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Introduction:
The cybersecurity landscape is rapidly evolving with AI integration, creating distinct categories of tools designed for specific phases of security operations. While AI Security Operations Centers (SOCs) excel at high-volume alert triage, next-generation platforms like Spacewalk AI are emerging to revolutionize the critical incident response phase, moving beyond classification to empower human-led investigation.
Learning Objectives:
- Understand the fundamental operational difference between AI-driven alert classification and AI-assisted incident response.
- Learn key technical commands for initial triage and deep-dive investigation across Linux and Windows environments.
- Gain practical knowledge for integrating AI tools into existing SOC workflows for enhanced threat hunting and hypothesis testing.
You Should Know:
1. Initial Triage with AI SOC Tools
AI SOC platforms typically ingest alerts from EDR/XDR systems. The initial filtering often relies on querying security data lakes.
Example KQL query for Microsoft Sentinel to identify high-volume alert types SecurityAlert | where TimeGenerated > ago(24h) | summarize AlertCount=count() by AlertName, Severity | order by AlertCount desc
This KQL query helps security analysts quickly identify the most frequent alert types in their environment, allowing them to prioritize which alerts warrant deeper investigation. The AI SOC would use machine learning to automatically surface these patterns and suggest prioritization.
2. Endpoint Investigation Commands
When an alert escalates to incident status, responders need immediate endpoint visibility.
Linux process and network examination ps aux --sort=-%mem | head -20 Top memory-consuming processes lsof -i -P -n | grep LISTEN All listening ports and associated processes ss -tulwn Socket statistics with numeric ports netstat -tulnp Traditional network status (if ss unavailable)
These commands provide critical visibility into running processes and network connections on potentially compromised Linux systems. The `ps` command identifies resource-intensive processes, while `lsof` and `ss` reveal what network services are active and which processes are responsible.
3. Windows Forensic Data Collection
Windows environments require specific commands for rapid evidence gathering.
PowerShell commands for incident response
Get-Process | Sort-Object WS -Descending | Select-Object -First 20
Get-NetTCPConnection | Where-Object {$_.State -eq 'Listen'}
Get-WinEvent -FilterHashtable @{LogName='Security';ID=4624,4625} -MaxEvents 20
These PowerShell commands mirror the Linux functionality for identifying resource-intensive processes, examining listening ports, and reviewing recent authentication events. The Get-WinEvent command specifically looks for successful (4624) and failed (4625) logins within the Security log.
4. Memory Acquisition for Analysis
Preserving volatile memory is crucial for advanced attack investigation.
Using WinPmem for Windows memory acquisition winpmem_minimal_x64_rc2.exe --output memory.raw Using LiME for Linux memory acquisition insmod lime-$(uname -r).ko "path=/tmp/memory.raw format=raw"
Memory acquisition creates a forensic copy of RAM contents, preserving running processes, network connections, and other volatile artifacts that would be lost during shutdown. These commands use respected tools (WinPmem for Windows, LiME for Linux) to dump memory to disk for later analysis with tools like Volatility or Rekall.
5. Hypothesis Testing with Network Analysis
During incident response, testing connectivity hypotheses is essential.
Testing network hypotheses from Linux
curl -s -o /dev/null -w "%{http_code}" https://suspicious-domain.com/api
timeout 5 telnet suspicious-ip 443
tcpdump -i any -w packet_capture.pcap host suspicious-ip
These commands help validate whether systems can communicate with suspected malicious domains or IPs. The curl command checks HTTP response codes, telnet tests basic TCP connectivity, and tcpdump captures packets for deeper analysis of communication patterns.
6. Timeline Creation for Investigation
Building event timelines is critical for understanding attack progression.
Linux system timeline creation find / -type f -printf "%T+ %p\n" 2>/dev/null | sort > file_timeline.txt last -a | head -20 > recent_logins.txt journalctl --since "2023-10-01" --until "2023-10-02" > system_events.log
These commands generate timelines of file modifications, user logins, and system events that help reconstruct attacker activities. The find command with printf timestamps all files, last shows authentication history, and journalctl extracts systemd journal entries for specific timeframes.
7. Automated Response Integration
SOAR platforms enable automated containment during high-volume incidents.
Example TheHive Cortex analyzer example for automated containment
{
"name": "Isolate_Endpoint",
"description": "Isolate compromised endpoint via EDR API",
"url": "https://edr-platform.com/api/v1/contain",
"method": "POST",
"headers": {
"Authorization": "Bearer ${EDR_API_KEY}"
},
"payload": {
"endpoint_id": "${artifact.value}"
}
}
This JSON template represents how SOAR platforms like TheHive or Cortex can automate containment actions through EDR API integrations. When integrated with AI SOC platforms, these automated responses can quickly contain threats that meet specific confidence thresholds.
What Undercode Say:
- AI SOC platforms optimize alert fatigue management but don’t replace deep investigation capabilities
- The real innovation gap exists in the incident response phase where human-AI collaboration creates maximum impact
- Future security platforms must seamlessly transition from automated triage to assisted investigation
The distinction between AI SOC and Spacewalk AI represents a maturation in cybersecurity product categories. While AI SOC solutions address the critical problem of alert overload through improved classification, they fundamentally operate in the pre-incident space. The emerging category exemplified by Spacewalk AI recognizes that once a serious incident is declared, the workflow changes dramatically—from classification to collaborative investigation. This evolution mirrors the natural specialization occurring across the cybersecurity industry, where different AI solutions address distinct phases of the security operations lifecycle. The most effective security programs will integrate both types of solutions, using each where they provide maximum value.
Prediction:
Within three years, AI-assisted investigation platforms will become standard in enterprise security stacks, reducing mean time to resolution (MTTR) for critical incidents by over 60%. The integration between AI SOC triage systems and AI investigation platforms will become seamless, with automated handoff mechanisms that preserve context and initial findings. This evolution will fundamentally change SOC team compositions, with greater emphasis on forensic specialists and hypothesis-driven analysts while reducing needs for tier-1 alert reviewers.
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IT/Security Reporter URL:
Reported By: Chris – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
