AI Driven Predictive Security Analytics and Risk Scoring Workflow

Predictive Security Analytics and Risk Scoring

1. Data Collection

1.1 Identify Data Sources

Gather data from various sources such as:

• Network logs
• Endpoint activity
• Threat intelligence feeds
• User behavior analytics

1.2 Data Ingestion

Utilize tools such as:

• Splunk for log management
• ELK Stack (Elasticsearch, Logstash, Kibana) for data visualization

2. Data Processing

2.1 Data Cleaning

Implement algorithms to clean and normalize data for consistency.

2.2 Data Enrichment

Enhance data with additional context using:

• Threat intelligence platforms (e.g., Recorded Future)
• External databases for user and device profiling

3. Risk Assessment

3.1 Develop Risk Models

Utilize machine learning algorithms to create risk scoring models based on historical data. Tools such as:

• IBM Watson for AI-driven insights
• Microsoft Azure Machine Learning for model training

3.2 Implement Predictive Analytics

Use predictive analytics to forecast potential security threats. Examples include:

• Darktrace for anomaly detection
• Cylance for proactive threat prevention

4. Monitoring and Alerting

4.1 Real-time Monitoring

Deploy AI-based monitoring tools to continuously analyze data streams. Consider:

• Security Information and Event Management (SIEM) systems like LogRhythm
• AI-driven intrusion detection systems (IDS) such as Vectra AI

4.2 Automated Alerts

Set up automated alerts for high-risk scores using:

• PagerDuty for incident management
• ServiceNow for workflow automation

5. Incident Response

5.1 Define Response Protocols

Establish protocols for responding to identified threats based on risk scores.

5.2 Leverage AI for Response Automation

Utilize AI tools to automate response actions, such as:

• Demisto for security orchestration
• Phantom for automated incident response

6. Continuous Improvement

6.1 Post-Incident Analysis

Conduct thorough analysis of incidents to improve predictive models.

6.2 Update Risk Models

Regularly refine risk scoring models based on new data and threat intelligence.

7. Reporting and Compliance

7.1 Generate Reports

Create detailed reports on risk assessments and incident responses for stakeholders.

7.2 Ensure Compliance

Utilize tools to ensure compliance with industry standards such as:

• GDPR
• PCI DSS