AI Driven Cybersecurity Risk Scoring and Prioritization Workflow

Cybersecurity Risk Scoring and Prioritization

1. Risk Identification

1.1 Asset Inventory

Compile a comprehensive inventory of all assets within the energy and utilities sector, including hardware, software, and data repositories.

1.2 Threat Assessment

Utilize AI-driven threat intelligence platforms, such as IBM QRadar and CrowdStrike Falcon, to identify potential threats to the identified assets.

2. Risk Analysis

2.1 Vulnerability Scanning

Implement automated vulnerability scanning tools like Qualys and Tenable.io to assess the security posture of assets.

2.2 Risk Scoring

Apply AI algorithms to analyze vulnerabilities and assign risk scores based on potential impact and exploitability. Tools such as RiskLens can be utilized for quantitative risk analysis.

3. Risk Prioritization

3.1 Scoring Framework

Develop a scoring framework that incorporates factors such as asset criticality, vulnerability severity, and threat intelligence to prioritize risks effectively.

3.2 AI-Enhanced Decision Making

Leverage AI-driven analytics tools like Splunk or Darktrace to enhance decision-making processes in prioritizing risks based on real-time data and predictive analytics.

4. Risk Mitigation

4.1 Remediation Strategies

Formulate remediation strategies based on prioritized risks, utilizing AI tools for automated patch management and incident response, such as ServiceNow and IBM Resilient.

4.2 Continuous Monitoring

Implement continuous monitoring solutions powered by AI, such as Microsoft Sentinel, to ensure ongoing assessment and management of cybersecurity risks.

5. Reporting and Review

5.1 Risk Reporting

Generate comprehensive risk reports using AI-driven reporting tools that provide insights into risk levels and mitigation progress.

5.2 Review and Update

Establish a regular review process to update risk assessments and scoring based on evolving threats and changes in the asset landscape.