Privacy-Preserving Machine Learning Training Course

AAISM represents an advanced framework designed for assessing, governing, and managing security risks within artificial intelligence systems.

This instructor-led, live training, available either online or onsite, targets advanced-level professionals seeking to implement robust security controls and governance practices for enterprise AI environments.

Upon completion of this program, participants will be equipped to:

• Assess AI security risks using industry-recognized methodologies.
• Implement governance models that support the responsible deployment of AI.
• Align AI security policies with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability in AI-driven operations.

Course Format

• Instructor-led lectures complemented by expert analysis.
• Hands-on workshops and assessment-driven activities.
• Applied exercises utilizing real-world AI governance scenarios.

Customization Options

• For training tailored to your organization's AI strategy, please contact us to customize the course.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level enterprise leaders seeking to understand how to responsibly govern and secure AI systems in alignment with emerging global frameworks like the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

Upon completion of this training, participants will be able to:

• Grasp the legal, ethical, and regulatory risks associated with AI usage across various departments.
• Interpret and apply key AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Implement security, auditing, and oversight policies for AI deployment in the enterprise.
• Create procurement and usage guidelines for third-party and in-house AI systems.

Artificial Intelligence (AI) introduces new layers of operational risk, governance complexities, and cybersecurity vulnerabilities for government agencies and departments.

This instructor-led live training (available online or onsite) is designed for public sector IT and risk professionals with limited prior AI experience who want to learn how to evaluate, monitor, and secure AI systems within a government or regulatory framework.

Upon completion of this training, participants will be able to:

• Understand key risk concepts associated with AI systems, such as bias, unpredictability, and model drift.
• Implement AI-specific governance and auditing frameworks, including the NIST AI RMF and ISO/IEC 42001.
• Identify cybersecurity threats directed at AI models and data pipelines.
• Develop cross-departmental risk management plans and align policies for AI deployment.

Course Format

• Interactive lectures and discussions focusing on public sector use cases.
• Exercises on AI governance frameworks and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Customization Options

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Argentina (online or onsite) targets beginner to intermediate-level IT professionals seeking to understand and implement AI TRiSM within their organizations.

By the end of this training, participants will be able to:

• Grasp the key concepts and importance of AI trust, risk, and security management.
• Identify and mitigate risks associated with AI systems.
• Implement security best practices for AI.
• Understand regulatory compliance and ethical considerations for AI.
• Develop strategies for effective AI governance and management.

This instructor-led, live training in Argentina (online or on-site) targets intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs. The training emphasizes incorporating security controls such as 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 Argentina (online or onsite) targets intermediate-level AI and cybersecurity professionals seeking to understand and address security vulnerabilities specific to AI models and systems, especially within highly regulated industries like finance, data governance, and consulting.

By the end of this training, participants will be able to:

• Understand the types of adversarial attacks targeting AI systems and methods to defend against them.
• Implement model hardening techniques to secure machine learning pipelines.
• Ensure data security and integrity in machine learning models.
• Navigate regulatory compliance requirements related to AI security.

This instructor-led, live training in Argentina (online or on-site) is designed for security engineers and compliance officers who aim to strengthen their EXO deployments, manage model access controls, and govern AI workloads that run entirely on-premise.

This instructor-led, live training in Argentina (online or onsite) is designed for IT security, risk, and compliance professionals at the beginner level 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.

Based on the latest OWASP GenAI Security Project guidance, participants will learn to identify, assess, and mitigate AI-specific threats through hands-on exercises and real-world scenarios.

This instructor-led, live training in Argentina (online or onsite) is designed for advanced security professionals and ML specialists aiming to simulate attacks on AI systems, identify vulnerabilities, and strengthen the resilience of deployed AI models.

Upon completion of this training, participants will be capable of:

• Simulating real-world threats against machine learning models.
• Generating adversarial examples to evaluate model robustness.
• Assessing the attack surface of AI APIs and pipelines.
• Designing red teaming strategies for AI deployment environments.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level engineers and security professionals aiming to fortify edge-deployed AI models against threats like tampering, data leakage, adversarial inputs, and physical attacks.

Upon completion of this training, participants will be capable of:

• Identifying and evaluating security risks in edge AI deployments.
• Implementing tamper-resistant measures and encrypted inference techniques.
• Strengthening edge-deployed models and securing data pipelines.
• Deploying threat mitigation strategies tailored to embedded and resource-constrained systems.

This instructor-led live training in Argentina (online or onsite) targets intermediate-level professionals in machine learning and cybersecurity who aim to understand and mitigate emerging threats against AI models. The course integrates conceptual frameworks with practical defenses like robust training and differential privacy.

Upon completing this training, participants will be able to:

• Identify and categorize AI-specific threats, such as adversarial attacks, inversion, and poisoning.
• Utilize tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate model resilience.
• Implement practical defenses, including adversarial training, noise injection, and privacy-preserving techniques.
• Develop threat-aware model evaluation strategies for production environments.

TinyML represents a methodology for deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be equipped to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI solutions.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This course explores the governance, identity management, and adversarial testing required for agentic AI systems, with a strong emphasis on enterprise-safe deployment patterns and practical red-teaming techniques.

Delivered as instructor-led, live training (available online or onsite), this program is designed for advanced practitioners looking to design, secure, and evaluate agent-based AI systems within production environments.

Upon completion of this training, participants will be equipped to:

• Establish governance models and policies to ensure safe deployment of agentic AI.
• Architect non-human identity and authentication flows for agents, ensuring least-privilege access.
• Implement access controls, audit trails, and observability mechanisms specifically tailored for autonomous agents.
• Plan and conduct red-team exercises to identify misuse scenarios, escalation paths, and data exfiltration risks.
• Mitigate prevalent threats to agentic systems through policy enforcement, engineering controls, and continuous monitoring.

Course Format

• Interactive lectures combined with threat-modeling workshops.
• Hands-on labs covering identity provisioning, policy enforcement, and adversary simulation.
• Red-team/blue-team exercises culminating in an end-of-course assessment.

Customization Options

• For tailored training solutions, please reach out to us to arrange a customized session.