Agentic AI Risk Management Training Course

Agentic AI Risk Management Training Course

Introduction

Agentic AI Risk Management Training Course is designed to equip professionals with advanced knowledge and practical skills to identify, assess, mitigate, and govern risks associated with autonomous AI agents, AI-driven decision systems, generative AI platforms, and intelligent automation ecosystems. As organizations rapidly adopt AI agents, large language models (LLMs), machine learning workflows, and autonomous business processes, managing risks such as AI hallucinations, security vulnerabilities, model failures, data privacy threats, algorithmic bias, compliance challenges, and operational disruptions has become a strategic priority. This course explores modern AI governance frameworks, responsible AI principles, AI risk assessment methodologies, agent behavior monitoring, trust management, and enterprise AI controls.

Participants will gain expertise in building robust AI risk management strategies aligned with emerging standards, regulatory requirements, and industry best practices. Through practical exercises and real-world case studies, learners will understand how to implement AI safety frameworks, agentic AI controls, risk mitigation strategies, continuous monitoring systems, and ethical AI governance models. The course enables organizations to confidently deploy intelligent agents while maintaining security, transparency, accountability, reliability, and regulatory compliance in an increasingly autonomous digital environment.

Course Duration

5 Days

Course Objectives

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

1. Understand the foundations of Agentic AI architectures, autonomous agents, and AI risk ecosystems. 
2. Apply advanced AI risk assessment frameworks for enterprise AI deployments. 
3. Identify and mitigate AI safety, security, and operational risks. 
4. Develop effective AI governance and responsible AI management strategies. 
5. Implement AI agent monitoring, evaluation, and control mechanisms. 
6. Manage risks associated with large language models (LLMs) and generative AI systems. 
7. Apply AI compliance, regulatory alignment, and governance standards. 
8. Design AI threat modeling and vulnerability assessment processes. 
9. Establish human-in-the-loop controls and AI accountability mechanisms. 
10. Evaluate risks related to AI autonomy, decision-making, and agent behavior. 
11. Implement continuous AI risk monitoring and observability practices. 
12. Build organizational capabilities for trustworthy and ethical AI adoption. 
13. Create enterprise-ready Agentic AI risk management roadmaps. 

Target Audience

1. AI Risk Managers and AI Governance Professionals 
2. Chief Information Officers (CIOs) and Technology Leaders 
3. Chief Information Security Officers (CISOs) 
4. Data Scientists and Machine Learning Engineers 
5. AI Engineers and LLM Application Developers 
6. Compliance, Audit, and Regulatory Professionals 
7. Enterprise Risk Management Teams 
8. Business Leaders Implementing AI Transformation 

Course Modules

Module 1: Foundations of Agentic AI Risk Management

• Understanding Agentic AI concepts, architectures, and autonomous workflows
• Exploring AI agents, LLM-based systems, and intelligent automation risks 
• Introduction to AI risk management lifecycle frameworks
• Understanding AI failure modes and operational challenges 
• Building an AI risk management mindset 
• Case Study: An organization experiences inaccurate AI-generated recommendations due to insufficient risk controls and governance.

Module 2: AI Risk Identification and Assessment Frameworks

• Performing comprehensive AI risk assessments
• Identifying technical, ethical, operational, and business risks 
• Applying AI risk scoring and prioritization methods 
• Developing AI risk registers and impact assessments 
• Using risk frameworks such as NIST AI RMF and ISO AI governance principles 
• Case Study: Evaluating bias, transparency, and regulatory risks in an AI-powered lending system.

Module 3: Agentic AI Security and Threat Management

• Understanding AI agent security vulnerabilities 
• Managing prompt injection and adversarial AI attacks 
• Protecting AI agents from unauthorized actions 
• Implementing AI security controls and safeguards 
• Applying AI threat modeling techniques 
• Case Study: Preventing malicious users from manipulating an enterprise chatbot.

Module 4: AI Governance, Ethics, and Compliance

• Designing enterprise AI governance frameworks 
• Implementing responsible AI principles 
• Managing AI transparency and explainability requirements 
• Understanding global AI regulations and compliance expectations 
• Creating AI accountability structures 
• Case Study: Establishing compliance controls for AI systems assisting medical professionals.

Module 5: AI Agent Reliability, Monitoring, and Observability

• Implementing AI agent performance monitoring 
• Tracking AI behavior and decision patterns 
• Managing hallucination and reliability risks 
• Building AI evaluation and testing processes 
• Applying AI observability tools and practices 
• Case Study: Detecting inaccurate product recommendations through continuous AI monitoring.

Module 6: Data Privacy and Information Risk Management

• Managing AI data lifecycle risks 
• Protecting sensitive information used by AI agents 
• Implementing privacy-preserving AI approaches 
• Understanding data governance requirements 
• Preventing unauthorized data exposure 
• Case Study: Improving controls after accidental exposure of confidential customer information.

Module 7: AI Agent Operational Risk and Business Continuity

• Managing risks from autonomous decision-making 
• Establishing AI incident response processes 
• Designing AI fallback and recovery strategies 
• Assessing business impact of AI failures 
• Creating AI operational resilience frameworks 
• Case Study: Managing operational risks after an autonomous planning system generates incorrect forecasts.

Module 8: Building an Enterprise Agentic AI Risk Strategy

• Creating enterprise AI risk management programs 
• Developing AI policies and governance models 
• Implementing continuous improvement processes 
• Measuring AI risk maturity 
• Designing future-ready AI governance strategies 
• Case Study: Building a scalable AI risk framework across multiple departments.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.