AI Security Training in Turin

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

This instructor-led, live training (available online or onsite) is tailored for advanced-level professionals seeking to implement effective security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Evaluate AI security risks using industry-recognized methodologies.
• Implement governance models for responsible AI deployment.
• Align AI security policies with organizational goals and regulatory expectations.
• Enhance resilience and accountability within AI-driven operations.

Course Format

• Facilitated lectures supported by expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises using real-world AI governance scenarios.

Course Customization Options

• For tailored training aligned to your organizational AI strategy, please contact us to customize the course.

This instructor-led, live training in Turin (online or onsite) is designed for beginner to intermediate IT professionals aiming to understand and implement AI TRiSM in their organizations.

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

• Understand 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 course explores the governance, identity management, and adversarial testing essential for agentic AI systems, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

Designed for advanced practitioners, this instructor-led live training (available online or onsite) equips you to design, secure, and evaluate agent-based AI systems within production environments.

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

• Establish governance models and policies to ensure safe agentic AI deployments.
• Architect non-human identity and authentication flows for agents, enforcing least-privilege access.
• Implement tailored access controls, audit trails, and observability mechanisms for autonomous agents.
• Plan and execute red-team exercises to identify misuse vectors, 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 and a final course assessment.

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

This instructor-led, live training in Turin (online or onsite) is designed for intermediate-level AI and cybersecurity professionals who wish to understand and address security vulnerabilities specific to AI models and systems, particularly within highly regulated industries such as 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 Turin (online or onsite) is aimed at advanced-level security professionals and ML specialists who wish to simulate attacks on AI systems, uncover vulnerabilities, and enhance the robustness of deployed AI models.

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

• Simulate real-world threats to machine learning models.
• Generate adversarial examples to test model robustness.
• Assess the attack surface of AI APIs and pipelines.
• Design red teaming strategies for AI deployment environments.

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

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

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

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in resource-constrained environments.

Course Format

• Engaging lectures supported by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML frameworks.

Course Customization Options

• Organizations can request a tailored version of this training to align with their specific security and compliance requirements.

This instructor-led, live training in Turin (online or onsite) is designed for intermediate-level engineers and security professionals who aim to safeguard AI models deployed at the edge against threats like tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identify and assess security risks in edge AI deployments.
• Apply tamper resistance and encrypted inference techniques.
• Harden edge-deployed models and secure data pipelines.
• Implement threat mitigation strategies specific to embedded and constrained systems.

This instructor-led, live training in Turin (online or onsite) targets advanced professionals aiming to implement and evaluate techniques like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning pipelines.

Upon completing this training, participants will be equipped to:

• Grasp and evaluate the primary privacy-preserving methods used in machine learning.
• Build federated learning systems leveraging open-source tools.
• Utilize differential privacy to ensure secure data sharing and model training.
• Employ encryption and secure computation strategies to shield model inputs and outputs.

Artificial Intelligence (AI) introduces new dimensions of operational risk, governance challenges, and cybersecurity exposure for government agencies and departments.

This instructor-led, live training (online or onsite) is aimed at public sector IT and risk professionals with limited prior experience in AI who wish to understand how to evaluate, monitor, and secure AI systems within a government or regulatory context.

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

• Interpret key risk concepts related to AI systems, including bias, unpredictability, and model drift.
• Apply AI-specific governance and auditing frameworks such as NIST AI RMF and ISO/IEC 42001.
• Recognize cybersecurity threats targeting AI models and data pipelines.
• Establish cross-departmental risk management plans and policy alignment for AI deployment.

Format of the Course

• Interactive lecture and discussion of public sector use cases.
• AI governance framework exercises and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Turin (online or onsite) is designed for 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 Turin (online or onsite) targets intermediate to advanced 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 Turin (online or onsite) is designed for intermediate-level professionals in machine learning and cybersecurity who wish to understand and mitigate emerging threats to AI models, utilizing both conceptual frameworks and practical defenses such as 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 Turin (online or onsite) is designed for IT security, risk, and compliance professionals at the beginner level who seek to master foundational AI security concepts, threat vectors, and global frameworks such as the NIST AI RMF and ISO/IEC 42001.

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

• Grasping the distinctive security risks associated with AI systems.
• Recognizing threat vectors including adversarial attacks, data poisoning, and model inversion.
• Implementing foundational governance models such as the NIST AI Risk Management Framework.
• Aligning AI initiatives with emerging standards, compliance guidelines, and ethical principles.

Based on the latest guidance from the OWASP GenAI Security Project, 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 Turin (online or onsite) is designed for security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.