Protect AI systems from evolving threats with hands-on, instructor-led training in AI Security.
These live courses teach how to defend machine learning models, counter adversarial attacks, and build trustworthy, resilient AI systems.
Training is available as online live training via remote desktop or onsite live training in Guatemala City, featuring interactive exercises and real-world use cases.
Onsite live training can be delivered at your location in Guatemala City or at a NobleProg corporate training center in Guatemala City.
Also known as Secure AI, ML Security, or Adversarial Machine Learning.
NobleProg – Your Local Training Provider
Guatemala - Europlaza
5a Avenue 5-55 Zone 14, Guatemala City, Guatemala, 1014
The Guatemala Europlaza Center is located on the second floor of one of the four modern 19-story towers that form the Europlaza World Business Center. The Guatemala Europlaza Center is convenient for public transport connections, including the new Transmetro bus system introduced to improve traffic flow, and the city Center is just 10 minutes away.
Guatemala - Citibank Tower
3r avenue 13-78, zone 10 , Guatemala City, Guatemala, 01010
Experience convenient access to Citibank Tower via public transportation or by car, with on-site parking available. Additionally, relish seamless international travel with the airport a mere 15-minute drive from the tower.
This instructor-led, live training in Guatemala City (online or onsite) is aimed at intermediate-level enterprise leaders who wish to understand how to govern and secure AI systems responsibly and in compliance with emerging global frameworks such as the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.By the end of this training, participants will be able to:
Understand the legal, ethical, and regulatory risks of using AI across departments.
Interpret and apply major AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
Establish security, auditing, and oversight policies for AI deployment in the enterprise.
Develop procurement and usage guidelines for third-party and in-house AI systems.
This instructor-led, live training in Guatemala City (online or onsite) is aimed at intermediate-level to advanced-level AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating security controls like input validation, human-in-the-loop oversight, and output guardrails.By the end of this training, participants will be able to:
Understand the core vulnerabilities of LLM-based systems.
Apply secure design principles to LLM app architecture.
Use tools such as Guardrails AI and LangChain for validation, filtering, and safety.
Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.
This instructor-led, live training in Guatemala City (online or onsite) is aimed at intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models, using both conceptual frameworks and hands-on defenses like robust training and differential privacy.By the end of this training, participants will be able to:
Identify and classify AI-specific threats such as adversarial attacks, inversion, and poisoning.
Use tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and test models.
Apply practical defenses including adversarial training, noise injection, and privacy-preserving techniques.
Design threat-aware model evaluation strategies in production environments.
This instructor-led, live training in Guatemala City (online or onsite) is aimed at beginner-level IT security, risk, and compliance professionals who wish to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO/IEC 42001.By the end of this training, participants will be able to:
Understand the unique security risks introduced by AI systems.
Identify threat vectors such as adversarial attacks, data poisoning, and model inversion.
Apply foundational governance models like the NIST AI Risk Management Framework.
Align AI use with emerging standards, compliance guidelines, and ethical principles.
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