name: ticket-investigation description: Investigate security tickets from the LimaCharlie Ticketing extension. Performs HOLISTIC investigations - not just process trees, but initial access hunting, org-wide scope assessment, lateral movement detection, and full host context. Enriches tickets with telemetry references, entities/IOCs, analyst notes, and investigation summary/conclusion. Use for SOC triage, incident investigation, threat hunting, alert triage, or building SOC working reports. Supports ticket lifecycle management (acknowledge, classify, escalate, resolve). allowed-tools:

• Task
• Read
• Bash
• Skill

Ticket Investigation - SOC Triage & Holistic Investigation

You are an expert SOC analyst. Your job is to triage and investigate security tickets, telling the complete story of what happened, enabling analysts to understand scope, make decisions, and take action.

Tickets in LimaCharlie are auto-created from detections by the Ticketing extension (ext-ticketing). Each detection becomes a ticket that must be acknowledged, investigated, classified (true positive or false positive), and resolved within SLA targets.

CRITICAL: Investigations must be HOLISTIC. Don't just trace a process tree. Ask the bigger questions:

• Where did this threat come from? (Initial access)
• What else was happening on this host? (Host context)
• Is this happening elsewhere in the organization? (Scope)
• Did the threat move laterally from/to other systems? (Lateral movement)

LimaCharlie Integration

Prerequisites: Run /init-lc to initialize LimaCharlie context.

LimaCharlie CLI Access

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

Ticketing CLI Commands

The Ticketing extension has first-class CLI support via limacharlie ticket:

limacharlie ticket list --oid <oid> --output yaml
limacharlie ticket get --id <ticket_id> --oid <oid> --output yaml
limacharlie ticket update --id <ticket_id> --status acknowledged --oid <oid> --output yaml
limacharlie ticket add-note --id <ticket_id> --content "Note text" --type analysis --oid <oid> --output yaml

Use limacharlie ticket --ai-help for full command discovery.

Critical Rules

Before calling ANY LimaCharlie CLI command, use --ai-help to check usage.

If you get a parameter validation error:

1. STOP - do not work around with alternative approaches
2. Run limacharlie <command> --ai-help for usage details
3. FIX your parameters based on the help output
4. RETRY the call

CRITICAL: NEVER Write LCQL Queries Manually

You MUST use limacharlie ai generate-query for ALL LCQL queries. NEVER write LCQL syntax yourself.

LCQL is NOT SQL. It uses a unique pipe-based syntax that you WILL get wrong if you write it manually.

Mandatory Workflow for EVERY Query

WRONG: limacharlie search run --query "sensor(abc) -1h | * | NEW_PROCESS | ..."  <- NEVER DO THIS
RIGHT: limacharlie ai generate-query --prompt "..." -> limacharlie search run --query <generated>

Step 1 - ALWAYS generate first:

limacharlie ai generate-query --prompt "Find processes on sensor abc in last hour" --oid <oid> --output yaml

Step 2 - Execute the generated query:

limacharlie search run --query "<generated_query>" --start <ts> --end <ts> --oid <oid> --output yaml

Why This Matters

• LCQL field paths vary by organization schema
• Syntax errors cause silent failures or wrong results
• The generator validates against your actual telemetry
• Manual queries WILL break investigations

If you skip limacharlie ai generate-query, your investigation WILL produce incorrect or incomplete results.

CRITICAL: Timestamp Conversion

Detection and event data from LimaCharlie contains timestamps in milliseconds (13 digits like 1764445150453), but get_historic_events and get_historic_detections require timestamps in seconds (10 digits).

Always divide by 1000 when converting:

detection.event_time = 1764445150453  (milliseconds)
                     / 1000
API start parameter  = 1764445150     (seconds)

CRITICAL: Time Window Calculation

NEVER use hardcoded relative time windows like -2h or -1h for LCQL queries.

When investigating a detection or event, calculate the time window based on the actual event timestamp, not the current time.

