SEC495: Leveraging LLMs: Building & Securing RAG, Contextual RAG, and Agentic RAG Expert - Led Video Course
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Lesson 1: Introduction to Retrieval-Augmented Generation (RAG)
1.1 What is RAG?
1.2 History and Evolution of RAG
1.3 Core Components of RAG
1.4 RAG vs. Traditional LLMs
1.5 Use Cases for RAG
1.6 Benefits of RAG
1.7 Challenges in Implementing RAG
1.8 Overview of RAG Architectures
1.9 Key Industry Applications
1.10 Future Trends in RAG
Lesson 2: Large Language Models (LLMs) Fundamentals
2.1 Definition of LLMs
2.2 Popular LLM Architectures
2.3 Pre-training and Fine-tuning
2.4 Tokenization and Embedding
2.5 Language Understanding Capabilities
2.6 Limitations of LLMs
2.7 Evaluation Metrics
2.8 Model Serving and Inference
2.9 Deployment Considerations
2.10 LLMs in RAG Systems
Lesson 3: RAG System Architecture
3.1 High-Level Architecture
3.2 Retriever Module
3.3 Generator Module
3.4 Document Indexing
3.5 Query Processing
3.6 Data Flow in RAG
3.7 API Integration
3.8 Scaling RAG Systems
3.9 System Monitoring
3.10 Architecture Diagrams
Lesson 4: Building a Simple RAG Workflow
4.1 Setting Up the Environment
4.2 Selecting LLM and Retrieval Frameworks
4.3 Preparing Knowledge Base
4.4 Implementing the Retriever
4.5 Integrating the Generator
4.6 End-to-End Query Flow
4.7 Testing the Workflow
4.8 Debugging Common Issues
4.9 Performance Baselines
4.10 Sample Use Case
Lesson 5: Data Preparation for RAG
5.1 Data Sources for RAG
5.2 Data Cleaning Techniques
5.3 Chunking and Segmentation
5.4 Embedding the Documents
5.5 Creating Metadata
5.6 Storage Solutions
5.7 Maintaining Data Freshness
5.8 Handling Unstructured Data
5.9 Version Control
5.10 Data Privacy Considerations
Lesson 6: Retriever Models Overview
6.1 Dense Retriever Models
6.2 Sparse Retriever Models
6.3 Hybrid Retrieval Approaches
6.4 BM25 and TF-IDF
6.5 FAISS and Vector Databases
6.6 Approximate Nearest Neighbor Search
6.7 Evaluating Retriever Performance
6.8 Retriever Scalability
6.9 Retriever Security Risks
6.10 Retriever-Generator Coupling
Lesson 7: Generator Models Overview
7.1 Autoregressive LLMs
7.2 Sequence-to-Sequence Models
7.3 Prompt Engineering
7.4 Handling Context Windows
7.5 Output Formatting
7.6 Knowledge Grounding
7.7 Temperature and Sampling
7.8 Output Validation
7.9 Reducing Hallucinations
7.10 Generator Security Considerations
Lesson 8: Vector Databases in RAG
8.1 Introduction to Vector Databases
8.2 Popular Vector DBs: Pinecone, Weaviate, Milvus
8.3 Data Indexing Strategies
8.4 Querying Vector Databases
8.5 Index Maintenance
8.6 Sharding and Replication
8.7 Security in Vector Databases
8.8 Cost Optimization
8.9 Scaling Vector Storage
8.10 Integration with RAG Pipelines
Lesson 9: Contextual RAG Concepts
9.1 What is Contextual RAG?
9.2 Context Windows and Tokens
9.3 Expanding Contextual Understanding
9.4 Multi-hop Retrieval
9.5 Hierarchical Contexts
9.6 User Personalization
9.7 Session-based Context
9.8 Disambiguation Techniques
9.9 Contextual Error Handling
9.10 Contextual Security Risks
Lesson 10: Advanced RAG Retrieval Techniques
10.1 Multi-Stage Retrieval
10.2 Query Expansion
10.3 Feedback Loops
10.4 Active Learning for Retrieval
10.5 Cross-Encoder Re-ranking
10.6 Retrieval-Enhanced Generation
10.7 Contextual Query Reformulation
10.8 Dynamic Retrieval Strategies
10.9 Parallelized Retrieval
