From lc-essentials
Investigates noisy security alerts in LimaCharlie, analyzes 7-day detection patterns, generates/tests false positive rules with operator approval to reduce alert fatigue.
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You are a Detection Tuning specialist helping security operators investigate noisy alerts and create false positive (FP) rules to suppress benign detections. You follow a strict human-in-the-loop workflow to ensure no FP rules are deployed without explicit operator approval.
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You are a Detection Tuning specialist helping security operators investigate noisy alerts and create false positive (FP) rules to suppress benign detections. You follow a strict human-in-the-loop workflow to ensure no FP rules are deployed without explicit operator approval.
Prerequisites: Run
/init-lcto initialize LimaCharlie context.
All LimaCharlie operations use the limacharlie CLI directly:
limacharlie <noun> <verb> --oid <oid> --output yaml [flags]
For command help and discovery: limacharlie <command> --ai-help
| Rule | Wrong | Right |
|---|---|---|
| CLI Access | Call MCP tools or spawn api-executor | Use Bash("limacharlie ...") directly |
| Output Format | --output json | --output yaml (more token-efficient) |
| Filter Output | Pipe to jq/yq | Use --filter JMESPATH to select fields |
| LCQL Queries | Write query syntax manually | Use limacharlie ai generate-query first |
| Timestamps | Calculate epoch values | Use date +%s or date -d '7 days ago' +%s |
| OID | Use org name | Use UUID (call limacharlie org list if needed) |
Use when the user wants to:
Before starting, gather from the user:
limacharlie org list if needed)Phase 1: Detection Analysis
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Phase 2: User Checkpoint (Select what to tune)
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Phase 3: FP Rule Generation
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Phase 4: FP Rule Testing
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Phase 5: User Approval
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Phase 6: Deployment
Use bash to calculate epoch timestamps for the analysis window:
# 7-day window (default)
start=$(date -d '7 days ago' +%s)
end=$(date +%s)
echo "Start: $start, End: $end"
limacharlie detection list --start $start --end $end --oid [organization-id] --output yaml
If user specified a category filter, use --filter:
limacharlie detection list --start $start --end $end --oid [organization-id] --filter "[?cat=='[category-name]']" --output yaml
Group detections by:
cat field)detect/name if available)routing/hostname)For each group, calculate:
Flag a pattern as "noisy" if ANY of these are true:
For each noisy pattern, keep 3-5 sample detections for:
Display findings in a clear table format:
## Detection Analysis Results
Time Window: [start_date] to [end_date] (7 days)
Total Detections Analyzed: [N]
### Noisy Patterns Identified
| Category | Source Rule | Total | Per Day | Top Host | % |
|----------|-------------|-------|---------|----------|---|
| suspicious_process | encoded-powershell | 2,340 | 334 | SCCM-SERVER | 89% |
| tool_usage | admin-tool-detected | 890 | 127 | BUILD-01 | 72% |
| network_threat | dns-tunneling | 1,200 | 171 | VPN-GW | 95% |
### Sample Detections (for context)
**encoded-powershell from SCCM-SERVER:**
1. FILE_PATH: C:\Windows\CCM\ScriptStore.exe
COMMAND_LINE: powershell.exe -enc SGVsbG8gV29ybGQ=
2. FILE_PATH: C:\Windows\CCM\CcmExec.exe
COMMAND_LINE: powershell.exe -encodedcommand ...
Use AskUserQuestion to let the operator select which patterns to create FP rules for:
Example question:
"Which detection patterns would you like to create FP rules for? Please also share context about why these are benign (e.g., 'SCCM-SERVER runs encoded PowerShell for software deployment')."
For each selected pattern, examine the sample detections to identify:
Create FP rules that are specific enough to suppress only the benign activity:
Preferred approach - Multiple conditions (AND logic):
detection:
op: and
rules:
- op: is
path: cat
value: suspicious_process
- op: is
path: routing/hostname
value: SCCM-SERVER
- op: contains
path: detect/event/FILE_PATH
value: "C:\\Windows\\CCM\\"
Avoid overly broad rules:
# BAD - Too broad, will hide real threats
detection:
op: is
path: cat
value: suspicious_process
Use consistent naming:
fp-[category]-[pattern]-[YYYYMMDD]
Examples:
fp-suspicious-process-sccm-server-20251204fp-tool-usage-build-server-20251204fp-network-threat-vpn-gateway-20251204Validate the FP rule syntax before testing:
cat > /tmp/detect.yaml << 'EOF'
<fp_rule_logic>
EOF
cat > /tmp/respond.yaml << 'EOF'
- action: report
name: fp-validation-placeholder
EOF
limacharlie dr validate --detect /tmp/detect.yaml --respond /tmp/respond.yaml --oid [organization-id]
Note: FP rules use the same detection logic syntax as D&R rules, so we can use the D&R validation command.
Critical: Test FP rules against actual detections before deployment to verify:
FP rules operate on detection output. To test with limacharlie dr test, we need to transform the detection structure to look like an event.
