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Introduction:
A recent study by the Center for AI Safety has delivered a surprising verdict on the current state of AI automation, finding it can only complete 1.3% to 2.5% of complex remote work projects. For cybersecurity professionals, this data provides a crucial reality check against the hype, underscoring that human expertise in threat analysis, ethical decision-making, and complex system hardening remains irreplaceable in the current technological landscape.
Learning Objectives:
- Understand the current technical limitations of AI in automating complex security tasks.
- Learn essential commands and techniques that demonstrate skills AI cannot yet replicate.
- Develop a future-proof skill set focused on AI-augmented security operations.
You Should Know:
1. Threat Intelligence Correlation and Analysis
While AI can process data, correlating disparate threat intelligence sources requires human intuition and context.
Using MISP for threat intelligence correlation misp-search -s "CVE-2023-34362" Search for IOCs related to a specific CVE misp-import -p /path/to/stix2/file.json Import STIX2 formatted intelligence misp-tag -e <event_id> -t "credential-theft" Tag events for future correlation
This sequence demonstrates threat intelligence management using MISP (Malware Information Sharing Platform). Security analysts use these commands to search for specific indicators, import structured threat information, and tag events for pattern recognition – tasks requiring contextual understanding that current AI struggles to automate effectively.
2. Advanced Network Forensic Analysis
Network forensic investigation requires understanding attack patterns across multiple data sources.
Zeek/Bro log analysis for lateral movement detection
zeek-cut id.orig_h id.resp_h id.resp_p < conn.log | sort | uniq -c | sort -nr
tshark -r packet_capture.pcap -Y "dns.qry.name contains .internal" -T fields -e dns.qry.name
bro-cut < http.log | awk '{print $3}' | sort | uniq -c | sort -nr | head -10
These commands analyze network traffic for suspicious patterns. The first identifies frequent connections between hosts, the second extracts internal DNS queries that might indicate reconnaissance, and the third ranks HTTP user agents – all requiring interpretive skills to distinguish between normal and malicious activity.
3. Memory Forensic Malware Analysis
Memory analysis remains a highly specialized skill that resists automation due to its interpretive nature.
Volatility 3 framework for memory analysis vol -f memory.dump windows.info vol -f memory.dump windows.malfind vol -f memory.dump windows.cmdline vol -f memory.dump windows.netstat
This Volatility framework sequence extracts critical information from a memory dump: system information, potential malware injections, command line history, and network connections. Analyzing the output requires understanding of normal vs. anomalous process behavior and attack patterns that AI systems frequently misinterpret.
4. Cloud Security Configuration Auditing
Cloud infrastructure assessment requires nuanced understanding of business context and security requirements.
AWS Security Hub and Config auditing
aws configservice describe-config-rules --config-rule-names "required-tags"
aws securityhub get-findings --filters 'ProductFields={"ProviderName"="Security Hub"}'
aws iam generate-credential-report
aws guardduty list-detectors --region us-east-1
These AWS CLI commands audit cloud security posture by checking configuration rules, retrieving security findings, generating credential reports, and verifying GuardDuty deployment. Interpreting results requires understanding the organization’s specific risk tolerance and operational requirements.
5. API Security Testing and Validation
API security testing involves business logic understanding that current AI cannot reliably replicate.
OWASP ZAP API scanning
zap-cli quick-scan --self-contained --start-options '-config api.disablekey=true' http://localhost:8080
zap-cli active-scan http://localhost:8080/api/v1/users
zap-cli report -o api-security-report.html -f html
curl -H "Authorization: Bearer $token" -X POST http://api.target.com/v1/admin/users --data '{"role":"admin"}'
This API security workflow uses OWASP ZAP for automated scanning combined with manual business logic testing. The curl command tests for privilege escalation vulnerabilities – a test that requires understanding application-specific user roles and workflows.
6. Incident Response Containment Procedures
Incident response requires rapid, context-aware decision making under pressure.
Windows incident response containment
Get-Process | Where-Object {$<em>.ProcessName -like "suspicious"} | Stop-Process -Force
Get-NetTCPConnection | Where-Object {$</em>.RemoteAddress -like "192.168."} | Format-Table
Disable-LocalUser -Name "compromised_account"
Set-NetFirewallRule -DisplayName "Block_Malicious_IP" -RemoteAddress "203.0.113.17" -Enabled True
These PowerShell commands demonstrate rapid containment during a security incident: identifying and stopping suspicious processes, reviewing network connections, disabling compromised accounts, and updating firewall rules. The sequence requires understanding the incident context to avoid business disruption.
7. Advanced SIEM Query Development
Creating effective SIEM queries requires deep knowledge of both attack patterns and log sources.
Splunk SPL for detecting pass-the-hash attacks index=windows (EventCode=4624 LogonType=3) OR (EventCode=4624 LogonType=9) | transaction host, user maxspan=5m | search EventCode=4624 LogonType=3 | table host, user, _time
This Splunk Search Processing Language (SPL) query detects potential pass-the-hash attacks by correlating network (LogonType=3) and batch (LogonType=9) logins from the same host and user within a 5-minute window. Developing such queries requires understanding both the attack technique and the organization’s specific authentication patterns.
What Undercode Say:
- The 2.5% automation ceiling represents immediate tactical limitations, not long-term strategic reality
- Cybersecurity professionals should focus on developing AI-augmentation skills rather than fearing replacement
- The most vulnerable security roles are those focused on repetitive, predictable tasks already documented in playbooks
- AI’s current failure in complex project completion highlights the value of human problem-solving in novel attack scenarios
- Organizations investing in AI should prioritize tools that enhance analyst capabilities rather than replace decision-making
The study’s findings reveal a crucial window of opportunity for security professionals. While AI excels at processing volumes of data and identifying known patterns, it consistently fails at the contextual understanding, ethical reasoning, and creative problem-solving required for novel security challenges. The commands and techniques demonstrated here represent categories of work that require precisely the type of human judgment AI cannot yet replicate. Security teams should interpret these findings as a mandate to double down on developing advanced analytical, investigative, and strategic thinking skills that complement rather than compete with AI capabilities.
Prediction:
The current automation gap will persist for 3-5 years in cybersecurity’s complex decision-making domains, creating a bifurcated job market. Entry-level positions focused on alert triage and basic monitoring will face increasing automation pressure, while senior roles requiring threat hunting, incident response leadership, and security architecture will experience growing demand and compensation. Organizations that successfully navigate this transition will develop AI-augmented security operations centers where human expertise directs AI capabilities, creating a new paradigm of super-powered analysts rather than replaced personnel.
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IT/Security Reporter URL:
Reported By: Michael Tchuindjang – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
