Automated Threat Detection Workflow with AI Integration

Automated Threat Detection and Triage

1. Data Collection

1.1 Log Aggregation

Utilize tools such as Splunk or ELK Stack to aggregate logs from various sources including servers, applications, and network devices.

1.2 Network Traffic Monitoring

Implement network monitoring solutions like Wireshark or Darktrace to capture and analyze network traffic for anomalies.

2. Threat Detection

2.1 Anomaly Detection

Leverage AI-driven tools such as IBM QRadar or CrowdStrike Falcon which utilize machine learning algorithms to identify deviations from normal behavior.

2.2 Signature-Based Detection

Utilize traditional antivirus solutions enhanced with AI capabilities, like McAfee MVISION, to detect known threats through signature databases.

3. Threat Analysis

3.1 Automated Triage

Integrate AI systems like ServiceNow Security Incident Response to automate the triage process, categorizing threats based on severity and potential impact.

3.2 Contextual Analysis

Employ tools such as ThreatConnect to provide contextual intelligence, correlating threat indicators with existing vulnerabilities.

4. Incident Response

4.1 Automated Response Actions

Utilize SOAR (Security Orchestration, Automation, and Response) tools like Palo Alto Networks Cortex XSOAR to automate response actions based on predefined playbooks.

4.2 Human Oversight

Incorporate a human review process for high-severity incidents, allowing security analysts to validate automated responses and take necessary actions.

5. Continuous Improvement

5.1 Feedback Loop

Establish a feedback mechanism to continuously improve AI models by incorporating new threat data and analyst insights into the training process.

5.2 Performance Metrics

Monitor key performance indicators (KPIs) such as detection accuracy, response time, and incident resolution rates to assess the effectiveness of the automated threat detection system.