage, time from use case creation to production deployment, and tuning effort. Hunto AI also recommends purple team exercises where known techniques are executed and detection coverage is measured by ATT&CK technique.
What is Sigma, and why does it matter for a SIEM use case library?
Sigma is a vendor-agnostic detection rule format that can be converted to queries for most major SIEM platforms. SIEMUseCases.com suggests the Sigma ruleset as a useful starting point for teams deciding what to do with their SIEM logs, while Hunto AI notes that Sigma helps teams share detections across tools without rewriting every rule from scratch.