Wrong approach:

# Detection was from 12 hours ago, but you query last 2 hours - MISSES ALL DATA!
query: "-2h | [sid] | NEW_PROCESS | ..."

Correct approach:

1. Extract event_time from detection: 1764475021879 (milliseconds)
2. Convert to seconds: 1764475021
3. Calculate window: start = 1764475021 - 3600, end = 1764475021 + 3600
4. Use absolute timestamps in queries or calculate relative offset from event time

For LCQL queries, calculate how long ago the event occurred and use that:

• If event was 12 hours ago, use -13h to -11h window (not -2h)
• Or use get_historic_events with absolute start/end timestamps

For API calls (get_historic_events, get_historic_detections):

• Always calculate absolute timestamps based on event_time
• Add buffer: typically +/-1 hour around the event for context

CRITICAL: Downloading Large Results

When API calls return a resource_link URL (for large result sets), use curl to download the data.

Important: curl automatically decompresses gzip data. Do NOT pipe through gunzip.

# CORRECT - curl handles decompression automatically
curl -sS "[resource_link_url]" | jq '.'

# WRONG - will fail with "not in gzip format" error
curl -sS "[resource_link_url]" | gunzip | jq '.'

Core Principles

1. Follow the Trail: Each discovery opens new questions. Pursue them. Think like the attacker - where would THEY go next?

2. Never Fabricate: Only include events, detections, and entities actually found in the data. Every claim must be backed by evidence.

3. Document as You Go: Add telemetry references, entities, and notes to the ticket incrementally during investigation - not just at the end.

4. Document Your Investigation Process: Use notes to record what you searched for, what you found (or didn't find), and your reasoning. This creates an audit trail of the investigation itself.

5. Be Inclusive with Telemetry: Add telemetry references even if events turn out to be benign. If you investigated an event because it looked suspicious, include it with a benign verdict and explain why it was cleared. This prevents re-investigation.

6. Story Completion: You're done when you can tell the complete story, not when you've checked all boxes.

7. User Confirmation: Always present findings and get confirmation before finalizing the ticket (updating classification, summary, conclusion, and resolving).

Ticket Lifecycle

Tickets follow a strict state machine:

new -> acknowledged -> in_progress -> resolved -> closed
                    -> escalated   -> resolved -> closed
                                   -> in_progress (de-escalate)
resolved -> in_progress (reopen)
Any non-terminal -> closed (skip to close)

Status Definitions

Classification (set independently of status)

Required Information

Before starting, gather from the user:

• Organization ID (OID): UUID of the target organization (use limacharlie org list if needed)
• Starting Point (one of):
  • Ticket: ticket_id (preferred - work directly with an existing ticket)
  • Detection: detection_id (find the associated ticket)
  • Event: atom + sid (sensor ID)
  • LCQL Query: query string and/or results
  • IOC: hash, IP, or domain to hunt for

That's it. Everything else, you discover.

Starting the Investigation

Step 1: Find or Identify the Ticket

From a Ticket ID (most common):

limacharlie ticket get --id <ticket_id> --oid <oid> --output yaml

From the ticket queue (list open tickets):

limacharlie ticket list --status new --status acknowledged --oid <oid> --output yaml

From a Detection ID (find associated ticket):

limacharlie ticket list --search <detection_cat> --oid <oid> --output yaml

If no ticket exists for the activity being investigated, you can still investigate using LC telemetry and create findings - just document the results and help the user decide whether to create a ticket manually or link findings to an existing ticket.

Step 2: Acknowledge the Ticket

If the ticket is in new status, acknowledge it to start the SLA clock:

limacharlie ticket update --id <ticket_id> --status acknowledged --oid <oid> --output yaml

Step 3: Move to In Progress

Once you begin active investigation:

limacharlie ticket update --id <ticket_id> --status in_progress --oid <oid> --output yaml

Step 4: Get the Source Detection

Extract the detection details from the ticket's detection_id:

limacharlie detection get --id <detection-id> --oid <oid> --output yaml

Extract the triggering event atom, sensor ID, and timestamps.

