AI Safety Engineering Training Course

AI Safety Engineering Training Course

Introduction

AI Safety Engineering Training Course is designed to equip professionals with advanced knowledge and practical skills to develop, deploy, and manage safe, reliable, and trustworthy artificial intelligence systems. As organizations rapidly adopt Generative AI, Large Language Models (LLMs), Autonomous AI Agents, Machine Learning Systems, and AI-powered decision platforms, the demand for AI safety engineering, AI risk mitigation, model robustness, alignment strategies, adversarial testing, and responsible AI development continues to grow. This course focuses on engineering approaches that ensure AI systems operate securely, transparently, and ethically while reducing risks associated with unintended behaviors, model failures, vulnerabilities, and harmful outcomes.

Through a combination of technical frameworks, safety-by-design principles, evaluation methodologies, and real-world industry case studies, participants will learn how to implement comprehensive AI safety practices across the AI lifecycle. The program explores AI alignment, robustness engineering, red teaming, model evaluation, human oversight, safety governance, secure AI deployment, and continuous monitoring, enabling organizations to build AI systems that are resilient, accountable, and aligned with human values. Participants will gain practical capabilities to design safer AI solutions for sectors including healthcare, finance, government, cybersecurity, education, and enterprise technology.

Course Duration

5 days

Course Objectives

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

1. Understand the foundations of AI Safety Engineering, trustworthy AI, and responsible AI development. 
2. Apply AI risk assessment frameworks to identify and mitigate safety challenges. 
3. Implement AI alignment strategies for human-centered and value-aligned systems. 
4. Design robust AI models using machine learning safety and reliability engineering principles. 
5. Conduct adversarial testing, AI red teaming, and vulnerability assessments. 
6. Apply AI security engineering practices to protect models, data, and applications. 
7. Develop effective AI evaluation and benchmarking methodologies. 
8. Implement human-in-the-loop and human oversight mechanisms. 
9. Understand AI governance frameworks, safety standards, and regulatory requirements. 
10. Manage risks associated with Generative AI and Large Language Models (LLMs). 
11. Build continuous AI monitoring, auditing, and incident response processes. 
12. Apply ethical AI engineering practices throughout the AI lifecycle. 
13. Develop organizational strategies for AI safety culture and operational excellence. 

Target Audience

1. AI Engineers and Machine Learning Engineers 
2. Data Scientists and AI Researchers 
3. Software Developers Building AI Applications 
4. Cybersecurity Professionals and AI Security Specialists 
5. AI Product Managers and Technology Leaders 
6. Risk, Compliance, and Governance Professionals 
7. Government Technology and Digital Transformation Teams 
8. Enterprise Architects and Innovation Managers 

Course Modules

Module 1: Foundations of AI Safety Engineering

• Introduction to AI safety principles, challenges, and emerging risks
• Understanding the AI lifecycle and safety-by-design approaches 
• Exploring trustworthy AI, responsible innovation, and reliability engineering 
• Overview of global AI safety frameworks and standards 
• Case Study: AI safety challenges in deploying autonomous decision systems 

Module 2: AI Risk Assessment and Threat Modeling

• Identifying technical, operational, and societal AI risks 
• Developing AI threat models and risk assessment methodologies 
• Understanding failure modes in machine learning systems 
• Applying risk mitigation strategies across AI pipelines 
• Case Study: Risk assessment for AI-powered financial fraud detection systems 

Module 3: AI Alignment and Value Alignment Engineering

• Understanding AI alignment challenges and objectives 
• Designing AI systems aligned with human goals and policies 
• Exploring reinforcement learning from human feedback (RLHF) 
• Managing unintended AI behaviors and goal misalignment 
• Case Study: Improving safety alignment in conversational AI assistants 

Module 4: Robustness Engineering for AI Systems

• Building resilient machine learning models 
• Understanding model reliability and performance stability 
• Managing data distribution shifts and model uncertainty 
• Implementing robustness testing techniques 
• Case Study: Enhancing robustness of AI diagnostic systems in healthcare 

Module 5: Adversarial AI Testing and Red Teaming

• Understanding adversarial attacks against AI models 
• Conducting AI penetration testing and safety evaluations 
• Identifying prompt injection and jailbreak vulnerabilities 
• Developing AI red team strategies 
• Case Study: Red teaming a Generative AI customer service chatbot 

Module 6: Secure AI Development and Deployment

• Applying secure software engineering for AI systems 
• Protecting AI models, APIs, and training data 
• Managing AI supply chain and third-party model risks 
• Implementing secure AI deployment pipelines 
• Case Study: Securing enterprise AI platforms against model attacks 

Module 7: AI Evaluation, Monitoring, and Incident Management

• Designing AI safety evaluation frameworks 
• Measuring reliability, fairness, and system performance 
• Implementing continuous AI monitoring solutions 
• Managing AI incidents and response strategies 
• Case Study: Monitoring an AI-powered healthcare recommendation platform 

Module 8: AI Governance, Ethics, and Future Safety Challenges

• Understanding AI governance and regulatory landscapes 
• Developing organizational AI safety policies 
• Integrating ethics into AI engineering workflows 
• Preparing for emerging autonomous AI technologies 
• Case Study: Creating an enterprise AI safety governance framework 

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.