AI Driven Automated Threat Intelligence Workflow Explained

Automated Threat Intelligence Gathering and Analysis

1. Define Objectives and Scope

1.1 Identify Key Threat Vectors

Determine the specific types of threats to focus on, such as malware, phishing, or insider threats.

1.2 Establish Data Sources

Identify relevant data sources, including open-source intelligence (OSINT), commercial threat feeds, and internal logs.

2. Data Collection

2.1 Utilize AI-Driven Tools for Data Aggregation

Implement AI tools such as Recorded Future or ThreatConnect to automate the collection of threat data from various sources.

2.2 Integrate with SIEM Systems

Utilize Security Information and Event Management (SIEM) tools like Splunk or IBM QRadar to gather and analyze security logs in real-time.

3. Data Processing and Normalization

3.1 Clean and Normalize Data

Use AI algorithms to clean and standardize incoming data for consistency and accuracy.

3.2 Enrichment of Data

Employ tools like VirusTotal or AlienVault OTX to enrich threat data with additional context.

4. Threat Analysis

4.1 Implement Machine Learning Models

Use machine learning algorithms to identify patterns and anomalies in the data. Tools such as Cylance or Darktrace can be employed for this purpose.

4.2 Risk Assessment

Utilize AI-driven risk assessment tools to evaluate the potential impact of identified threats on the organization.

5. Reporting and Visualization

5.1 Generate Automated Reports

Employ reporting tools like Tableau or Power BI to create visual representations of threat intelligence findings.

5.2 Share Insights with Stakeholders

Disseminate reports to relevant stakeholders through automated email alerts or dashboards.

6. Continuous Improvement

6.1 Feedback Loop

Establish a feedback mechanism to refine AI models based on the effectiveness of threat detection and response.

6.2 Update Data Sources and Tools

Regularly review and update data sources and tools to ensure the threat intelligence process remains current and effective.