Legitimized [FOR563: Applied AI for Digital Forensics and Incident Response: Leveraging Local Large Language Models] Expert – Led Video Course – MASTERYTRAIL

1. Introduction to Applied AI in DFIR
1.1. Overview of Digital Forensics and Incident Response
1.2. The Role of AI in DFIR
1.3. Evolution of AI Technologies
1.4. Key Challenges in Modern DFIR
1.5. Defining Local Large Language Models (LLMs)
1.6. Advantages of Using Local LLMs
1.7. Ethical Considerations
1.8. Overview of the Course Structure
1.9. Setting Up Your Learning Environment
1.10. Expectations and Outcomes

2. Fundamentals of Digital Forensics
2.1. Digital Evidence Types
2.2. The Forensic Process
2.3. Chain of Custody Principles
2.4. Acquisition Methods
2.5. Preservation of Data Integrity
2.6. Forensic Imaging Techniques
2.7. File System Analysis
2.8. Volatile vs. Non-volatile Data
2.9. Reporting and Documentation
2.10. Legal Frameworks in Digital Forensics

3. Incident Response Essentials
3.1. Incident Response Lifecycle
3.2. Preparation Phase
3.3. Identification Phase
3.4. Containment Strategies
3.5. Eradication and Remediation
3.6. Recovery Processes
3.7. Post-Incident Activity
3.8. Communication During Incidents
3.9. Metrics for Incident Response
3.10. Common Incident Types

4. AI Concepts for DFIR
4.1. Machine Learning vs. Deep Learning
4.2. Natural Language Processing (NLP) Basics
4.3. Language Models Explained
4.4. Training Data and Datasets
4.5. Supervised vs. Unsupervised Learning
4.6. Model Evaluation Metrics
4.7. Overfitting and Underfitting
4.8. Data Preprocessing Techniques
4.9. AI in Text Analysis
4.10. Limitations of AI in DFIR

5. Local Large Language Models: Overview
5.1. What are LLMs?
5.2. LLM Architectures (e.g., Transformer)
5.3. Pre-trained vs. Fine-tuned Models
5.4. Open Source LLM Projects
5.5. Model Size and Performance
5.6. Hardware Requirements
5.7. Privacy and Security Considerations
5.8. Comparison: Local vs. Cloud LLMs
5.9. Use Cases in DFIR
5.10. Future Trends in Local LLMs

6. Setting Up Local LLMs
6.1. Hardware Setup
6.2. GPU vs. CPU Considerations
6.3. Required Software and Dependencies
6.4. Downloading Pre-trained Models
6.5. Installing Frameworks (e.g., PyTorch, Transformers)
6.6. Model Loading and Initialization
6.7. Storage and Memory Management
6.8. Security Configurations
6.9. Troubleshooting Common Issues
6.10. Maintaining Model Versions

7. Data Collection for DFIR AI
7.1. Sources of Digital Evidence
7.2. Automated Data Collection Tools
7.3. Ethical Data Collection
7.4. Labeling and Annotation
7.5. Data Quality Assurance
7.6. Data Normalization
7.7. Handling Sensitive Information
7.8. Data Augmentation
7.9. Storage and Access Control
7.10. Compliance Considerations

8. Preprocessing Digital Forensic Data
8.1. Structured vs. Unstructured Data
8.2. Text Extraction from Evidence
8.3. Data Cleaning Steps
8.4. Tokenization Techniques
8.5. Removing Noise and Redundancy
8.6. Encoding and Formatting
8.7. Language Detection
8.8. Metadata Extraction
8.9. Feature Engineering
8.10. Pipeline Automation

9. Prompt Engineering for DFIR Tasks
9.1. Understanding Prompts in LLMs
9.2. Simple vs. Complex Prompts
9.3. Prompt Templates for DFIR
9.4. Contextual Prompting
9.5. Prompt Chaining Techniques
9.6. Evaluating Prompt Effectiveness
9.7. Adversarial Prompting
9.8. Prompt Security Considerations
9.9. Automating Prompt Generation
9.10. Real-world Prompt Examples