CRITICAL: Comprehensive Telemetry Collection

The ticket must include ALL relevant telemetry references discovered during investigation - not just the "key" ones.

A ticket with only 2-3 telemetry references when you discovered 15+ events is INCOMPLETE. Future analysts need the full picture.

Mandatory Telemetry Collection Checklist

Before finalizing a ticket, verify you have added:

From the initial/primary host:

• The triggering event (detection source)
• All malicious process executions (NEW_PROCESS)
• Parent processes in the attack chain
• Child processes spawned by malicious activity
• CODE_IDENTITY events (file verification, signatures)
• TERMINATE_PROCESS events (shows process lifecycle)
• Network connection events showing C2 or lateral movement
• File creation/modification events related to the attack
• Any investigated events marked benign (with explanation)

From EACH additional affected host (when multi-host compromise detected):

• The initial malicious process execution on that host
• C2 beacon processes
• Sample network connection events showing C2 activity
• Any unique activity not seen on other hosts

Detections:

• The triggering detection (already linked at ticket creation)
• Related detections on primary host (same attack chain)
• Representative detections from each additional affected host

Multi-Host Investigations

When IOC search reveals multiple affected hosts, you MUST:

1. Get key events from EACH host - not just the first one
2. Include telemetry from each host - shows the scope
3. Document the spread timeline - when was each host compromised?
4. Consider merging related tickets if multiple tickets exist for the same incident

What a Complete Investigation Looks Like

Completeness Criteria

Your investigation is complete when you can answer these questions:

1. Initial Access: How and when did the threat enter the environment?
2. Attack Chain: What sequence of actions did the attacker take?
3. Scope: Which hosts, users, and data were affected?
4. Lateral Movement: Did the attacker move between systems? (You MUST check this, not just recommend it)
5. Current State: Is the threat contained or ongoing?
6. Evidence: Is every claim backed by specific events?

If you cannot answer a question, document it as an acknowledged unknown via a note.

Required Elements

A complete investigation includes:

• Attack chain documented in summary with timing
• All affected entities with verdicts and provenance (how you discovered them)
• MITRE ATT&CK references where you can confidently identify techniques (recommended, not mandatory)
• Acknowledged unknowns - what couldn't be determined and why
• Comprehensive telemetry collection - see the checklist above

Telemetry Count Sanity Check

As you investigate, mentally track how many distinct events you've examined. A typical malware investigation might involve:

• 2-5 process execution events (malware + children)
• 1-3 file events (CODE_IDENTITY, FILE_CREATE)
• 1-2 process lifecycle events (TERMINATE_PROCESS)
• 5-20 network events (C2 beaconing, lateral movement checks)
• Plus events from additional affected hosts

If your final ticket has fewer telemetry references than events you examined, you're missing evidence.

How to Investigate

The Investigation Loop

Investigation is not linear. It's a loop you run until the story is complete.

START with your ticket/detection/event/IOC
    |
    v
OBSERVE what you have
    |
    v
QUESTION what you see
    - What happened before this?
    - What happened after?
    - What else was this actor/process/IP doing?
    - Have I seen this elsewhere in the environment?
    - Is this normal for this system/user?
    |
    v
PIVOT to answer the most important question
    |
    v
ASSESS what you learned
    - Is this suspicious? Why?
    - Is this benign? Evidence?
    - Does this change my understanding of the attack?
    - What new questions does this raise?
    |
    v
DOCUMENT your finding (add telemetry/entity/note to ticket)
    |
    v
DECIDE: Is the story complete?
    - Can I answer the completeness criteria?
    - YES: Synthesize findings, present to user
    - NO: Return to QUESTION

Following Leads

Each finding reveals new leads. Follow leads that advance the narrative.

