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 Lima, featuring interactive exercises and real-world use cases.
Onsite live training can be delivered at your location in Lima or at a NobleProg corporate training center in Lima.
Also known as Secure AI, ML Security, or Adversarial Machine Learning.
NobleProg – Your Local Training Provider
Lima - Edificio Tempus San Isidro
Av. Santo Toribio 115, Lima, peru, 27
Edificio Tempus is a Class A brand new office building is strategically located in the corner of Av Victor Belaunde and Av. San Toribio in the heart of San Isidro. There is plenty of parking on 4 parking decks. This center is only a short 300 mt walk in the same block from our exiting Real Seis in located in Centro Empresarial.
Lima - Miguel Grau
Av. Almte. Miguel Grau 709 Lima , Lima, Peru, 15063
Expand your business horizons with a workspace in Barranco, one of Lima's 43 districts, boasting excellent connectivity to its surroundings. Enjoy effortless commuting to your office via the Avenida Almirante Miguel Grau S/N and Bulevar - Embarque Sur 2 bus stops.
This instructor-led, live training in Lima (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 Lima (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 Lima (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 Lima (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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