AI Security Training in Banat

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

This instructor-led live training (available online or onsite) is designed for advanced professionals seeking to implement robust security controls and governance practices in enterprise AI environments.

Upon completion of this program, participants will be equipped to:

• Evaluate AI security risks using industry-recognized methodologies.
• Implement governance models for responsible AI deployment.
• Align AI security policies with organizational goals and regulatory expectations.
• Enhance 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

• For tailored training aligned to your organizational AI strategy, please contact us to customize the course.

This instructor-led live training in Banat (available online or onsite) targets IT professionals from beginner to intermediate levels who intend to understand and implement AI TRiSM within their organizations.

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

• Comprehend the core concepts and value 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 addresses governance, identity management, and adversarial testing for agentic AI systems, emphasizing 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 aim to design, secure, and evaluate agent-based AI systems in production environments.

Upon completion 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 Banat (online or onsite) is designed for intermediate-level AI and cybersecurity professionals who seek to understand and mitigate security vulnerabilities specific to AI models and systems, particularly within highly regulated sectors such as finance, data governance, and consulting.

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

• Identify the types of adversarial attacks targeting AI systems and the methods used to defend against them.
• Apply model hardening techniques to secure machine learning pipelines.
• Ensure data security and integrity within machine learning models.
• Navigate regulatory compliance requirements pertinent to AI security.

This instructor-led live training in Banat (online or onsite) is designed for advanced security professionals and ML specialists who aim 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 targeting 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 on low-power, resource-constrained devices 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 completing 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.

Course Format

• Engaging lectures supported by expert-led discussions.
• Practical exercises focusing on 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 meet their specific security and compliance needs.

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

Upon completing this training, participants will be capable of:

• Grasping and comparing essential privacy-preserving techniques in ML.
• Building federated learning systems using open-source tools.
• Implementing differential privacy for secure data sharing and model training.
• Leveraging encryption and secure computation methods to safeguard 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 Banat (online or onsite) is designed for intermediate-level enterprise leaders who want to learn 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.

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 Banat (online or in-person) targets intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks linked to LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered outputs, while implementing security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Comprehend the primary vulnerabilities of LLM-based systems.
• Apply secure design principles to the architecture of LLM applications.
• Employ tools like Guardrails AI and LangChain for validation, filtering, and safety assurance.
• Integrate methods such as sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This instructor-led live training in Banat (online or onsite) is designed for intermediate-level machine learning and cybersecurity professionals who want to understand and mitigate emerging threats against AI models. The course covers both conceptual frameworks and hands-on defenses, such as robust training and differential privacy.

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

• Identify and classify AI-specific threats, including adversarial attacks, inversion, and poisoning.
• Utilize tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and test models.
• Apply practical defenses such as adversarial training, noise injection, and privacy-preserving techniques.
• Design threat-aware model evaluation strategies for production environments.

This instructor-led, live training in Banat (online or onsite) targets beginner-level IT security, risk, and compliance professionals seeking to grasp foundational AI security concepts, threat vectors, and global frameworks such as the 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.

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 provides a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants learn how AI security differs from traditional web security, explore common AI-specific threats such as prompt injection, RAG poisoning, and agent abuse, and understand how to protect AI systems using layered defenses including WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world examples, students gain the skills 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 how 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 Banat (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 Banat (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.