10. AI-powered Triage and Alerting
10.1. Automated Log Analysis
10.2. Alert Prioritization
10.3. False Positive Reduction
10.4. Anomaly Detection with LLMs
10.5. Correlating Events
10.6. Alert Summarization
10.7. Real-time Notification Systems
10.8. Integration with SIEM Tools
10.9. Custom Alert Rules using LLMs
10.10. Case Study: AI-Driven Triage

11. Automating Evidence Analysis with LLMs
11.1. Parsing Digital Artifacts
11.2. Interpreting File Metadata
11.3. Timeline Reconstruction
11.4. Text Summarization of Reports
11.5. Keyword Extraction
11.6. Entity Recognition
11.7. Sentiment Analysis
11.8. Contextual Analysis
11.9. Automating Correlation of Data
11.10. Case Study: Automated Analysis Pipeline

12. Malware Analysis and LLMs
12.1. Introduction to Malware Analysis
12.2. Static Code Analysis
12.3. Dynamic Behavior Analysis
12.4. Automated Report Generation
12.5. Signature Extraction
12.6. Code Similarity Detection
12.7. Identifying Malicious Patterns
12.8. AI for Deobfuscation
12.9. Threat Intelligence Enrichment
12.10. Limitations and Challenges

13. Network Forensics with AI
13.1. Capturing Network Traffic
13.2. Parsing Network Logs
13.3. Pattern Recognition in Traffic
13.4. Anomaly Detection in Flows
13.5. Automated IOC Extraction
13.6. Network Graph Analysis
13.7. Summarizing Network Events
13.8. Protocol Analysis Automation
13.9. Alerting on Suspicious Activity
13.10. Integrating AI in NIDS

14. Endpoint Forensics Automation
14.1. Collecting Endpoint Data
14.2. Parsing Event Logs
14.3. Detecting Unauthorized Access
14.4. File Integrity Monitoring
14.5. Automated Timeline Generation
14.6. Identifying Suspicious Processes
14.7. Registry Analysis
14.8. Memory Dump Analysis
14.9. Reporting Endpoint Findings
14.10. Case Study: Endpoint AI Workflow

15. AI-Driven Log Analysis
15.1. Types of Logs in DFIR
15.2. Log Parsing with LLMs
15.3. Identifying Key Events
15.4. Log Summarization
15.5. Outlier Detection
15.6. Log Correlation Across Sources
15.7. Automated Narrative Generation
15.8. Alerting on Log Patterns
15.9. Handling Large Log Volumes
15.10. Best Practices for Log Analysis

16. Natural Language Processing in Forensics
16.1. Text Mining Techniques
16.2. Information Extraction
16.3. Document Classification
16.4. Topic Modeling
16.5. Named Entity Recognition (NER)
16.6. Relation Extraction
16.7. Text Similarity Measures
16.8. Language Detection
16.9. Forensic Report Generation
16.10. NLP Challenges in DFIR

17. Summarizing and Reporting with LLMs
17.1. Key Elements of Forensic Reports
17.2. Automated Executive Summaries
17.3. Highlighting Critical Evidence
17.4. Generating Chronologies
17.5. Visualizing Findings
17.6. Customizing Report Formats
17.7. Explaining Technical Details
17.8. Redacting Sensitive Information
17.9. Quality Assurance in Automated Reports
17.10. User Feedback and Iteration

18. Evidence Correlation and Attribution
18.1. Linking Multiple Evidence Sources
18.2. Evidence Graph Construction
18.3. Entity Resolution
18.4. Timeline Alignment
18.5. Role of AI in Attribution
18.6. Identifying Attack Chains
18.7. Attribution Confidence Scoring
18.8. Dealing with Ambiguities
18.9. Visualizing Correlated Evidence
18.10. Reporting Attribution Findings

19. Case Management with AI
19.1. Digital Case Management Systems
19.2. Integrating LLMs in Workflow
19.3. Automated Task Assignment
19.4. Evidence Tagging
19.5. Case Summarization
19.6. Collaboration Tools
19.7. Audit Trails and Logs
19.8. Compliance Automation
19.9. Data Privacy Controls
19.10. Future Trends in Case Management

