Trustworthy & Responsible AI (NIST RMF & EU AI Act)

Why This Course

As Artificial Intelligence becomes embedded in critical business and societal functions, trust, transparency, and accountability have become essential pillars for sustainable AI adoption.

Global frameworks such as the NIST AI Risk Management Framework (RMF) and the EU AI Act are setting new benchmarks for how organizations design, deploy, and govern AI systems responsibly.

This program equips participants with the knowledge, tools, and methodologies to operationalize trustworthy and responsible AI within their organizations.

It bridges policy, ethics, and technology — enabling participants to align AI innovation with compliance, safety, and human-centric values.

What You’ll Learn and Practice

By joining this program, you will:

• Understand the foundations of trustworthy and responsible AI governance.
• Learn the structure and application of the NIST AI Risk Management Framework (RMF).
• Gain a deep understanding of the EU AI Act and its implications for global organizations.
• Develop practical strategies to assess, mitigate, and monitor AI risks.
• Build frameworks for transparency, accountability, and ethical AI deployment.

The Program Flow

Day 1: Foundations of Trustworthy & Responsible AI

• Defining trustworthy AI: fairness, accountability, transparency, and ethics.
• Global landscape of AI regulation and governance initiatives.
• Principles of responsible AI from OECD, UNESCO, and ISO perspectives.
• Risk categories: data bias, model explainability, security, and societal impact.
• Workshop: assessing your organization’s AI trust maturity level.

Day 2: The NIST AI Risk Management Framework (RMF)

• Overview and purpose of the NIST AI RMF (released 2023).
• The four core functions: Govern, Map, Measure, and Manage.
• Practical tools for risk identification and mitigation in AI lifecycle stages.
• Aligning NIST RMF with organizational AI strategies and MLOps workflows.
• Exercise: applying NIST RMF to evaluate an AI project’s risk posture.

Day 3: The EU AI Act — Regulation, Risk Classification, and Compliance

• The scope and objectives of the European Union AI Act.
• AI risk categories: unacceptable, high-risk, limited-risk, and minimal-risk systems.
• Obligations for providers, deployers, and users of AI systems.
• Conformity assessments, documentation, and CE marking requirements.
• Case study: mapping an AI use case to the EU AI Act risk levels and obligations.

Day 4: Operationalizing Responsible AI in Practice

• Embedding AI governance into product development and decision-making.
• Data governance, human oversight, and model transparency.
• Designing explainable and auditable AI systems.
• Integrating ethical review, fairness testing, and bias mitigation pipelines.
• Simulation: designing an internal AI governance and risk management framework.

Day 5: Future Readiness — Global Compliance, Auditing & Implementation

• Harmonizing international compliance: NIST RMF, EU AI Act, ISO/IEC 42001 (AI Management Systems).
• AI audits and continuous monitoring processes.
• Balancing innovation and compliance through adaptive governance.
• Preparing for the future: generative AI, autonomous systems, and emerging regulations.
• Action workshop: creating a roadmap for responsible AI implementation in your organization.

Individual Impact

• Gain a deep understanding of AI ethics, risk management, and governance frameworks.
• Strengthen the ability to align AI initiatives with regulatory and ethical requirements.
• Build confidence in managing AI compliance across global jurisdictions.
• Develop leadership skills in responsible technology management.
• Enhance reputation as a trusted professional in AI governance and policy implementation.

Work Impact

• Strengthen compliance with global AI regulations and ethical standards.
• Reduce regulatory, reputational, and operational risks associated with AI systems.
• Build transparency, fairness, and accountability into AI-driven operations.
• Improve public and stakeholder trust through responsible innovation.
• Establish a scalable AI governance and compliance framework across teams.

Training Methodology

This program combines policy insight, risk management practices, and hands-on implementation exercises for practical learning.

Learning methods include:

• Case studies based on NIST RMF and EU AI Act applications.
• Group workshops on AI risk assessment and governance design.
• Real-world analysis of ethical and regulatory AI dilemmas.
• Interactive discussions on emerging AI policy trends.
• Toolkits, templates, and checklists for AI risk and compliance audits.

Beyond the Course

Upon completion, participants will be equipped to lead and implement trustworthy AI governance strategies aligned with NIST RMF and the EU AI Act.

They will leave ready to operationalize responsible AI practices — ensuring safety, compliance, and ethical excellence while enabling innovation in the age of intelligent systems.