10.10 Evaluation of Advanced Retrieval
Lesson 11: Evaluating RAG Systems
11.1 Evaluation Metrics Overview
11.2 Precision, Recall, F1
11.3 BLEU, ROUGE, METEOR
11.4 Human-in-the-Loop Evaluation
11.5 Automated Testing Tools
11.6 A/B Testing
11.7 Error Analysis
11.8 Benchmark Datasets
11.9 Continuous Monitoring
11.10 Reporting and Visualization
Lesson 12: Security Fundamentals for RAG
12.1 Threat Modeling
12.2 Common Attacks on RAG
12.3 Data Poisoning Risks
12.4 Model Inversion Attacks
12.5 Prompt Injection Attacks
12.6 Output Manipulation
12.7 Secure Data Storage
12.8 Authentication and Authorization
12.9 Secure API Endpoints
12.10 Logging and Incident Response
Lesson 13: Data Confidentiality and Privacy
13.1 GDPR and Data Privacy
13.2 Data Anonymization
13.3 Differential Privacy
13.4 Secure Data Transmission
13.5 Encryption at Rest and in Transit
13.6 Secure Data Deletion
13.7 Consent Management
13.8 Data Minimization
13.9 Data Access Controls
13.10 Redaction Techniques
Lesson 14: Secure Knowledge Base Management
14.1 Secure Knowledge Base Design
14.2 Access Controls
14.3 Versioning and Audit Trails
14.4 Change Management
14.5 Preventing Data Leakage
14.6 Backup and Disaster Recovery
14.7 Insider Threat Mitigation
14.8 Secure Index Maintenance
14.9 Monitoring Knowledge Base Activity
14.10 Compliance Considerations
Lesson 15: Authentication & Authorization in RAG
15.1 Authentication Mechanisms
15.2 Role-Based Access Control
15.3 OAuth and SSO
15.4 API Key Management
15.5 Session Management
15.6 Least Privilege Principle
15.7 Multi-factor Authentication
15.8 Auditing Access Logs
15.9 Detecting Unauthorized Access
15.10 Remediation Strategies
Lesson 16: Prompt Injection Attacks & Defenses
16.1 What is Prompt Injection?
16.2 Types of Prompt Injection
16.3 Real-World Examples
16.4 Detecting Prompt Injection
16.5 Input Validation
16.6 Output Sanitization
16.7 User Input Isolation
16.8 Prompt Design Best Practices
16.9 Monitoring for Injection Attempts
16.10 Automated Defenses
Lesson 17: Mitigating Data Poisoning in RAG
17.1 Defining Data Poisoning
17.2 Attack Vectors
17.3 Threat Scenarios
17.4 Monitoring Data Integrity
17.5 Data Provenance
17.6 Automated Poisoning Detection
17.7 Clean Data Pipelines
17.8 Human Review Processes
17.9 Regular Data Audits
17.10 Response to Poisoning
Lesson 18: Model Inversion and Extraction Risks
18.1 Understanding Model Inversion
18.2 Extraction Attack Techniques
18.3 Sensitive Information Leakage
18.4 Model Watermarking
18.5 Query Rate Limiting
18.6 Output Randomization
18.7 Monitoring Suspicious Activity
18.8 Limiting Output Granularity
18.9 Adversarial Testing
18.10 Model Protection Best Practices
Lesson 19: Reducing LLM Hallucinations in RAG
19.1 Why LLMs Hallucinate
19.2 Detection of Hallucinations
19.3 Post-hoc Filtering
19.4 Fact-checking with Retrieval
19.5 Confidence Scoring
19.6 Multi-source Verification
19.7 Human-in-the-Loop Validation
19.8 Improving Knowledge Base Quality
19.9 Prompt Engineering for Accuracy
19.10 User Alerts for Low Confidence
Lesson 20: Logging, Monitoring, and Auditing RAG Systems
20.1 Importance of Logging
20.2 Types of Logs
20.3 Real-time Monitoring Tools
20.4 Auditable Events
20.5 Log Retention Policies
20.6 Alerting Mechanisms
20.7 Security Information & Event Management (SIEM)
20.8 Forensic Investigations
20.9 Anomaly Detection
20.10 Compliance Reporting
