This program equips cybersecurity professionals, AI engineers, and security architects with the expertise to identify, analyze, and mitigate vulnerabilities in Generative AI (GenAI) and Large Language Models (LLMs). You’ll begin by exploring the foundations of GenAI threats, examining common attack vectors such as prompt injection, jailbreaks, model theft, and adversarial manipulation. Through practical demonstrations, you will learn how attackers exploit weaknesses in AI-driven systems and how defenders can detect and respond to these risks in real-world environments.

Building on these fundamentals, you’ll gain hands-on experience in securing LLM applications, aligning model outputs to security objectives, and applying guardrails, watermarking, and safety evaluation methods. You’ll also work with API integrations using platforms like Gemini API and Google Colab to simulate secure deployment practices and mitigate risks in live systems. Next, the program delves into AI lifecycle security, covering strategies to secure training data, prevent poisoning attacks, and protect AI pipelines. You’ll explore model provenance, dependency scanning, and secure deployment pipelines—ensuring the integrity of AI systems across their entire supply chain. The course also emphasizes AI ethics and compliance, including bias detection, fairness in model design, and global regulatory frameworks like GDPR, CCPA, NIST AI RMF, ISO standards, and the EU AI Act. Using tools like Sola Security, you’ll practice auditing, governance, and risk management to operationalize ethical and compliant AI practices. Finally, you’ll examine frontier threats in emerging domains such as multimodal AI and Agentic AI, exploring adversarial attacks, cross-modal vulnerabilities, and their implications for enterprise cybersecurity. By the end of this program, you will be able to: - Identify and evaluate attack vectors targeting GenAI and LLMs. - Apply secure prompt engineering and defense strategies against prompt injection and jailbreaks. - Design and implement guardrails, safety mechanisms, and watermarking in LLM applications. - Protect AI training data, pipelines, and deployment workflows from poisoning and supply chain risks. - Assess and enforce regulatory compliance with GDPR, CCPA, NIST, ISO, and the EU AI Act. - Recognize and mitigate frontier threats in multimodal and agentic AI systems. - Integrate ethical, transparent, and resilient security practices across the AI lifecycle. This specialization is designed for cybersecurity engineers, LLM developers, AI security specialists, ML engineers, and cloud/edge security architects who want to build advanced expertise in safeguarding GenAI systems. Join us to gain the skills, tools, and strategies required to secure next-generation AI systems against evolving adversarial threats.