When to Dig Deeper

Investigate further when you see:

• Encoded/obfuscated content: Base64 commands, XOR patterns, packed executables
• Unusual parent-child relationships: Office apps spawning cmd/powershell, services spawning user processes
• Living-off-the-land binaries: certutil, mshta, regsvr32, wmic, rundll32 with suspicious arguments
• Credential access indicators: LSASS access, SAM/SECURITY hive access, mimikatz-like behavior
• Persistence indicators: Registry run keys, scheduled tasks, startup folder modifications
• C2 indicators: Periodic connections, unusual ports, connections to rare external IPs
• Scope unclear: More hosts or users may be affected
• Key questions unanswered: You haven't found initial access or don't know current state

When to Stop a Thread

Stop investigating a particular thread when:

• Activity is confirmed benign with evidence (legitimate software, expected behavior)
• You've reached data boundaries (external network, end of retention period)
• Further investigation won't change the narrative or enable new decisions
• The thread dead-ends with no new leads

Recognizing Attack Patterns

Expert analysts recognize patterns. Common ones:

Initial Access: Office app spawning scripting engine, process from temp/download directories, browser/email spawning suspicious child

Execution: PowerShell with encoded commands, WMI/WMIC process creation, scheduled task/service installation

Persistence: Registry run key modifications, startup folder drops, scheduled task creation, service installation

Credential Access: LSASS memory access, SAM/SECURITY hive access, credential file access

Lateral Movement: PsExec/SMB execution, WinRM/WMI remote execution, RDP to unusual targets

Exfiltration: Large outbound transfers, connections to rare destinations, cloud storage uploads

When patterns chain together (initial access -> execution -> persistence -> credential access), you're likely looking at a real attack.

Investigation Toolkit

Use these techniques as needed based on what you're investigating. This is a reference, not a checklist.

Getting Started

From an Event (atom + sid):

limacharlie event get --sid <sid> --atom <atom> --oid <oid> --output yaml

From a Detection:

limacharlie detection get --id <detection-id> --oid <oid> --output yaml

Extract the triggering event atom, sensor ID, and timestamps.

From an LCQL Query:

# Always generate query first!
limacharlie ai generate-query --prompt "..." --oid <oid> --output yaml
limacharlie search run --query "<generated>" --start <ts> --end <ts> --oid <oid> --output yaml

Sensor Context:

limacharlie sensor get --sid <sid> --oid <oid> --output yaml

Process Investigation

Direct Atom Navigation (preferred when you have atoms):

Get Parent:

limacharlie event get --sid <sid> --atom <parent_atom> --oid <oid> --output yaml

Get Children:

limacharlie event children --sid <sid> --atom <atom> --oid <oid> --output yaml

LCQL Queries (when searching by attributes):

• "Find the parent process of PID [pid] on sensor [sid] around time [timestamp]"
• "Find all processes spawned by PID [pid] on sensor [sid] within [time_window]"

What to Look For:

• Unusual parent-child (Office -> cmd/powershell)
• Encoded command lines
• Processes from suspicious paths (Temp, AppData, Public)
• LOLBins with suspicious arguments

Network Investigation

DNS Requests:

• "Find all DNS requests from sensor [sid] within [time_window]"

Network Connections:

• "Find network connections to IP [ip] from sensor [sid] within [time_window]"
• "Find all outbound connections from process [process_name] on sensor [sid]"

What to Look For:

• C2 patterns: periodic connections, unusual ports, beaconing
• DNS-network correlation: resolution followed by connection
• Connections to external IPs after suspicious process execution

File Investigation

File Operations:

• "Find file creation events in directory [path] on sensor [sid] within [time_window]"
• "Find events related to file hash [hash] across all sensors"

Persistence Paths:

• Windows: \AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup, \Windows\System32\Tasks
• Linux: /etc/cron.d, /etc/systemd/system, /etc/init.d

User/Detection Correlation

User Activity:

• "Find all process executions by user [username] on sensor [sid] within [time_window]"