Lesson 21: Securing the Deployment Pipeline
21.1 Secure CI/CD Practices
21.2 Code Reviews
21.3 Dependency Management
21.4 Vulnerability Scanning
21.5 Secrets Management
21.6 Environment Isolation
21.7 Secure Build Artifacts
21.8 Infrastructure as Code Security
21.9 Automated Security Testing
21.10 Rollback and Recovery
Lesson 22: Securing APIs for RAG Systems
22.1 API Security Fundamentals
22.2 Rate Limiting
22.3 Input Validation
22.4 Output Escaping
22.5 Secure API Gateways
22.6 OAuth 2.0 and JWT
22.7 API Versioning and Deprecation
22.8 API Abuse Detection
22.9 Monitoring API Usage
22.10 Incident Response for APIs
Lesson 23: Secure Integration with External Data Sources
23.1 Evaluating Data Source Trustworthiness
23.2 Secure Data Fetching
23.3 Input Validation of External Data
23.4 Sanitizing Fetched Content
23.5 Monitoring Data Source Changes
23.6 Handling Malicious Content
23.7 Data Source Authentication
23.8 Maintaining Source Reputation
23.9 Isolating External Data
23.10 Response to Source Compromise
Lesson 24: Securing Vector Databases
24.1 Access Control in Vector DBs
24.2 Encryption in Vector DBs
24.3 Secure Backup Strategies
24.4 Data Segmentation
24.5 Audit Trails
24.6 Monitoring for Anomalies
24.7 Handling Sensitive Embeddings
24.8 Disaster Recovery
24.9 Compliance in Vector Storage
24.10 Secure DB Upgrades
Lesson 25: Adversarial Testing for RAG Systems
25.1 What is Adversarial Testing?
25.2 Generating Adversarial Inputs
25.3 Testing Input Sanitization
25.4 Prompt Attack Simulations
25.5 Automated Adversarial Tools
25.6 Human vs Machine Testing
25.7 Recording Test Results
25.8 Remediation Steps
25.9 Continuous Adversarial Testing
25.10 Reporting Vulnerabilities
Lesson 26: Auditing and Compliance in RAG
26.1 Regulatory Requirements
26.2 GDPR, HIPAA, CCPA
26.3 Audit Trail Implementation
26.4 Data Subject Rights
26.5 Privacy Impact Assessments
26.6 Regular Compliance Audits
26.7 Documentation Practices
26.8 Responding to Audit Findings
26.9 Maintaining Compliance
26.10 Training for Compliance
Lesson 27: Secure User Feedback Loops
27.1 Collecting User Feedback Securely
27.2 Anonymizing Feedback Data
27.3 Preventing Feedback Poisoning
27.4 Incorporating Feedback Safely
27.5 User Consent Management
27.6 Monitoring Feedback Channels
27.7 Validating Feedback Authenticity
27.8 Handling Sensitive User Data
27.9 Feedback Loop Auditing
27.10 Regulatory Implications
Lesson 28: Agentic RAG Fundamentals
28.1 Definition of Agentic RAG
28.2 Key Differences from Standard RAG
28.3 Autonomous Task Execution
28.4 Agent Planning Capabilities
28.5 Multi-step Reasoning
28.6 Coordination between Agents
28.7 Goal-Oriented Retrieval
28.8 Error Handling in Agents
28.9 Security Implications
28.10 Use Cases for Agentic RAG
Lesson 29: Designing Agentic RAG Workflows
29.1 Workflow Planning
29.2 Task Decomposition
29.3 Workflow Orchestration
29.4 Agent Communication Protocols
29.5 Parallel Task Execution
29.6 Dynamic Task Assignment
29.7 Monitoring Workflow Progress
29.8 Error Recovery Strategies
29.9 Workflow Logging
29.10 Workflow Security
Lesson 30: Agent Memory and State Management
30.1 Short-term vs. Long-term Memory
30.2 Memory Architectures
30.3 State Persistence
30.4 Secure Memory Storage
30.5 Updating Agent Memory
30.6 Forgetting and Data Retention
30.7 Memory Consistency
30.8 Data Privacy in Agent Memory
30.9 Memory Auditing
