AI Security Training in Tashkent

AAISM is an advanced framework for assessing, governing, and managing security risks in artificial intelligence systems.

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

At the conclusion of this program, participants will be prepared 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.

Format of the Course

• 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 Tashkent (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to understand and implement AI TRiSM in 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 course explores governance, identity management, and adversarial testing for agentic AI systems, emphasizing enterprise-safe deployment strategies and practical red-teaming methodologies.

This instructor-led, live training (available online or onsite) is designed for advanced practitioners aiming 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 agentic AI deployments.
• Design 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 execute red-team exercises to identify potential misuse, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy adjustments, engineering controls, and 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 requests, please contact us to make arrangements.

This instructor-led, live training in Tashkent (online or onsite) targets intermediate-level AI and cybersecurity professionals aiming to understand and address security vulnerabilities specific to AI models and systems, particularly within highly regulated industries like finance, data governance, and consulting.

Upon completing this training, participants will be able to:

• Identify various types of adversarial attacks targeting AI systems and learn defensive strategies.
• Apply model hardening techniques to safeguard machine learning pipelines.
• Maintain data security and integrity within machine learning models.
• Comprehend and meet regulatory compliance requirements related to AI security.

This instructor-led, live training in Tashkent (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-limited 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 completing 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 constrained environments.

Course Format

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

Customization Options

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

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level engineers and security professionals who aim to protect edge-deployed AI models from threats such as tampering, data leakage, adversarial inputs, and physical attacks.

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

• Recognize and evaluate security vulnerabilities in edge AI deployments.
• Utilize techniques for tamper resistance and encrypted inference.
• Strengthen models deployed at the edge and secure data pipelines.
• Execute threat mitigation strategies tailored to embedded and resource-constrained systems.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning pipelines.

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

• Understand and compare key privacy-preserving techniques in ML.
• Implement federated learning systems using open-source frameworks.
• Apply differential privacy for safe data sharing and model training.
• Use encryption and secure computation techniques to protect model inputs and outputs.

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

This instructor-led, live training (online or onsite) is designed for public sector IT and risk professionals with limited prior experience in AI who want to learn 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:

• Understand 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.
• Identify cybersecurity threats targeting AI models and data pipelines.
• Develop cross-departmental risk management plans and ensure policy alignment for AI deployment.

Course Format

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

Course Customisation Options

• To request a customised training version of this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level enterprise leaders who aim to understand how to govern and secure AI systems responsibly, ensuring compliance with emerging global frameworks such as 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:

• Comprehend the legal, ethical, and regulatory risks associated with deploying AI across various departments.
• Interpret and apply key AI governance frameworks, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Establish robust security, auditing, and oversight policies for AI deployment within the enterprise.
• Develop procurement and usage guidelines for both third-party and in-house AI systems.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while integrating 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 Tashkent (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 Tashkent (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals who aim to grasp 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 associated with AI systems.
• Identify threat vectors, including adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models like the NIST AI Risk Management Framework.
• Align AI usage 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 Tashkent (online or onsite) targets security engineers and compliance officers seeking to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.