AI Security Training in Belgrade

AAISM serves as 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 aiming to implement robust 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 that support the responsible deployment of AI.
• Align AI security policies with both organizational objectives and regulatory expectations.
• Strengthen 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

• To arrange tailored training that aligns with your organizational AI strategy, please contact us to customize the course.

This instructor-led, live training in Belgrade (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, 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 wish 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 Belgrade (online or on-site) is designed for AI and cybersecurity professionals at an intermediate level 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 Belgrade (online or onsite) is designed for 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 against 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 training session (available online or onsite) is designed for advanced professionals aiming 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 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 Belgrade (online or onsite) is designed for intermediate-level engineers and security experts who aim to defend AI models deployed at the edge against threats such as tampering, data exposure, adversarial attacks, and physical breaches.

Upon completing this training, participants will be equipped to:

• Identify and evaluate security risks within edge AI deployments.
• Implement tamper-resistant measures and encrypted inference methods.
• Strengthen edge-deployed models and secure data pipelines.
• Deploy threat mitigation strategies tailored to embedded and constrained systems.

This instructor-led, live training in Belgrade (online or onsite) targets advanced 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.

The adoption of 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 Belgrade (online or onsite) is designed for intermediate-level enterprise leaders seeking to understand how to responsibly govern and secure AI systems in compliance with emerging global frameworks such as the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

Upon completing this training, participants will be able to:

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

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs, 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 Belgrade (online or onsite) is designed for intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models, utilizing 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 Belgrade (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.

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 Belgrade (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.