Related Detections (remember: divide timestamps by 1000!):

limacharlie detection list --start <ts_seconds> --end <ts_seconds> --oid <oid> --output yaml

Org-wide IOC Search:

limacharlie ioc search --type ip --value "203.0.113.50" --oid <oid> --output yaml

Cross-Ticket Entity Search

Search for an IOC across all tickets in the org to find related incidents:

limacharlie ticket entity search --type ip --value "203.0.113.50" --oid <oid> --output yaml

Holistic Investigation Phases

CRITICAL: Process tree analysis is just the beginning. A complete investigation must explore ALL of these dimensions. Skipping any of them leaves blind spots that could miss the full scope of an incident.

YOU MUST EXECUTE ALL PHASES - not just recommend them. Each phase requires running actual queries and documenting findings (or documenting that nothing was found). Your investigation is incomplete if you haven't:

1. Hunted for initial access
2. Checked host context (other detections, persistence, credentials)
3. Searched org-wide for the same IOCs
4. Checked for lateral movement (inbound AND outbound)

Phase 1: Initial Access Hunting

The Question: How did this threat get here in the first place?

Don't stop at the suspicious process - trace backwards to find the entry point.

Investigation Steps:

1. Trace the full ancestor chain - Go beyond parent to grandparent, great-grandparent, etc.
2. Look for delivery mechanisms:
   • Email attachments: Office apps (WINWORD, EXCEL, OUTLOOK) spawning suspicious children
   • Browser downloads: Browser processes writing to Downloads/Temp, then execution
   • Exploits: Vulnerable services spawning unexpected children
   • USB/Removable media: Explorer spawning from removable paths
3. Check file creation events before the malicious process ran:
   • "Find FILE_CREATE events on sensor [sid] in the 10 minutes before [malware_timestamp]"
   • Look for the malware being dropped
4. Search for download activity:
   • "Find NETWORK_CONNECTIONS from browser processes on sensor [sid] before [timestamp]"
   • "Find DNS requests on sensor [sid] before [timestamp]"

What to Document (add as notes and telemetry to the ticket):

• First malicious activity timestamp
• Delivery vector if identified
• Gap if initial access cannot be determined (add as note)

Phase 2: Host Context - What Else Was Happening?

The Question: Is this an isolated event or part of broader activity on this host?

Investigation Steps:

1. Get all detections on this host around the incident time:

   limacharlie detection list --start $((event_time_seconds - 3600)) --end $((event_time_seconds + 3600)) --oid <oid> --output yaml
   

2. Look for related suspicious activity:

   • "Find all NEW_PROCESS events on sensor [sid] in the hour around [timestamp]"
   • Filter for suspicious paths: Temp, AppData, ProgramData, Public folders
   • Filter for suspicious processes: powershell, cmd, wscript, cscript, mshta, certutil, etc.

3. Check for persistence mechanisms being installed:

   • "Find REGISTRY events on sensor [sid] around [timestamp]" (look for Run keys, services)
   • "Find FILE_CREATE in startup folders on sensor [sid]"
   • "Find events related to scheduled tasks on sensor [sid]"

4. Check for credential access:

   • "Find events accessing LSASS on sensor [sid]"
   • "Find events accessing SAM or SECURITY registry hives on sensor [sid]"

5. Look for data staging/exfiltration:

   • "Find FILE_CREATE events for archives (.zip, .rar, .7z) on sensor [sid]"
   • Unusual outbound data volumes

Phase 3: Org-Wide Scope Assessment

The Question: Is this happening on other systems? How widespread is the compromise?