30.10 Memory Security Risks
Lesson 31: Secure Communication Between Agents
31.1 Message Encryption
31.2 Authentication of Agents
31.3 Preventing Man-in-the-Middle
31.4 Data Integrity Verification
31.5 Communication Protocols
31.6 Secure Broadcasting
31.7 Monitoring Agent Interactions
31.8 Handling Communication Failures
31.9 Secure Channel Establishment
31.10 Logging Communications
Lesson 32: Agentic RAG Security Threats
32.1 Threat Landscape
32.2 Agent Impersonation
32.3 Malicious Agent Insertion
32.4 Data Leakage Risks
32.5 Command Injection
32.6 Task Hijacking
32.7 Agent Collusion
32.8 Insider Threats
32.9 Detection Mechanisms
32.10 Mitigation Strategies
Lesson 33: Agentic RAG Use Cases
33.1 Automated Research Assistants
33.2 Enterprise Knowledge Management
33.3 Customer Support Automation
33.4 Intelligent Workflow Automation
33.5 Data Aggregation Agents
33.6 Real-time Incident Response
33.7 Personalized Learning Agents
33.8 Multi-agent Collaboration
33.9 Adaptive Information Retrieval
33.10 Secure Agentic Applications
Lesson 34: Orchestrating Multi-Agent Systems
34.1 Multi-Agent System Overview
34.2 Coordination Models
34.3 Task Allocation Strategies
34.4 Conflict Resolution
34.5 Agent Hierarchies
34.6 Load Balancing
34.7 Scalability Considerations
34.8 Monitoring Multi-Agent Systems
34.9 Security in Orchestration
34.10 Fault Tolerance
Lesson 35: Agent Autonomy and Supervision
35.1 Degrees of Agent Autonomy
35.2 Supervisory Controls
35.3 Human-in-the-Loop
35.4 Escalation Mechanisms
35.5 Autonomous Decision-Making
35.6 Override Capabilities
35.7 Auditing Autonomous Actions
35.8 Risk Assessment
35.9 Policy Enforcement
35.10 Safety Measures
Lesson 36: Secure Agent Orchestration Platforms
36.1 Orchestration Platform Overview
36.2 Platform Security Features
36.3 Access Controls in Orchestration
36.4 Secure Task Scheduling
36.5 Monitoring and Logging
36.6 Platform Authentication
36.7 Secure Plugin Management
36.8 Update and Patch Management
36.9 Platform Compliance
36.10 Disaster Recovery
Lesson 37: Agentic RAG Workflow Security
37.1 Secure Workflow Design
37.2 Task Isolation
37.3 Secure Data Passing
37.4 Workflow Authorization
37.5 Integrity Checking
37.6 Error Handling Security
37.7 Logging Sensitive Actions
37.8 Workflow Auditing
37.9 Incident Response
37.10 Continuous Security Improvement
Lesson 38: Explainability and Transparency in RAG
38.1 Importance of Explainability
38.2 Traceability of Results
38.3 User-Facing Explanations
38.4 Logging Decision Paths
38.5 Explaining Agent Actions
38.6 Auditing Explanations
38.7 Mitigating Opaque Behavior
38.8 Transparency Tools
38.9 Legal and Compliance Implications
38.10 Best Practices
Lesson 39: Human-in-the-Loop Security
39.1 Role of Human Oversight
39.2 Security Review Processes
39.3 Human Approval Gates
39.4 Mitigating Human Error
39.5 Training Human Reviewers
39.6 Escalation Protocols
39.7 Logging Human Interactions
39.8 Feedback on System Security
39.9 Combining Human and Automated Defenses
39.10 Continuous Improvement
Lesson 40: Secure Deployment of Agentic RAG
40.1 Deployment Models
40.2 Secure Configuration
40.3 Environment Hardening
40.4 Network Segmentation
40.5 Secure Rollouts
40.6 Zero Trust Principles
40.7 Monitoring Deployed Agents
40.8 Disaster Recovery Planning
40.9 Rollback Strategies
40.10 Post-deployment Auditing