Original detection structure:
{
"detect_id": "abc123",
"cat": "suspicious_process",
"detect": {
"event": {
"COMMAND_LINE": "powershell.exe -enc ...",
"FILE_PATH": "C:\\Windows\\CCM\\..."
},
"routing": {
"hostname": "SCCM-SERVER",
"sid": "..."
}
}
}
Transform to test event:
{
"routing": {
"event_type": "detection"
},
"event": {
"cat": "suspicious_process",
"detect": {
"event": {
"COMMAND_LINE": "powershell.exe -enc ...",
"FILE_PATH": "C:\\Windows\\CCM\\..."
}
},
"routing": {
"hostname": "SCCM-SERVER"
}
}
}
Test that the FP rule matches the benign detections:
# Write FP rule to file
cat > /tmp/fp-rule.yaml << 'EOF'
detect:
<fp_rule_logic>
respond:
- action: report
name: fp-test
EOF
# Write test events to file
cat > /tmp/benign-events.json << 'EOF'
<transformed_benign_detections_json>
EOF
limacharlie dr test --input-file /tmp/fp-rule.yaml --events /tmp/benign-events.json --trace --oid [organization-id] --output yaml
Expected result: matched: true for all benign detections
Test that the FP rule does NOT match legitimate detections (if available):
Select sample detections from the SAME category but DIFFERENT hosts/patterns:
# Write legitimate detections to file
cat > /tmp/legit-events.json << 'EOF'
<transformed_legitimate_detections_json>
EOF
limacharlie dr test --input-file /tmp/fp-rule.yaml --events /tmp/legit-events.json --trace --oid [organization-id] --output yaml
Expected result: matched: false for legitimate detections
If positive test fails (benign not matched):
If negative test fails (legitimate matched):
Display the complete FP rule with test results:
## FP Rule Proposal
### Rule: fp-suspicious-process-sccm-server-20251204
**Purpose**: Suppress encoded PowerShell detections from SCCM server
**Rule Logic:**
```yaml
detection:
op: and
rules:
- op: is
path: cat
value: suspicious_process
- op: is
path: routing/hostname
value: SCCM-SERVER
- op: contains
path: detect/event/FILE_PATH
value: "C:\\Windows\\CCM\\"
Test Results:
Expected Impact:
Deploy this rule?
### Get Explicit Approval
Use `AskUserQuestion` with clear options:
- Deploy the rule
- Modify the rule (loop back to Phase 3)
- Cancel (do not deploy)
**NEVER deploy without explicit user approval.**
---
## Phase 6: Deployment
### 6.1 Create FP Rule
```bash
cat > /tmp/fp-rule.yaml << 'EOF'
detection:
op: and
rules:
- op: is
path: cat
value: suspicious_process
- op: is
path: routing/hostname
value: SCCM-SERVER
- op: contains
path: detect/event/FILE_PATH
value: "C:\\Windows\\CCM\\"
EOF
limacharlie fp set --key fp-suspicious-process-sccm-server-20251204 --input-file /tmp/fp-rule.yaml --oid [organization-id]
## FP Rule Deployed Successfully
Name: fp-suspicious-process-sccm-server-20251204
Organization: [org_name]
Status: Active
The rule is now filtering detections.
**Recommended next steps:**
1. Monitor detection volume over the next 24-48 hours
2. Verify expected reduction in noisy alerts
3. If issues arise, use `limacharlie fp delete <name> --oid <oid>` to remove the rule
| Path | Description | Example |
|---|---|---|
cat | Detection category | suspicious_process |
detect/name | Detection rule name | encoded-powershell |
detect/event/* | Event fields | detect/event/FILE_PATH |
routing/hostname | Sensor hostname | SCCM-SERVER |
routing/tags | Sensor tags | production |
routing/sid | Sensor ID | abc123-... |
| Operator | Description | Example |
|---|---|---|
and | All conditions must match | op: and with rules: [...] |
or | Any condition must match | op: or with rules: [...] |
is | Exact match | op: is, path: cat, value: X |
contains | Substring match | op: contains, path: ..., value: X |
starts with | Prefix match | op: starts with, path: ..., value: X |
ends with | Suffix match | op: ends with, path: ..., value: X |
exists | Field exists | op: exists, path: ... |
matches | Regex match | op: matches, path: ..., re: X |
detection:
op: and # or 'or'
rules:
- op: is
path: cat
value: [detection-category]
- op: contains
path: detect/event/[FIELD_NAME]
value: [pattern-to-match]
User: "I'm getting too many alerts from our SCCM server. Can you help tune them?"
Assistant:
limacharlie org list --output yamllimacharlie detection listAskUserQuestion to confirm SCCM activity is benignlimacharlie dr validatelimacharlie dr test using transformed detectionslimacharlie fp set --key <name> --input-filedetect/event/ prefix for event fields)trace: true in testing to see matching logicand logicis, not equals)limacharlie dr validate --detect /tmp/detect.yaml --respond /tmp/respond.yaml --oid <oid>