Graph Topology for Securing Wall Street's AI Agents and Multi-agent Systems

Overview

Explore how graph topology can secure AI agents in financial markets, defend against malicious attacks, and protect multi-agent systems through topology-guided security frameworks.

Syllabus

• Introduction to Graph Theory
Basics of Graphs: Nodes, Edges, and Paths
Types of Graphs: Directed, Undirected, Weighted, and Unweighted
Graph Measures: Centrality, Connectivity, and Clustering Coefficients
• Overview of AI Agents in Financial Markets
Types and Roles of AI Agents
Multi-agent Systems in Trading and Risk Management
Security Challenges in Financial AI Applications
• Graph Topology Basics
Understanding Graph Topology and its Importance
Topological Properties: Connectivity, Redundancy, and Robustness
Analysing Topologies: Tools and Techniques
• Security Risks in AI Agents and Multi-agent Systems
Common Vulnerabilities and Attack Vectors
Case Studies: Historical Attacks and Breaches in Financial AI
• Topology-Guided Security Frameworks
Designing Secure Graph Structures
Redundancy and Resilience Against Malicious Attacks
Implementation of Topological Defenses
• Defending AI Agents with Graph Topology
Protecting Data Integrity and Privacy
Enhancing Communication Efficiency and Security
Intrusion Detection through Topological Changes
• Securing Multi-agent Systems
Cooperative Strategies for Distributed Agents
Fault Tolerance and Error Detection
Topological Influence on Multi-agent Coordination and Decision Making
• Hands-on Projects and Case Studies
Analyzing a Financial Multi-agent System's Topology
Designing a Security-Enhanced Agent Network
Simulating Attacks and Defenses on Graph-based Systems
• Future Directions and Advanced Topics
Emerging Trends in Graph-based Security Models
AI and Quantum Computing in Graph Security
Towards Fully Autonomous Secure Financial Systems
• Conclusion and Course Review
Summary of Key Concepts
Discussion on Practical Implementations and Limitations
Final Q&A and Feedback Session

Subjects

Computer Science