AI Security Training in Istanbul

The AAISM framework provides advanced methodologies for assessing, governing, and managing security risks within artificial intelligence systems.

This live, instructor-led training session (available online or onsite) is designed for senior professionals seeking to establish effective security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Assess AI security risks using recognized industry standards.
• Implement governance frameworks for 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

• Expert-led lectures combined with in-depth analysis.
• Hands-on workshops and assessment-driven activities.
• Practical exercises utilizing real-world AI governance scenarios.

Customization Options

• For bespoke training aligned with your organization’s AI strategy, please contact us to tailor the course.

This instructor-led live training in Istanbul (online or onsite) is aimed at beginner 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, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

This instructor-led, live training (available online or onsite) is designed for advanced-level practitioners who want to design, secure, and evaluate agent-based AI systems in production environments.

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

• Define governance models and policies for safe agentic AI deployments.
• Design non-human identity and authentication flows for agents with least-privilege access.
• Implement access controls, audit trails, and observability tailored to autonomous agents.
• Plan and execute red-team exercises to discover misuses, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy, engineering controls, and monitoring.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Istanbul (online or onsite) is designed for intermediate-level AI and cybersecurity professionals who aim to understand and address security vulnerabilities specific to AI models and systems, particularly in 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 Istanbul (online or onsite) is designed for advanced security professionals and ML specialists who aim to simulate attacks on AI systems, uncover vulnerabilities, and improve 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 involves deploying machine learning models onto low-power, resource-constrained devices that operate at the network edge.

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

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy 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.

Format of the Course

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

Course 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 Istanbul (online or onsite) is designed for intermediate-level engineers and security professionals who aim to safeguard AI models deployed at the edge against threats including tampering, data leakage, adversarial inputs, and physical attacks.

Upon completion 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 Istanbul (online or onsite) targets advanced professionals who aim to implement and assess methods such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning workflows.

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

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

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

This instructor-led live training (available online or onsite) targets public sector IT and risk professionals who have limited prior exposure to AI but wish to learn how to evaluate, monitor, and secure AI systems within a governmental or regulatory environment.

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

• Understand fundamental risk concepts associated with AI systems, such as bias, unpredictability, and model drift.
• Implement AI-specific governance and auditing frameworks, including NIST AI RMF and ISO/IEC 42001.
• Identify cybersecurity threats aimed at AI models and data pipelines.
• Develop cross-departmental risk management plans and ensure policy alignment for AI deployments.

Course Format

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

Customization Options

• For customized training requests, please contact us to arrange details.

This instructor-led, live training in Istanbul (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 Istanbul, available online or onsite, is designed for intermediate to advanced AI developers, architects, and product managers who seek to identify and mitigate risks linked to LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs. The curriculum emphasizes the integration of 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 Istanbul (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, delivered online or onsite, targets 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.

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 Istanbul (online or onsite) is aimed at security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.