Cybersecurity in AI Systems Training Course

AAISM represents an advanced framework designed for assessing, governing, and managing security risks within artificial intelligence systems.

This instructor-led, live training, available either online or on-site, 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 industry-recognized methodologies.
• Implement governance models for the responsible deployment of AI.
• 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 with your organizational AI strategy, please contact us to customize the course.

This instructor-led live training in Sweden (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.

The adoption of Artificial Intelligence (AI) brings forth novel operational risks, governance complexities, and cybersecurity vulnerabilities for government entities and departments.

This instructor-led training, available online or onsite, is designed for public sector IT and risk professionals who have limited prior exposure to AI but seek to comprehend how to assess, monitor, and safeguard AI systems within governmental or regulatory environments.

Upon completion of this training, participants will be capable of:

• Interpreting fundamental risk concepts associated with AI systems, such as bias, unpredictability, and model drift.
• Implementing AI-specific governance and auditing frameworks, including NIST AI RMF and ISO/IEC 42001.
• Identifying cybersecurity threats directed at AI models and data pipelines.
• Developing cross-departmental risk management strategies and ensuring policy alignment for AI implementation.

Course Format

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

Customization Options

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

This instructor-led, live training in Sweden (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 instructor-led, live training in Sweden (online or onsite) targets 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 output, while incorporating security controls like input validation, human-in-the-loop oversight, and output guardrails.

Upon completion of this training, participants will be capable of:

• Grasping the fundamental vulnerabilities inherent in LLM-based systems.
• Implementing secure design principles within LLM application architecture.
• Utilizing tools like Guardrails AI and LangChain for validation, filtering, and safety measures.
• Incorporating techniques such as sandboxing, red teaming, and human-in-the-loop reviews into production-grade pipelines.

This instructor-led, live training in Sweden (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 Sweden (online or onsite) is designed for IT security, risk, and compliance professionals at the beginner level who wish to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO/IEC 42001.

Upon completing this training, participants will be able to:

• Comprehend the distinct security risks inherent to AI systems.
• Recognize threat vectors, including adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models, such as the NIST AI Risk Management Framework.
• Align AI usage 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 guided, real-time workshop in Sweden (delivered virtually or in-person) targets experienced professionals looking to deploy and assess methods like collaborative learning, secure multi-party processing, homomorphic encryption, and differential privacy within practical machine learning workflows.

Upon completing this workshop, attendees will be capable of:

• Grasping and evaluating essential techniques for protecting privacy in ML.
• Building collaborative learning networks via open-source tools.
• Leveraging differential privacy to ensure secure data sharing and model development.
• Employing encryption and secure computing methods to safeguard model inputs and results.

This instructor-led, live training in Sweden (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, live training in Sweden (online or onsite) is aimed at 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.

By the end 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 Sweden (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.

TinyML represents a methodology for deploying machine learning models onto low-power devices with limited resources, 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 integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be equipped to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI solutions.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Implement best practices for secure data handling in resource-constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML platforms.

Course Customization Options

• Organizations can request a customized version of this training to align with their specific security and compliance requirements.

This course addresses governance, identity management, and adversarial testing for agentic AI systems, emphasizing enterprise-safe deployment patterns and practical red-teaming methodologies.

Delivered as an instructor-led, live training session (available online or on-site), this program is designed for advanced practitioners seeking to design, secure, and evaluate agent-based AI systems within production environments.

Upon completion of this training, participants will be capable of:

• Defining governance models and policies to ensure the safe deployment of agentic AI.
• Designing non-human identity and authentication workflows for agents, ensuring least-privilege access.
• Implementing access controls, audit trails, and observability mechanisms tailored for autonomous agents.
• Planning and executing red-team exercises to identify misuse scenarios, escalation paths, and data exfiltration risks.
• Mitigating common threats to agentic systems through policy, engineering controls, and continuous monitoring.

Course Format

• Interactive lectures combined with threat-modeling workshops.
• Practical labs covering identity provisioning, policy enforcement, and adversary simulation.
• Red-team versus blue-team exercises and a comprehensive end-of-course assessment.

Customization Options