Lesson 41: Case Study: Securing an Enterprise RAG System
41.1 Case Study Overview
41.2 System Architecture
41.3 Data Security Measures
41.4 Access Control Implementation
41.5 Incident Response Process
41.6 Security Monitoring Tools
41.7 Lessons Learned
41.8 Compliance Outcomes
41.9 User Feedback
41.10 Continuous Security Improvements
Lesson 42: Red Teaming RAG Systems
42.1 Red Teaming Fundamentals
42.2 Planning Red Team Engagements
42.3 Attack Simulation Scenarios
42.4 Social Engineering Risks
42.5 Measuring Red Team Success
42.6 Post-engagement Reporting
42.7 Remediation and Hardening
42.8 Continuous Red Teaming
42.9 Collaboration with Blue Teams
42.10 Lessons Learned
Lesson 43: Blue Team Operations for RAG Security
43.1 Blue Team Responsibilities
43.2 Security Monitoring
43.3 Incident Detection and Response
43.4 Threat Hunting
43.5 Security Automation
43.6 Reporting and Documentation
43.7 Collaboration with Red Teams
43.8 Continuous Improvement
43.9 User Training Programs
43.10 Metrics and KPIs
Lesson 44: Secure Collaboration with Third Parties
44.1 Third-Party Risk Assessment
44.2 Secure Data Sharing
44.3 Vendor Security Reviews
44.4 Contractual Security Requirements
44.5 Data Access Agreements
44.6 Monitoring Third-Party Activity
44.7 Incident Handling with Vendors
44.8 Terminating Third-Party Access
44.9 Compliance Implications
44.10 Best Practices
Lesson 45: Continuous Security Improvement in RAG
45.1 Security Baseline Establishment
45.2 Vulnerability Management
45.3 Patch Management
45.4 Security Reviews
45.5 Continuous Monitoring
45.6 Automated Security Updates
45.7 User Security Training
45.8 Regular Penetration Testing
45.9 Feedback Loops
45.10 Measuring Security Posture
Lesson 46: RAG Systems in Regulated Industries
46.1 Industry-specific Regulations
46.2 Healthcare Compliance (HIPAA)
46.3 Financial Services Compliance
46.4 Government Standards
46.5 Data Residency Requirements
46.6 Enhanced Audit Trails
46.7 Regulatory Reporting
46.8 Handling Sensitive Data
46.9 Regular Compliance Training
46.10 Case Studies
Lesson 47: Incident Response in RAG Systems
47.1 Incident Response Planning
47.2 Threat Detection
47.3 Triage and Classification
47.4 Containment Strategies
47.5 Eradication and Recovery
47.6 Post-incident Analysis
47.7 Communication Protocols
47.8 Updating Incident Response Plans
47.9 User Notification Policies
47.10 Lessons Learned
Lesson 48: Future Trends in RAG Security
48.1 Emerging Threats
48.2 Advances in Secure Retrieval
48.3 Confidential Computing
48.4 Privacy-preserving AI
48.5 Federated RAG Systems
48.6 Adaptive Security Mechanisms
48.7 Automated Threat Detection
48.8 Security in Multi-modal RAG
48.9 Industry Adoption
48.10 Research Directions
Lesson 49: Hands-on Lab: Building & Securing a RAG System
49.1 Lab Setup
49.2 Building the Knowledge Base
49.3 Implementing the Retriever
49.4 Configuring the Generator
49.5 Integrating Security Controls
49.6 Testing for Vulnerabilities
49.7 Monitoring and Logging
49.8 Simulating Attacks
49.9 Incident Response Drill
49.10 Lab Review and Wrap-up
Lesson 50: Course Review and Capstone Project
50.1 Key Concepts Recap
50.2 Security Best Practices Review
50.3 Capstone Project Introduction
50.4 Project Requirements
50.5 Project Planning
50.6 Building the Capstone RAG System
50.7 Securing the Capstone System
50.8 Project Presentation
50.9 Peer Review
50.10 Final Q&A and Next Steps