AI Security Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is designed for beginner to intermediate IT professionals seeking 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 instructor-led program in Plovdiv addresses governance, identity management, and red-teaming for agentic AI systems. It empowers advanced practitioners to design secure deployments, enforce least-privilege access, and perform adversarial testing to neutralize real-world threats in production settings.

This instructor-led, live training in Plovdiv (online or onsite) 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 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 (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.

This instructor-led training on Plovdiv empowers advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, reinforce models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This instructor-led, live training in Plovdiv (online or onsite) targets intermediate-level engineers and security professionals who wish to secure AI models deployed at the edge against threats such as tampering, data leakage, adversarial inputs, and physical attacks.

Upon completing this training, participants will be able to:

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

This instructor-led live training in Plovdiv (online or onsite) is designed for advanced professionals who wish to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within 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.

This instructor-led, live training in Plovdiv (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 Plovdiv (online or onsite) is aimed at intermediate-level to advanced-level 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 live, instructor-led training Plovdiv (online or on-site) is designed for intermediate-level professionals in machine learning and cybersecurity who aim to understand and mitigate emerging threats to AI models using both conceptual frameworks and practical defenses like robust training and differential privacy.

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

• Identify and classify AI-specific threats, including adversarial attacks, inversion, and data poisoning.
• Utilize tools such as the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate model resilience.
• Implement practical defenses, including adversarial training, noise injection, and privacy-preserving techniques.
• Design evaluation strategies for models in production that account for potential threats.

This instructor-led, live training in Plovdiv (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals seeking to understand 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 able to:

• Grasp the unique security risks inherent to 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 utilization with emerging standards, compliance guidelines, and ethical principles.

Informed by 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 course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants will learn how AI security diverges from traditional web security, examine common AI-specific threats such as prompt injection, RAG poisoning, and agent abuse, and understand how to protect AI systems using layered defenses like WAFs, AI gateways, API security measures, and guardrails. Through hands-on labs and real-world examples, students will acquire the skills needed to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses for production environments.

This course teaches software developers how to build AI-powered applications securely by design. Participants learn to protect chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats such as prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The course covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, helping developers build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in Plovdiv (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.

This instructor-led, live training in Plovdiv (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.