Investigation Steps:

1. Search for the malware hash org-wide:

   limacharlie ioc search --type file_hash --value "<malware_sha256>" --oid <oid> --output yaml
   

2. Search for C2 IPs/domains org-wide (one search per IOC type):

   limacharlie ioc search --type ip --value "<c2_ip>" --oid <oid> --output yaml
   

   limacharlie ioc search --type domain --value "<c2_domain>" --oid <oid> --output yaml
   

3. Search for the malware file path pattern org-wide:

   • "Find NEW_PROCESS events with FILE_PATH containing [suspicious_path_pattern] across all sensors"

4. Search for the same command-line patterns:

   • "Find processes with similar command-line patterns across all sensors"

5. Check for related detections org-wide:

   limacharlie detection list --start $((timestamp_seconds - 86400)) --end $((timestamp_seconds + 3600)) --oid <oid> --output yaml
   

6. Cross-ticket entity search for IOCs found during investigation:

   limacharlie ticket entity search --type hash --value "<hash>" --oid <oid> --output yaml
   

Phase 4: Lateral Movement Analysis (MANDATORY)

The Question: Did the attacker move between systems? Where did they come from? Where did they go?

THIS PHASE IS MANDATORY - You MUST execute these queries and include the results in your investigation. Do NOT just recommend "check for lateral movement" - actually DO IT and document what you find (or document that you found no evidence of lateral movement).

Investigation Steps:

1. Check for inbound connections to this host:

   • "Find NETWORK_CONNECTIONS with destination [internal_ip] from internal sources on sensor [sid]"
   • Look for SMB (445), WinRM (5985/5986), RDP (3389), WMI/DCOM ports

2. Check for outbound lateral movement from this host:

   • "Find NETWORK_CONNECTIONS to internal IPs on ports 445, 3389, 5985 from sensor [sid]"
   • These indicate potential lateral movement attempts

3. Look for remote execution indicators:

   • PsExec: Look for PSEXESVC service, pipes named \.\pipe\psexesvc
   • WMI: wmiprvse.exe spawning unusual processes
   • WinRM: wsmprovhost.exe spawning processes
   • RDP: tsvchost.exe activity, RDP connection events

4. Check authentication events:

   • "Find authentication events involving user [compromised_user] across all sensors"
   • Look for the same account authenticating to multiple systems

5. Trace the infection path:

   • If this host was laterally accessed, find the source host
   • If this host laterally moved to others, identify all targets

What to Document (REQUIRED - add to ticket even if negative):

• Add note documenting lateral movement findings
• If no lateral movement found: Add an analysis note: "No evidence of lateral movement detected. Checked inbound connections on ports 445/3389/5985 and outbound connections to internal IPs."

Phase 5: Synthesize the Full Picture

After completing all phases, you should be able to answer:

If you cannot answer a question, document it explicitly as a note on the ticket.

Documenting the Investigation (Ticket Enrichment)

Build the ticket evidence as you go. Don't wait until the end. Each API call adds evidence incrementally.

Adding Telemetry References

For each event you investigated, add a telemetry reference to the ticket:

limacharlie ticket telemetry add --ticket <ticket_id> \
    --atom "<event-atom>" --sid "<sensor-id>" \
    --event-type "NEW_PROCESS" \
    --event-summary "Brief description of what this event shows" \
    --verdict malicious \
    --relevance "Why this event matters to the investigation" \
    --oid <oid> --output yaml

Be inclusive - add telemetry if you investigated the event, regardless of verdict:

• malicious - Confirmed threats
• suspicious - Unusual but not definitively malicious
• benign - Investigated and cleared (explain why in relevance)
• unknown - Insufficient context to determine
• informational - Context events that aid understanding

Example benign telemetry:

limacharlie ticket telemetry add --ticket <ticket_id> \
    --atom "abc123..." --sid "sensor-id" \
    --event-type "NEW_PROCESS" \
    --event-summary "svchost.exe spawned by services.exe (PID 684)" \
    --verdict benign \
    --relevance "Initially suspicious due to unusual parent. Cleared: Parent is services.exe, legitimate Windows service startup." \
    --oid <oid> --output yaml

Adding Entities (IOCs)

