Riesgos de la IA generativa y ciberseguridad: seguridad LLM

Riesgos de la IA generativa y ciberseguridad: seguridad LLM – con el Dr. Amar Massoud (Español)
Free generative‑AI security course on Udemy – Updated July 2026

If you’re searching for a free generative AI course, a Udemy course on IA generativa y ciberseguridad, or want to learn LLM security online, this Spanish‑language training delivers exactly that. The program focuses on practical attack‑and‑defense techniques for large language models (LLM), covering OWASP Top 10 for LLM, MITRE ATLAS, and European AI regulations. By the end of the curriculum you can confidently assess, exploit, and harden generative‑AI systems, earning a Udemy certificate that validates your new skill set.

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What You'll Learn

• Build secure LLM pipelines by mapping attack surfaces and applying defense‑in‑depth controls.
• Master prompt injection, indirect injection, and jailbreak techniques in a local lab environment.
• Learn how each LLM attack aligns with the OWASP Top 10 for LLM and MITRE ATLAS threat techniques.
• Understand regulatory frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001 and apply them to generative‑AI projects.
• Create monitoring and incident‑response workflows that detect anomalous model behavior in real time.
• Implement secure development lifecycle (SDLC) practices for AI, including red‑team exercises and risk assessments.
• Apply emerging risk analysis for agentic AI, AI‑vs‑AI threats, and post‑quantum cryptography impacts.
• Analyze real‑world case studies from the fictitious NovaStar AI company to solidify hands‑on expertise.

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Course Details

• Instructor: Dr. Amar Massoud (Español)
• Duration: 5 hours of video + extensive lab setup (≈1 hour) and 14 hands‑on labs (≈9‑12 hours)
• Level: Intermediate (requires basic cybersecurity knowledge)
• Language: Español (es‑LA)
• Enrolled students: 957
• Certificate: Yes, upon completion
• Includes: Lifetime access, mobile‑friendly playback, downloadable resources

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What This Course Covers

1. Mapping the Attack Surface

• Identificar vulnerabilidades de LLM usando OWASP Top 10 para LLM.
• Relacionar cada vulnerabilidad con técnicas de MITRE ATLAS.
• Documentar la superficie de ataque de la aplicación ficticia NovaStar AI.
• Generar diagramas de riesgo que sirvan como base para defensas posteriores.

2. Prompt Injection & Jailbreaks

• Ejecutar inyecciones de prompts directas e indirectas en un modelo Llama local.
• Desarrollar scripts de jailbreak que eludan filtros de entrada.
• Analizar respuestas del modelo para detectar alucinaciones explotables.
• Registrar resultados en un entorno controlado sin necesidad de API externa.

3. Data Poisoning, Bias & Resource Exhaustion

• Simular envenenamiento de datos y medir su impacto en la salida del modelo.
• Detectar y mitigar sesgos introducidos por datos maliciosos.
• Implementar límites de recursos para prevenir ataques de agotamiento (DoS).
• Evaluar técnicas de mitigación como sanitización de datos y control de temperatura.

4. Defense‑in‑Depth Architecture

• Diseñar validación de entradas y filtrado de salidas robusto.
• Configurar barreras de protección, aislamiento de contenedores y sandboxing.
• Implementar monitorización continua con alertas de comportamiento anómalo.
• Integrar controles de seguridad en el ciclo de desarrollo de IA (DevSecOps).

5. Regulatory Compliance & Governance

• Aplicar el Reglamento Europeo de Inteligencia Artificial a casos prácticos.
• Mapear requisitos del NIST AI Risk Management Framework (RMF).
• Implementar normas ISO/IEC 42001 para gestión de riesgos de IA.
• Realizar auditorías de cumplimiento y gestionar propiedad intelectual.

6. Emerging Threats & Red‑Team Exercise

• Explorar riesgos de IA agéntica y ataques de IA contra IA.
• Analizar impactos de la criptografía poscuántica en modelos LLM.
• Conducir un ejercicio completo de red‑team contra NovaStar AI.
• Entregar un informe de evaluación de riesgos y plan de mitigación final.

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Who Should Take This Course

• Cybersecurity professionals tasked with protecting generative‑AI applications.
• Developers and solution architects who embed LLMs into products or services.
• CISO, risk managers, and compliance officers overseeing AI governance.
• Pen‑testers and red‑team members seeking hands‑on experience with AI‑specific attacks.
• Technical enthusiasts who want a practical, lab‑focused understanding of LLM security.

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Prerequisites

• Basic knowledge of cybersecurity fundamentals (threat modeling, OWASP, etc.).
• Familiarity with command‑line tools and containerization (Docker or similar).
• Recommended: Prior exposure to Python scripting and API interaction (not mandatory).

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Why Enroll in This Course

This Udemy training delivers a complete, hands‑on pathway to mastering LLM security without any cost when you apply the free coupon that is available for a limited time. The curriculum blends theory with 14 real‑world labs, ensuring you can both attack and defend generative‑AI systems confidently. As of July 2026, the course remains up‑to‑date with the latest regulatory frameworks and emerging threats, making it a superior alternative to generic AI tutorials that lack depth in security.

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Course Highlights

• Lifetime access to all video lectures, labs, and supplemental materials.
• Self‑paced learning allowing you to practice attacks on a local LLM at any time.
• Certificate of completion that can be added to LinkedIn or a professional portfolio.
• Mobile‑friendly content so you can study on smartphones or tablets.
• Comprehensive coverage of OWASP, MITRE ATLAS, EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Real‑world red‑team project that consolidates all skills into a final deliverable.

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Frequently Asked Questions

Q: Is this course really free?
A: Yes. By applying the free Udemy coupon, you can enroll at 100 % off for a limited period. The coupon grants full access to all