Generative Ai Cybersecurity Solutions

Securing Generative AI-Based Products, AI Firewalls and AI Security Posture Management (AI-SPM) & Much More

What you'll learn

Understand the unique security risks of Generative AI, including prompt injection, hallucinations, and data exfiltration

Analyze and defend against the OWASP Top 10 threats for LLM applications

Identify GenAI-specific attack surfaces such as embeddings, plugins, vector stores, and API endpoints

Implement AI Firewalls using token filtering, response moderation, and behavioral rule sets

Design and enforce Security Posture Management (AI-SPM) for prompts, agents, tools, and memory

Mitigate prompt-based attacks with detection engines, heuristic checks, and red teaming tools like PromptBench and PyRIT

Harden Vector Stores and RAG architectures against poisoning, drift, and adversarial recall

Apply sandboxing, runtime controls, and execution boundaries to secure LLM-powered SaaS and enterprise agents

Secure multi-agent orchestration frameworks (LangChain, AutoGen, CrewAI) from memory poisoning and plugin hijacking

Use identity tokens, task chains, and capability boundaries to protect agent workflows

Build and automate AI-specific security test suites and integrate them into CI/CD pipelines

Deploy open-source and commercial AI security tools (e.g., Lakera, ProtectAI, HiddenLayer) effectively

Integrate MLOps and SecOps to monitor, respond, and remediate threats across GenAI pipelines

Apply cloud-native guardrails via Azure AI Studio and GCP Vertex AI for enterprise-grade compliance and moderation

Ensure traceability, auditability, and compliance with GDPR, HIPAA, and DORA in GenAI deployments

Requirements

Basic understanding of cybersecurity principles

Description

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.

Overview

Section 1: Introduction to GenAI Security Threats

Lecture 1 Understanding the GenAI Security Landscape

Lecture 2 Why Traditional Security Fails with Generative AI

Lecture 3 OWASP Top Threats for LLM Applications

Lecture 4 OWASP Top Threats for LLM Applications pt2

Lecture 5 Security Frameworks for Generative AI (NIST AI RMF, OWASP MAS, ISO 42001)

Section 2: Foundational Security Concepts for GenAI

Lecture 6 GenAI-Specific Attack Surfaces: Prompts, Embeddings, Plugins, and APIs

Lecture 7 Prompt Injection, Data Exfiltration, Hallucination Risks

Lecture 8 Security by Design for AI Systems

Lecture 9 Regulatory Implications (GDPR, HIPAA, DORA, and GenAI)

Section 3: AI Firewalls and Model-Level Defenses

Lecture 10 What is an AI Firewall? Concepts and Components

Lecture 11 Rule-Based vs. Model-Based Firewalls (example: Lakera Guard)

Section 4: AI Security Posture Management (AI-SPM)

Lecture 12 What is AI-SPM and Why It’s Needed

Lecture 13 Posture Discovery: Prompts, Memory, Plugins, Tools, Vectors

Lecture 14 Policy Controls and Auto-Remediation

Lecture 15 Use Cases: Real-Time Risk Scoring, Misconfiguration Detection, Role Drift

Section 5: Prompt Injection and Defense Products

Lecture 16 Detection Engines and Heuristic Approaches

Lecture 17 Red Teaming for Prompt Security (PromptBench, PyRIT)

Lecture 18 Products Defending Prompts (example: Lakera, ProtectAI’s Guardian)

Section 6: Vector Store and Memory Layer Hardening

Lecture 19 Why Vector Stores Are Vulnerable

Lecture 20 Vector Poisoning, Embedding Drift, Adversarial Recall Attacks

Lecture 21 Secure RAG Architecture and Retrieval Filtering

Lecture 22 Tools for Vector Anomaly Detection example LlamaIndex, LangChain Security Plugin

Section 7: LLM Sandboxing and Runtime Controls

Lecture 23 Need for LLM Sandboxing in SaaS and Enterprise

Lecture 24 Restricted Tool Use, Execution Boundaries, and API Quotas

Lecture 25 Memory Isolation and Session Scope Control

Lecture 26 Cloud Solutions: Azure AI Content Filters, Amazon Bedrock Guardrails

Section 8: Securing Multi-Agent Systems and Orchestrators

Lecture 27 Agent Architectures: LangChain, AutoGen, CrewAI

Lecture 28 Agent Identity Spoofing, Memory Poisoning, Plugin Hijacking

Lecture 29 MAS Threat Mitigation Products (example: PromptArmor, LLMGuard)

Lecture 30 Identity Tokens, Signed Task Chains, and Capability Boundaries

Section 9: Autonomous Red Teaming and Testing Tools

Lecture 31 Building AI-Specific Security Test Suites

Lecture 32 Red Teaming Workflows with PyRIT and PromptBench

Lecture 33 Replay Engines for Prompt Forensics and Drift Monitoring

Section 10: Toolchains and Ecosystem Overview

Lecture 34 Open-Source Tools: Guardrails, Traceloop, LLM Defender

Lecture 35 Commercial Platforms: ProtectAI, Lakera, HiddenLayer

Lecture 36 MLOps + SecOps Integration for GenAI Pipelines

Lecture 37 Cloud-Native GenAI Security: Azure AI Studio, GCP Vertex Guardrails

Cybersecurity professionals looking to expand their expertise into AI-driven threat models and GenAI-specific vulnerabilities,AI/ML engineers who are responsible for building, deploying, or managing LLMs, agentic workflows, and RAG systems,DevOps and SecOps teams seeking to integrate security into AI pipelines and enforce runtime controls,Cloud architects and solution designers deploying GenAI workloads on Azure, GCP, or AWS who need to ensure compliance and safety,Product managers and tech leads overseeing AI-based features, looking to embed “security by design” into product development,Governance, risk, and compliance (GRC) officers tasked with regulatory adherence for GenAI (GDPR, HIPAA, DORA, etc.),Security researchers and red teamers interested in learning how to test, exploit, and defend agentic and LLM-based systems,AI product consultants and enterprise architects developing scalable and secure GenAI systems for clients or internal users,Tool developers or open-source contributors working on GenAI security tools, plugins, or orchestration frameworks