Online AI Security Training in US

The Advanced Artificial Intelligence Security Management (AAISM) framework is designed for assessing, governing, and managing security risks in artificial intelligence systems.

This instructor-led, live training (available online or onsite) is targeted at advanced-level professionals aiming to implement robust security controls and governance practices for 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.

Format of the Course

• Facilitated lectures complemented by expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises using real-world AI governance scenarios.

Course Customization Options for Government

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

This instructor-led, live training in [location] (online or onsite) is designed for government IT professionals at the beginner to intermediate levels who wish to understand and implement AI TRiSM in their organizations. By the end of this training, participants will be able to: - Comprehend the fundamental concepts and significance of AI trust, risk, and security management. - Identify and mitigate risks associated with AI systems for government operations. - Apply security best practices for AI in a government context. - Understand regulatory compliance and ethical considerations specific to AI use in government. - Develop strategies for effective AI governance and management for government applications.

This course covers 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 (online or onsite) is aimed at advanced-level practitioners who wish to design, secure, and evaluate agent-based AI systems in production environments for government. 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 (available online or onsite) is designed for intermediate-level AI and cybersecurity professionals who seek to understand and mitigate the unique security challenges associated with AI models and systems. This training is particularly relevant for those working in highly regulated sectors such as finance, data governance, and consulting. By the end of this training, participants will be able to: - Identify and understand various types of adversarial attacks targeting AI systems and learn effective methods to defend against them. - Implement model hardening techniques to enhance the security of machine learning pipelines. - Ensure the security and integrity of data used in machine learning models. - Navigate and comply with regulatory requirements related to AI security, ensuring alignment with public sector workflows for government.

This instructor-led, live training in [location] (online or onsite) is designed for advanced-level security professionals and machine learning specialists who aim to simulate attacks on artificial intelligence systems, identify vulnerabilities, and strengthen the resilience 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 evaluate model robustness. - Assess the attack surface of AI APIs and pipelines. - Develop red teaming strategies for AI deployment environments, ensuring alignment with public sector workflows and governance standards for government.

TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge. This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications for government. At the conclusion of this course, participants will be able to: - Identify security risks unique 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 for government.

This instructor-led, live training in US (online or onsite) is designed for intermediate-level engineers and security professionals who aim 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 evaluate security risks in edge AI deployments.
• Implement tamper resistance and encrypted inference techniques.
• Strengthen edge-deployed models and secure data pipelines.
• Develop threat mitigation strategies tailored to embedded and constrained systems.

This training is specifically aligned with the needs of professionals working in security-critical environments, including those for government agencies.

This instructor-led, live training in [location] (online or onsite) is designed for advanced-level professionals who aim to implement and evaluate privacy-preserving 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 methods in machine learning. - Implement federated learning systems using open-source frameworks for government applications. - Apply differential privacy techniques to ensure safe data sharing and model training. - Utilize encryption and secure computation methods to protect both inputs and outputs of machine learning models.

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 designed for 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 government 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 (online or onsite) is designed for intermediate-level government 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 associated with using AI across government departments. - Interpret and apply major AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001) for government operations. - Establish security, auditing, and oversight policies for AI deployment within government agencies. - Develop procurement and usage guidelines for third-party and in-house AI systems for government use.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who seek to identify and mitigate risks associated with LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered output. The training will cover the implementation of security controls such as 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 application architecture. - Utilize tools like Guardrails AI and LangChain for validation, filtering, and safety measures. - Integrate techniques such as sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines. This training is tailored to enhance the security and reliability of AI applications for government use, ensuring they meet the highest standards of governance and accountability.

This instructor-led, live training (online or onsite) is aimed at intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models. The training will utilize 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. - Use 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, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training (available online or on-site) is designed for beginner-level IT security, risk, and compliance professionals who wish to gain a foundational understanding of AI security concepts, threat vectors, and global frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001. By the end of this training, participants will be able to: - Understand the unique security risks associated with AI systems. - Identify potential threat vectors, including adversarial attacks, data poisoning, and model inversion. - Apply foundational governance models, such as the NIST AI Risk Management Framework for government applications. - Align the use of AI with emerging standards, compliance guidelines, and ethical principles.

In accordance with the latest OWASP GenAI Security Project guidance, participants will gain the skills to identify, assess, and mitigate AI-specific threats through practical exercises and real-world scenarios. This training is designed to enhance cybersecurity capabilities for government agencies, ensuring alignment with public sector workflows, governance, and accountability.