Agentic Ai Security: Securing The Next Ai Frontier

Master the art of protecting autonomous AI agents and distributed systems from advanced threats like hijacking and data

What you'll learn

Identify and analyze new attack vectors specific to agentic and distributed AI systems, such as prompt injection and data poisoning.

Implement security measures to harden individual AI agents, including secure API usage, input/output sanitization, and sandboxing.

Apply security principles to distributed AI systems, including federated learning and multi-agent communication protocols.

Develop AI security policies and risk assessment frameworks to ensure governance, compliance, and alignment with AI safety principles.

Requirements

A foundational understanding of AI and Machine Learning concepts. Basic knowledge of cybersecurity principles is recommended, but not strictly required. No specific tools or software are needed before starting the course.

Description

Welcome to the forefront of cybersecurity: Agentic AI Security. As artificial intelligence evolves from simple models to autonomous, decision-making agents, a new and complex threat landscape emerges. These agentic and distributed AI systems, capable of independent action and collaboration, introduce unprecedented security challenges. This course is your comprehensive guide to understanding, identifying, and mitigating these next-generation threats.In this course, we will embark on a deep dive into the world of agentic AI. You'll begin by building a solid foundation, understanding what AI agents are, their common architectures, and how they operate within a larger distributed AI ecosystem. We'll demystify concepts like swarm intelligence and federated learning, setting the stage for the critical security discussions to follow.Next, we'll confront the new threat landscape head-on. You will learn to recognize and analyze novel attack vectors that are unique to this domain. We will dissect critical vulnerabilities like prompt injection and agent hijacking, data poisoning attacks that corrupt AI learning processes, evasion techniques that trick AI models, and the potential for malicious use of entire swarms of autonomous agents.The core of this course is focused on practical, actionable security measures. We will dedicate significant time to the techniques for hardening individual AI agents. This includes securing the agent's core logic, implementing safe practices for tool and API usage, mastering input and output sanitization to prevent manipulation, and using containment strategies like sandboxing to limit potential damage. Beyond individual agents, you will learn how to secure entire distributed AI systems. We'll explore the specific security considerations for federated learning, how to protect swarm intelligence from being compromised, methods for establishing secure communication channels between agents, and strategies for building trust in decentralized, multi-agent environments.Security is not just about technology; it's also about process and policy. A dedicated section on Governance, Risk, and Compliance (GRC) will teach you about the principles of AI safety and alignment, how to conduct thorough risk assessments using established frameworks, and how to develop robust AI security policies for your organization. We will also navigate the evolving regulatory and legal landscape surrounding AI.Finally, no security posture is complete without monitoring and response. You will learn the best practices for logging and auditing agent actions, techniques for detecting anomalous or malicious agent behavior, and how to build an effective incident response plan specifically for AI systems. We'll even cover the basics of forensic analysis for AI incidents, helping you understand what went wrong after a breach.By the end of this course, you will not just be aware of the risks; you will be equipped with the knowledge and frameworks to design, build, and maintain secure agentic AI systems. You will be able to confidently address the security challenges posed by the next generation of artificial intelligence, making you an invaluable asset in this rapidly growing field.

Overview

Section 1: Introduction to Agentic AI Security

Lecture 1 Welcome

Section 2: Understanding Agentic and Distributed AI

Lecture 2 What are AI Agents

Lecture 3 Architectures of AI Agents

Lecture 4 Introduction to Distributed AI

Lecture 5 The Agentic AI Ecosystem

Section 3: The Threat Landscape

Lecture 6 New Attack Vectors

Lecture 7 Prompt Injection and Hijacking

Lecture 8 Data Poisoning and Evasion

Lecture 9 Malicious Use of Autonomous Agents

Section 4: Securing Individual AI Agents

Lecture 10 Hardening the Agent Core

Lecture 11 Secure Tool and API Usage

Lecture 12 Input and Output Sanitization

Lecture 13 Containment and Sandboxing

Section 5: Securing Distributed AI Systems

Lecture 14 Federated Learning Security

Lecture 15 Swarm Intelligence Security

Lecture 16 Secure Multi-Agent Communication

Lecture 17 Establishing Trust Between Agents

Section 6: Governance, Risk, and Compliance

Lecture 18 AI Safety and Alignment

Lecture 19 Risk Assessment Frameworks

Lecture 20 Developing AI Security Policies

Lecture 21 Regulatory and Legal Landscape

Section 7: Monitoring and Incident Response

Lecture 22 Logging and Auditing Agent Actions

Lecture 23 Detecting Anomalous Agent Behavior

Lecture 24 Incident Response for AI Systems

Section 8: Conclusion

Lecture 25 The Future of AI Security

This course is designed for cybersecurity professionals, AI/ML engineers, software developers, and IT managers who want to understand and mitigate the unique security risks of agentic and distributed AI systems. It is also valuable for students and researchers interested in the cutting edge of AI safety and security.