AI Security Training in Varna

The AAISM serves as an advanced framework designed for the assessment, governance, and management of security risks within artificial intelligence systems.

This instructor-led live training, available both online and onsite, targets advanced-level professionals seeking 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 recognized industry methodologies.
• Implement governance models that support responsible AI deployment.
• Align AI security policies with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability within AI-driven operations.

Course Format

• Instructor-led lectures enhanced by expert analysis.
• Hands-on workshops and assessment-driven activities.
• Practical exercises based on real-world AI governance scenarios.

Course Customization Options

• For training tailored to your organization’s AI strategy, please contact us to customize the course.

This instructor-led, live training in Varna (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 course delves into governance, identity management, and adversarial testing for agentic AI systems, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

Delivered as instructor-led live training (available online or onsite), this program is designed for advanced practitioners looking 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.
• Architect non-human identity and authentication workflows 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 uncover misuses, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy enforcement, 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 followed by an end-of-course assessment.

Course Customization Options

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

This instructor-led, live training in Varna (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.

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 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 Varna (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 Varna (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.

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 Varna (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 Varna (online or onsite) is aimed at 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 live, instructor-led training Varna (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 Varna (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.

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 Varna (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.