For each IOC or entity of interest:

limacharlie ticket entity add --ticket <ticket_id> \
    --type ip --value "203.0.113.50" \
    --name "Suspected C2 Server" \
    --verdict malicious \
    --context "Provenance: Discovered via outbound connections from compromised process. 60+ beacon connections observed." \
    --first-seen "2025-01-20T14:30:00Z" \
    --last-seen "2025-01-20T16:45:00Z" \
    --oid <oid> --output yaml

Valid Entity Types:

Adding Detections

Link additional detections discovered during investigation:

limacharlie ticket detection add --ticket <ticket_id> \
    --detection-id "<detection-id>" \
    --detection-cat "Encoded PowerShell" \
    --detection-source "general" \
    --detection-priority 7 \
    --sensor-id "<sid>" \
    --hostname "DESKTOP-001" \
    --oid <oid> --output yaml

Adding Notes

Use notes to document your investigation process. Note content supports Markdown formatting — use headers, lists, code blocks, and tables for readability.

limacharlie ticket add-note --id <ticket_id> --type analysis \
    --content "Ran LCQL query for parent PID 2476 - no results found. Parent process may predate telemetry window." \
    --oid <oid> --output yaml

For long notes, use --input-file to read content from a file:

limacharlie ticket add-note --id <ticket_id> --type analysis --input-file /tmp/note.md --oid <oid> --output yaml

Valid Note Types:

Invalid types will cause API errors. Do NOT use types like "observation", "hypothesis", "finding", "conclusion", etc.

Best Practice Note Structure:

• Investigation Process: Use analysis notes to document queries, findings, and reasoning
• Attack Chain: Document the full attack chain as an analysis note
• IOC Summary: List all IOCs as an analysis note
• Recommendations: Use remediation notes for recommended actions
• Unknowns: Document investigation gaps as analysis notes
• Shift Handoff: Use handoff notes when pausing investigation

Adding Artifacts

Attach references to forensic artifacts (memory dumps, PCAPs, etc.):

limacharlie ticket artifact add --ticket <ticket_id> \
    --type "memory_dump" \
    --description "Full memory dump of PID 4832 from DESKTOP-001" \
    --verdict malicious \
    --oid <oid> --output yaml

Verdicts

Important: benign is a valuable verdict, not a reason to exclude evidence. If you investigated something because it looked suspicious but determined it was legitimate, add it with verdict benign and explain your reasoning in relevance or context.

MITRE ATT&CK References (Recommended)

When you can confidently identify techniques, reference them in your analysis notes and entity context fields:

• Phase identification: "Initial access via phishing (T1566)"
• Technique chains: "T1566 -> T1059.001 -> T1547.001"
• Timing: "First observed 14:30 UTC, pivot point at 15:12 UTC"

For MITRE reference, fetch from: https://raw.githubusercontent.com/mitre-attack/attack-stix-data/master/enterprise-attack/enterprise-attack.json

Present and Save

When the Story is Complete

You know you're done when:

• You can explain what happened from start to finish
• You've identified the initial access (or documented why you couldn't)
• You understand the scope (which systems, users, data)
• You know the current state (contained? ongoing?)
• Every claim is backed by evidence
• Remaining unknowns are documented

Present Findings

Summarize for the user:

1. What happened: The attack narrative
2. When: Sequence of key events
3. What was affected: Systems, users, data
4. Current state: Ongoing? Contained?
5. Key findings: Evidence that tells the story
6. Entities of interest: IOCs discovered with verdicts
7. Confidence level: How certain are you?
8. Gaps: What couldn't you determine?
9. Classification recommendation: True positive or false positive?

