AI Driven Network Security Threat Detection and Response Workflow

Network Security Threat Detection and Response Process

1. Threat Identification

1.1 Data Collection

Utilize AI-driven tools to aggregate data from various sources such as network traffic, user behavior, and system logs. Examples include:

• Splunk: For real-time data monitoring and analysis.
• Darktrace: Employs machine learning to detect anomalies in network traffic.

1.2 Threat Analysis

Implement AI algorithms to analyze collected data. This can include:

• IBM QRadar: Uses AI to correlate events and identify potential threats.
• Cylance: Leverages AI for endpoint security and threat prediction.

2. Threat Prioritization

2.1 Risk Assessment

Utilize AI tools to assess the severity and potential impact of identified threats. Tools may include:

• RiskLens: Provides quantitative risk assessments using AI-driven analytics.
• ServiceNow: Automates risk prioritization based on predefined criteria.

2.2 Alert Generation

Generate alerts based on threat severity. AI can assist in filtering false positives, using tools like:

• LogRhythm: AI-driven analytics to reduce alert fatigue.

3. Incident Response

3.1 Automated Response

Implement AI-based automated response mechanisms to contain threats. Examples include:

• Phantom: Automates incident response workflows.
• Demisto: Provides playbooks for automated threat containment.

3.2 Human Intervention

In cases where automated responses are insufficient, escalate to human security analysts for further investigation. AI can assist in:

• ThreatConnect: Providing contextual information to analysts for informed decision-making.

4. Post-Incident Analysis

4.1 Root Cause Analysis

Use AI tools to perform a thorough analysis of the incident to identify root causes. Tools may include:

• Palantir: Supports deep data analysis for uncovering underlying issues.

4.2 Reporting and Documentation

Document the incident and response actions taken, utilizing AI to generate comprehensive reports. Examples include:

• Siemplify: Automates reporting processes for incident handling.

5. Continuous Improvement

5.1 Feedback Loop

Implement a feedback loop to refine threat detection models using insights gained from incidents. AI tools can assist in:

• DataRobot: Enhances machine learning models based on historical incident data.

5.2 Training and Awareness

Utilize AI-driven training programs to enhance employee awareness of security threats. Tools may include:

• KnowBe4: Provides AI-based security awareness training.