Generative AI Cybersecurity Solutions
Securing Generative AI-Based Products, AI Firewalls and AI Security Posture Management (AI-SPM) & Much More
Generative AI Cybersecurity Solutions free download
Securing Generative AI-Based Products, AI Firewalls and AI Security Posture Management (AI-SPM) & Much More
As Generative AI becomes integral to modern business systems, ensuring its secure deployment has become a top priority. The “Generative AI Cybersecurity Solutions” course provides a comprehensive and structured deep dive into the evolving landscape of threats, controls, and security architectures specific to large language models (LLMs), agent frameworks, RAG pipelines, and AI-powered APIs. Unlike traditional cybersecurity approaches, which were built around static systems and deterministic logic, GenAI introduces new attack surfaces—including prompt injection, adversarial vector recall, plugin misuse, hallucinations, and memory poisoning—that demand a reimagined defense strategy.
This course begins with an overview of foundational threats to GenAI applications, covering why traditional security frameworks fall short and introducing learners to OWASP LLM Top 10, NIST AI Risk Management Framework, OWASP MAS, and ISO 42001. Learners then explore GenAI-specific risks such as prompt abuse, embedding drift, and data exfiltration, alongside the regulatory landscape including GDPR, HIPAA, and DORA. A deep dive into AI Firewalls and AI Security Posture Management (AI-SPM) equips students with the knowledge to deploy token filters, response moderation, policy enforcement, and posture discovery. Modules on Prompt Injection Defense, Vector Store Hardening, and Runtime Sandboxing bring practical tools and design patterns into focus, using examples like Lakera Guard, ProtectAI’s Guardian, LlamaIndex, and Azure AI Studio.
Advanced modules focus on securing agentic systems such as LangChain, AutoGen, and CrewAI, while exploring identity spoofing, signed task chains, and red teaming strategies with tools like PyRIT and PromptBench. The final module surveys the current security ecosystem—both open-source and commercial—highlighting how MLOps and SecOps can be unified to build robust, auditable, and scalable GenAI systems. By the end, learners will be equipped to assess, defend, and deploy secure GenAI pipelines across enterprise settings.