20. AI for Threat Intelligence Enrichment
20.1. Collecting Threat Intelligence Feeds
20.2. IOC Extraction and Analysis
20.3. TTP Mapping
20.4. Automated Threat Briefing
20.5. Integrating External Intelligence
20.6. Contextualizing Threat Data
20.7. Identifying Emerging Threats
20.8. Enriching Alerts with TI
20.9. Threat Scoring Models
20.10. Sharing Intelligence Securely

21. Automating Playbooks with LLMs
21.1. Playbook Fundamentals
21.2. Mapping Playbooks to AI Tasks
21.3. Creating Automated Responses
21.4. Dynamic Playbook Updates
21.5. Integrating with SOAR Platforms
21.6. Monitoring Playbook Execution
21.7. Playbook Customization
21.8. Error Handling in Automation
21.9. Evaluating Playbook Effectiveness
21.10. Playbook Version Control

22. AI for Insider Threat Detection
22.1. Understanding Insider Threats
22.2. Behavioral Analysis with AI
22.3. Identifying Anomalous Actions
22.4. Contextual Alerting
22.5. Automated Investigation
22.6. Privacy Considerations
22.7. Risk Scoring Models
22.8. Case Studies
22.9. Limitations of AI Approaches
22.10. Future Directions

23. Deepfakes and AI-Generated Content
23.1. What are Deepfakes?
23.2. Detection Techniques
23.3. Role of LLMs in Detection
23.4. Audio vs. Video Deepfakes
23.5. Use of AI in Content Verification
23.6. Chain of Custody for Digital Media
23.7. Mitigation Strategies
23.8. Reporting Deepfake Incidents
23.9. Legal and Ethical Issues
23.10. Emerging Tools and Research

24. AI for Phishing Analysis
24.1. Email Forensics Fundamentals
24.2. Phishing Indicators
24.3. LLM-based Email Analysis
24.4. Automated URL Extraction
24.5. Content Analysis for Phishing
24.6. Attachment Analysis
24.7. Real-time Phishing Detection
24.8. Reporting and Alerting
24.9. User Awareness Automation
24.10. Advanced Phishing Tactics

25. Memory Forensics and LLMs
25.1. Introduction to Memory Forensics
25.2. Memory Dump Acquisition
25.3. Parsing Memory Structures
25.4. Identifying Malicious Artifacts
25.5. Automated Search for IOCs
25.6. Correlating Memory Evidence
25.7. Pattern Recognition in Memory
25.8. Summarizing Findings
25.9. Reporting Memory Analysis
25.10. Challenges in Automation

26. AI for File System Analysis
26.1. File System Fundamentals
26.2. Parsing Directory Structures
26.3. Detecting Hidden Files
26.4. File Carving Automation
26.5. Identifying Suspicious Files
26.6. Metadata Analysis
26.7. File Timeline Reconstruction
26.8. Automated Classification
26.9. Reporting File System Evidence
26.10. Integration with Other Tools

27. Cloud Forensics and AI
27.1. Introduction to Cloud Forensics
27.2. Cloud Artefact Collection
27.3. Parsing Cloud Logs
27.4. LLMs for Multi-Cloud Environments
27.5. Automated Evidence Correlation
27.6. Cloud Incident Response
27.7. Privacy and Compliance Challenges
27.8. Data Residency Issues
27.9. Reporting Cloud Findings
27.10. Future of AI in Cloud Forensics

28. IoT Forensics Automation
28.1. IoT Device Fundamentals
28.2. Data Collection from IoT Devices
28.3. Parsing IoT Protocols
28.4. Identifying Anomalous Behavior
28.5. LLMs for IoT Log Analysis
28.6. Timeline Construction
28.7. Integrating IoT Evidence
28.8. Reporting IoT Incidents
28.9. Security Challenges
28.10. Case Study: IoT Forensic Analysis

29. Chain of Custody Automation
29.1. Importance of Chain of Custody
29.2. Digital Tracking Methods
29.3. LLM-based Documentation
29.4. Evidence Integrity Verification
29.5. Automated Logging
29.6. Secure Evidence Transfers
29.7. Audit Trail Generation
29.8. Reporting Chain of Custody
29.9. Legal Admissibility
29.10. Future Automation Directions