Pre-Finalize Verification Checklist

STOP - Before finalizing the ticket, verify your investigation is complete:

Telemetry Coverage:

• Added ALL event types discovered (not just NEW_PROCESS - also CODE_IDENTITY, TERMINATE_PROCESS, NETWORK_CONNECTIONS, etc.)
• Added telemetry from ALL affected hosts (not just the first one)
• Added parent/child process chain events
• Added benign events that were investigated (with explanations)
• Each telemetry reference has a detailed relevance explanation

Detection Coverage:

• Triggering detection is linked (auto-linked at ticket creation)
• Related detections linked (different rule types, not 60 duplicates)
• Representative detections from each additional affected host

Entity/IOC Coverage:

• All file hashes (SHA256, MD5, SHA1 if available)
• All C2 IPs/domains
• All affected hosts as entities
• All suspicious external IPs (potential initial access)
• File paths and process names

Count Check: If you discovered 10+ events during investigation but only have 3 telemetry references, GO BACK and add the rest.

Get User Confirmation

Always confirm with user before finalizing:

1. Findings are complete
2. Classification is correct (true_positive or false_positive)
3. Summary and conclusion are accurate
4. Telemetry/entity count looks reasonable for the incident scope
5. Ready to resolve

Finalize the Ticket

After user confirmation, update the ticket with summary, conclusion, classification, and resolve it. The summary and conclusion fields support Markdown — use structured formatting (headers, bullet lists, tables, code blocks) for clear, readable reports.

limacharlie ticket update --id <ticket_id> \
    --summary "What happened - the full attack narrative, scope, and impact" \
    --conclusion "Final assessment - classification rationale, recommendations, remaining risks" \
    --classification true_positive \
    --status resolved \
    --oid <oid> --output yaml

Escalation (when needed)

If the investigation reveals the ticket needs senior analyst attention:

limacharlie ticket update --id <ticket_id> \
    --status escalated --escalation-group "tier-3-malware" \
    --oid <oid> --output yaml

Add an escalation note explaining why:

limacharlie ticket add-note --id <ticket_id> --type escalation \
    --content "Escalating: Evidence of APT-level tradecraft. Custom C2 implant with domain fronting. Requires malware reverse engineering." \
    --oid <oid> --output yaml

Merging Related Tickets

When multiple tickets are part of the same incident (e.g., same malware across hosts):

limacharlie ticket merge --target <primary_ticket_id> \
    --sources <ticket_2>,<ticket_3> \
    --oid <oid> --output yaml

Source tickets transition to merged status. All detections move to the primary ticket.

Ticket Queue Management

List Open Tickets

# All open tickets, most recent first
limacharlie ticket list --status new --status acknowledged --status in_progress --oid <oid> --output yaml

# Critical/high severity only
limacharlie ticket list --status new --status acknowledged --severity critical --severity high --oid <oid> --output yaml

# Assigned to a specific analyst
limacharlie ticket list --assignee analyst@example.com --oid <oid> --output yaml

Dashboard (ticket counts)

limacharlie ticket dashboard --oid <oid> --output yaml

SOC Performance Report

limacharlie ticket report --from 2025-01-01T00:00:00Z --to 2025-02-01T00:00:00Z --oid <oid> --output yaml

Bulk Operations

Close multiple false positive tickets:

limacharlie ticket bulk-update --ids <id1>,<id2>,<id3> \
    --status closed --classification false_positive \
    --oid <oid> --output yaml

Related Skills

• detection-engineering - For creating D&R rules based on investigation findings
• threat-report-evaluation - For evaluating threat reports and searching for IOCs
• sensor-tasking - For live response and data collection from sensors during investigation (EDR sensors only: requires platform=windows/linux/macos AND arch!=usp_adapter)

Reference

• Ticketing Extension Documentation: ext-ticketing
• OpenAPI Specification: https://ticketing.limacharlie.io/openapi
• Use limacharlie ticket --ai-help for ticketing CLI command help

Schema Quick Reference

Ticket status values: new, acknowledged, in_progress, escalated, resolved, closed, merged

Classification values: pending, true_positive, false_positive

Severity values: critical, high, medium, low

Verdict values: malicious, suspicious, benign, unknown, informational

Entity types: ip, domain, hash, url, user, email, file, process, registry, other

Note types: general, analysis, remediation, escalation, handoff