AI Driven Cybersecurity Workflow for Utility Network Protection

Cybersecurity Threat Detection in Utility Networks

1. Initial Assessment

1.1 Identify Critical Assets

Catalog all critical infrastructure components such as SCADA systems, communication networks, and data storage.

1.2 Risk Assessment

Conduct a thorough risk assessment to identify vulnerabilities within the utility network.

2. Data Collection

2.1 Implement AI Data Tools

Utilize AI-driven data tools to gather real-time data from various sources including:

• Network traffic analysis tools (e.g., Darktrace)
• Intrusion detection systems (e.g., Snort)
• Log management solutions (e.g., Splunk)

2.2 Data Aggregation

Aggregate data from different sources into a centralized platform for analysis.

3. Threat Detection

3.1 AI Model Development

Develop machine learning models to detect anomalies in network behavior.

3.2 Real-Time Monitoring

Implement AI-driven monitoring tools such as:

• IBM QRadar for security intelligence
• Cylance for endpoint protection
• Microsoft Azure Sentinel for cloud security

4. Incident Response

4.1 Automated Response Protocols

Establish automated response protocols using AI to mitigate threats immediately upon detection.

4.2 Human Oversight

Ensure a human response team is available for complex incidents that require expert analysis.

5. Continuous Improvement

5.1 Post-Incident Review

Conduct a thorough review of incidents to refine detection algorithms and response strategies.

5.2 Ongoing Training

Regularly train AI models with new data to improve accuracy and adapt to evolving threats.

6. Reporting and Compliance

6.1 Generate Reports

Utilize reporting tools to create detailed reports on threat detection and incident response.

6.2 Compliance Audits

Ensure compliance with industry regulations and standards such as NERC CIP and ISO 27001.