AI Driven Phishing Email Analysis and Classification Workflow

Intelligent Phishing Email Analysis and Classification

1. Email Ingestion

1.1 Data Collection

Utilize email gateways and APIs to collect incoming emails for analysis. Tools such as Microsoft Graph API or Google Workspace API can be employed to extract email data.

1.2 Preprocessing

Clean and preprocess the email data by removing unnecessary headers and formatting. Use Python libraries like Pandas and NLTK for data manipulation and text processing.

2. Feature Extraction

2.1 Text Analysis

Implement Natural Language Processing (NLP) techniques to extract features from the email body and subject line. Tools such as SpaCy or TensorFlow can be used for text feature extraction.

2.2 Metadata Analysis

Analyze email metadata, including sender information, timestamps, and attachment types. Use custom scripts or tools like Apache Tika for extracting metadata.

3. AI Model Development

3.1 Model Selection

Choose appropriate machine learning algorithms for classification, such as Random Forest, SVM, or Neural Networks. Libraries like Scikit-learn or Keras can facilitate model development.

3.2 Training the Model

Train the selected model using labeled datasets of phishing and legitimate emails. Utilize platforms like Google Cloud AI or Azure Machine Learning for scalable training processes.

3.3 Model Evaluation

Evaluate the model’s performance using metrics such as accuracy, precision, and recall. Tools like TensorBoard or MLflow can assist in tracking model performance.

4. Deployment

4.1 Integration with Email Systems

Integrate the trained model into existing email systems for real-time analysis. Use RESTful APIs to allow seamless communication between the email system and the AI model.

4.2 Continuous Learning

Implement a feedback loop where user reports of phishing attempts are fed back into the model for continuous improvement. Tools like Apache Kafka can facilitate real-time data streaming for model updates.

5. Monitoring and Reporting

5.1 Real-Time Monitoring

Establish monitoring dashboards using tools like Grafana or Kibana to visualize email threat levels and model performance metrics.

5.2 Reporting

Generate periodic reports on phishing attempts and model efficacy to inform stakeholders. Use automated reporting tools such as Tableau or Power BI for data visualization and reporting.

6. Incident Response

6.1 Alerting

Set up alert mechanisms to notify security teams of detected phishing attempts. Leverage services like PagerDuty or Slack for instant alerts.

6.2 Remediation

Develop a standardized incident response plan to address detected phishing threats. Incorporate playbooks and runbooks that outline steps for containment and eradication.