30. LLMs for Legal and Compliance Automation
30.1. Compliance Frameworks Overview
30.2. Mapping Evidence to Regulations
30.3. Automated GDPR Checks
30.4. Data Privacy Controls
30.5. LLMs for Legal Summaries
30.6. Policy Enforcement Automation
30.7. Legal Risk Identification
30.8. Reporting Compliance Status
30.9. Audit Preparation
30.10. Future Legal Automation

31. Adversarial AI in DFIR
31.1. Understanding Adversarial Examples
31.2. Risks to Forensic AI Systems
31.3. Detection of Adversarial Attacks
31.4. Hardening LLMs
31.5. Testing AI Robustness
31.6. Red Teaming AI Models
31.7. Defense Strategies
31.8. Reporting Adversarial Incidents
31.9. Regulatory Considerations
31.10. Future Threats

32. AI for Data Loss Prevention
32.1. DLP Fundamentals
32.2. LLMs for Content Inspection
32.3. Real-time DLP Alerts
32.4. Automated Data Classification
32.5. Policy Enforcement
32.6. False Positive Reduction
32.7. Reporting DLP Incidents
32.8. Compliance Checks
32.9. Integration with SIEM
32.10. DLP Trends and Innovations

33. Reducing Analyst Burnout with Automation
33.1. Analyst Workload Challenges
33.2. Task Automation Benefits
33.3. LLMs for Repetitive Tasks
33.4. Alert Triage Automation
33.5. Knowledge Base Automation
33.6. Reducing Manual Documentation
33.7. Feedback Loops with Analysts
33.8. Improving Analyst Well-being
33.9. Measuring Burnout Reduction
33.10. Case Study: Analyst Productivity

34. Integrating LLMs into Existing Tools
34.1. Overview of DFIR Tools
34.2. LLM API Integration
34.3. Plugin Development
34.4. Automation Scripting
34.5. Data Flow Management
34.6. Compatibility Checks
34.7. Error Handling
34.8. Performance Optimization
34.9. User Training
34.10. Maintenance and Updates

35. Continuous Learning and Model Updates
35.1. Why Update Models?
35.2. Retraining LLMs
35.3. Incremental Learning
35.4. Data Drift Monitoring
35.5. Model Performance Tracking
35.6. Automating Updates
35.7. Rollback Strategies
35.8. User Feedback and Fine-tuning
35.9. Version Control
35.10. Documentation of Updates

36. AI for Mobile Forensics
36.1. Mobile Device Overview
36.2. Data Acquisition from Mobiles
36.3. Parsing Mobile Artifacts
36.4. Automated Message Analysis
36.5. App Data Parsing
36.6. Timeline Creation
36.7. Location Data Analysis
36.8. Reporting Mobile Evidence
36.9. Challenges in Mobile Forensics
36.10. Future Directions

37. AI for Timeline Reconstruction
37.1. Importance of Timelines
37.2. Collecting Timestamped Evidence
37.3. Automated Chronology Building
37.4. Correlating Multiple Sources
37.5. Visualizing Timelines
37.6. Outlier Event Detection
37.7. LLM-based Event Summarization
37.8. Reporting Timelines
37.9. Reviewing Timelines for Gaps
37.10. Case Study: Complex Timeline

38. Evaluating AI Performance in DFIR
38.1. Key Performance Metrics
38.2. Precision and Recall
38.3. ROC and AUC
38.4. Confusion Matrix Explained
38.5. Human-in-the-loop Evaluation
38.6. Benchmark Datasets
38.7. Continuous Monitoring
38.8. User Feedback Integration
38.9. Addressing Bias
38.10. Reporting AI Performance

39. Explainability in Forensic AI
39.1. Importance of Explainability
39.2. Black-box vs. White-box Models
39.3. Feature Importance
39.4. Model Interpretation Tools
39.5. Explaining Predictions to Stakeholders
39.6. Visualizing AI Decisions
39.7. Generating Explainable Reports
39.8. Addressing Legal Requirements
39.9. User Trust in AI
39.10. Future of Explainable AI

40. User Interaction with LLM-based Systems
40.1. Designing User Interfaces
40.2. Natural Language Queries
40.3. Chatbots in DFIR
40.4. Visualizing AI Results
40.5. User Feedback Mechanisms
40.6. Accessibility Considerations
40.7. Training Users
40.8. Error Handling for Users
40.9. Documenting Interactions
40.10. Continuous Improvement

