AI-Driven Cyber Defense: GenAI, Red Teaming & Modern Threats

Mastering Secure GenAI Deployment, Offensive AI Techniques, and Framework-Based Defense for 2026 and Beyond

Created byEduonix Learning Solutions

Last updated 1/2026

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What you'll learn

Apply GenAI and GPT tools to automate threat detection and incident response
Use frameworks like OWASP Top 10, MITRE ATLAS, and NIST AI guidance for secure AI adoption
Perform AI red teaming, including prompt attacks, model evasion, and agent-based testing
Secure APIs, LLM integrations, and GenAI applications against modern attack vectors

Course content

4 sections • 19 lectures • 3h 38m total length

Introduction to AI in Security Operations12:46
Explore how AI transforms security operations through anomaly detection, pattern recognition, and automated threat intelligence, empowering analysts to scale and act against evolving cyber threats.
Building GPT-powered Security Assistants11:35
Build practical GPT-powered security assistants to explain vulnerabilities, summarize logs, and draft policies, turning data overload into prioritized insights and reusable security automation.
Automating Threat Detection Pipelines13:26
Automate threat detection pipelines from ingestion to alerts, enriching and correlating data to deliver scalable, actionable insights; includes an automated phishing detection example using artificial intelligence.
AI for Digital Forensics & Incident Response11:19
AI-powered digital forensics and incident response accelerate evidence correlation across logs, memory dumps, and network traces, while AI acts as a co-investigator guided by human judgment.
Real-world case studies and labs13:31
Explore real-world ai-driven cyber defense through healthcare and cloud case studies, and hands-on labs that apply pattern recognition, continuous retraining, and malware analysis with VirusTotal and GPT.

AI Governance & Responsible Use in Cybersecurity23:55
Explore AI governance in cybersecurity, covering rules for building, training, using, and monitoring high-risk AI; ensure transparency, human in the loop, data privacy, and robust defenses against AI-driven threats.
Risk Management Frameworks for GenAI19:52
Explore how GenAI risk frameworks like the NIST RMF, ISO 42001, and the OWASP Top 10 for LLMs guide governance, mapping, measuring, and managing AI risk.
Threat Modeling for AI/LLM Systems16:14
Model ai threats by mapping data, model, orchestration, infrastructure, and human layers. Apply owasp top 10 for llms, stride, and pasta to design guardrails and least privilege.
Best Practices for Securing AI Workloads16:08
Explore securing AI workloads from data to inference by enforcing encryption, data validation, authentication, logging, and defenses against data poisoning and prompt injection, aligned with NIST and EU AI Act.
Future Trends in AI Security Policy (2025 and beyond)13:25
Explore how binding ai security rules reshape governance from the eu ai act and gdpr-style fines to sector-specific nist and iso 42001 standards, with red teaming and living policies.

LLM & GenAI Fundamentals – Architectures & Pipelines9:22
Explore the architectures and pipelines of LLM and GenAI, from tokens and embeddings to attention and decoding, with red teaming and safety guardrails.
Common Attack Vectors on AI Models8:35
Explore training and inference time attack vectors, such as data poisoning, backdoors, model inversion, and membership inference, and learn how red teaming treats ai systems as pipelines with entry points.
Adversarial Testing of AI/GenAI Apps8:47
Explore adversarial testing for Gen AI across the entire pipeline, applying red-team methods to reconnaissance and threat surface mapping, plan attack scenarios, and validate defenses with repeatable campaigns.
AI Agent Red Teaming – Exploiting Autonomous Systems8:35
Explore how AI systems face training and inference time attacks, including data poisoning, backdoors, model inversion, and supply chain risks, framed as a living pipeline for red teaming.
Reporting & remediation strategies9:18
Anchor AI vulnerability findings in MITRE, ATLAS, or Adversarial ML Threat Metrics, linking prompt injection, data poisoning, and model extraction to actionable remediation and clear risk reporting.

OWASP Top 10 For LLMs5:48
Defend LLMs from prompt injection and multi-stage attacks by applying contextual sanitizers, strict prompt schemes, red teaming, and defense in depth across tokens, outputs, and supply chains.
MITRE ATLAS Overview5:46
Mitre atlas overview in ai defense, mapping data, models, and serving topology to attacker techniques from extraction and poisoning to evasion, with mitigations.
Securing GenAI Pipelines And APIs5:54
Segment ingestion, inference, and delivery paths to prevent defects from cascading into breaches. Enforce strong authentication, sanitization, and automated CI validation to detect misconfigurations, secret leaks, and unsafe integrations.
Detection And Prevention Of API Exploits3:57
Identify and prevent api exploits targeting ai endpoints by enforcing strict validation, authentication, rate limiting, and guardrails against prompt, sql, and command injections.

Requirements

Basic cybersecurity knowledge and beginner-level familiarity with IT or scripting are recommended, but no advanced AI or ML experience is required.

Description

AI-Driven Cyber Defense: GenAI, Red Teaming & Modern Threats is a comprehensive, hands-on course designed to prepare cybersecurity professionals for the rapidly evolving era of AI-powered attacks and defenses. As organizations adopt GenAI and LLM-based tools at scale, new risks are emerging—from prompt injection and model manipulation to automated offensive agents capable of bypassing traditional security controls. This course bridges the current skills gap by combining foundational knowledge with practical, real-world application, and includes a downloadable ebook in the final lecture for continued learning and reference.

Across the program, learners will explore how GenAI is reshaping both sides of the cyber battlefield. You will learn to leverage AI for threat detection, incident response, and security automation while also understanding how adversaries weaponize AI to exploit vulnerabilities in models, APIs, and enterprise environments. The course introduces leading frameworks including MITRE ATLAS, OWASP Top 10 for LLMs, and GenAI risk-management best practices for 2025, ensuring students gain industry-aligned competencies rather than theory alone.

Hands-on labs and guided exercises will take you from basic AI red-teaming techniques to advanced attack simulations using AI agents, allowing you to safely test model behavior, identify weaknesses, and design resilient systems. You will also learn how to secure API-driven GenAI deployments, implement guardrails and monitoring strategies, and evaluate tooling for enterprise-grade protection.

Whether you are part of a SOC team, an ethical hacker, a security architect, or an IT professional transitioning into the AI-security space, this course equips you with the practical skills and strategic insight needed to defend modern environments. By the end, you will be ready to confidently assess, secure, and operationalize GenAI in real-world cybersecurity scenarios—staying ahead of threats instead of reacting to them.

Who this course is for:

Cybersecurity and IT professionals, students, and red or blue teamers who want to apply AI and GenAI tools in real-world security.

AI-Driven Cyber Defense: GenAI, Red Teaming & Modern Threats

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Course content

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GenAI & Cybersecurity – Frameworks and Best Practices 20255 lectures • 1hr 30min

AI Red Teaming – From Basics to AI Agents5 lectures • 45min

GenAI Cybersecurity – OWASP Top 10, MITRE ATLAS & API Attacks4 lectures • 21min

Requirements

Description

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