AI Driven Workflow for Intelligent Phishing and Malware Analysis

Intelligent Phishing and Malware Analysis

1. Initial Detection

1.1 Data Collection

Gather data from various sources, including email traffic, web traffic, and user reports.

1.2 AI Implementation

Utilize AI-driven tools like IBM Watson for Cyber Security to analyze incoming data for patterns indicative of phishing or malware.

2. Threat Classification

2.1 Automated Analysis

Employ machine learning algorithms to categorize threats based on historical data and known threat vectors.

2.2 Tools

Use tools such as Darktrace and Cisco Umbrella to classify threats in real-time.

3. Risk Assessment

3.1 Impact Analysis

Determine the potential impact of identified threats on the organization.

3.2 AI-Driven Risk Scoring

Implement AI models from platforms like Microsoft Azure Sentinel to provide risk scoring for identified threats.

4. Response Strategy

4.1 Automated Response

Utilize automated response systems to mitigate threats promptly, such as using CrowdStrike for endpoint protection.

4.2 Manual Review

Incorporate a manual review process for high-risk threats that require human intervention.

5. Continuous Monitoring

5.1 Ongoing Analysis

Set up continuous monitoring systems to detect new threats using AI tools like Splunk.

5.2 Feedback Loop

Establish a feedback mechanism to improve AI algorithms based on new data and threat landscapes.

6. Reporting and Documentation

6.1 Incident Reporting

Document all incidents and responses for compliance and future reference.

6.2 AI-Enhanced Reporting Tools

Utilize reporting tools like Tableau integrated with AI capabilities to visualize data and trends.

7. Training and Awareness

7.1 Employee Training

Conduct regular training sessions for employees on recognizing phishing attempts and malware threats.

7.2 AI-Driven Simulation Tools

Implement simulation tools such as KnowBe4 to create realistic phishing simulations for training purposes.

8. Review and Improvement

8.1 Process Evaluation

Regularly evaluate the workflow process for effectiveness and efficiency.

8.2 AI Model Refinement

Refine AI models based on evaluation outcomes to enhance detection and response capabilities.