41. AI-Driven Search in Forensics
41.1. Indexing Evidence
41.2. Semantic Search with LLMs
41.3. Query Expansion Techniques
41.4. Ranking Search Results
41.5. Search Performance Optimization
41.6. Handling Large Datasets
41.7. User-friendly Search Interfaces
41.8. Search Result Summarization
41.9. Relevance Feedback
41.10. Search Security Considerations

42. Privacy Preserving AI in DFIR
42.1. Privacy Risks in Forensic AI
42.2. Data Anonymization Techniques
42.3. Differential Privacy
42.4. Local Model Advantages
42.5. Secure Multi-party Computation
42.6. Federated Learning
42.7. Access Control Mechanisms
42.8. Privacy Impact Assessment
42.9. Reporting on Privacy Measures
42.10. Future Privacy Technologies

43. AI for Threat Hunting
43.1. Threat Hunting Fundamentals
43.2. Hypothesis-driven Hunting
43.3. Automated IOC Search
43.4. Pattern Discovery with LLMs
43.5. Enriching Hunt Data
43.6. Real-time Threat Hunting
43.7. Hunt Reporting Automation
43.8. Integrating with Security Platforms
43.9. Visualizing Hunt Results
43.10. Future of Automated Hunting

44. AI for Vulnerability Management
44.1. Vulnerability Scanning Fundamentals
44.2. Parsing Scan Results
44.3. LLMs for Vulnerability Prioritization
44.4. Automated Risk Scoring
44.5. Tracking Remediation
44.6. Reporting Vulnerability Status
44.7. Integrating with Patch Management
44.8. Alerting on Critical Issues
44.9. Auditing Vulnerability Management
44.10. Continuous Improvement

45. AI and Digital Evidence in Court
45.1. Legal Admissibility of AI Evidence
45.2. Preparing Evidence for Court
45.3. Explaining AI Methods to Non-experts
45.4. Documenting AI Processes
45.5. Cross-examination Preparation
45.6. Expert Witness Testimony
45.7. Addressing Legal Challenges
45.8. Case Law and Precedents
45.9. Ethical Issues in Testimony
45.10. Future of AI in Legal Proceedings

46. Human-in-the-loop AI for DFIR
46.1. Importance of Human Oversight
46.2. Collaborative Decision Making
46.3. Feedback Loops
46.4. AI Suggestions vs. Human Decisions
46.5. Escalation Paths
46.6. Balancing Automation and Review
46.7. User Interface Design
46.8. Training for Human-in-the-loop
46.9. Auditing Decisions
46.10. Measuring Effectiveness

47. Scaling AI Solutions in DFIR
47.1. Challenges of Scale
47.2. Distributed Processing
47.3. Cloud vs. On-prem Solutions
47.4. Load Balancing Techniques
47.5. Data Partitioning
47.6. Monitoring System Health
47.7. Multi-user Management
47.8. Performance Optimization
47.9. Cost Management
47.10. Future Scalability Trends

48. Research Frontiers in AI for DFIR
48.1. Current Research Trends
48.2. Open Problems in DFIR AI
48.3. New LLM Architectures
48.4. Data Scarcity Solutions
48.5. Explainable AI Advances
48.6. Multimodal AI in Forensics
48.7. AI for New Threat Vectors
48.8. Participating in DFIR Research
48.9. Publishing Findings
48.10. Collaborations and Consortia

49. Ethics and Responsible AI in DFIR
49.1. Defining Ethical AI
49.2. Bias and Fairness
49.3. Transparency and Accountability
49.4. Responsible Data Use
49.5. Human Rights Considerations
49.6. Addressing Unintended Outcomes
49.7. Regulatory Compliance
49.8. Ethics in Automation
49.9. Reporting Ethical Issues
49.10. Building an Ethical AI Culture

50. Capstone: Building an End-to-End AI-powered DFIR Solution
50.1. Project Planning
50.2. Defining User Requirements
50.3. Selecting Data Sources
50.4. Data Collection and Preprocessing
50.5. Model Selection and Customization
50.6. Integration and Automation
50.7. Testing and Evaluation
50.8. Reporting and Documentation
50.9. User Training and Handover
50.10. Final Presentation and Review