Home/Learn/Courses/Architecting Trust: AI Safety, Ethics & Compliance
Back to Courses
AI EthicsComplianceAI Safety

Architecting Trust: AI Safety, Ethics & Compliance

Building Robust and Regulated AI Systems for the Modern Enterprise

This program provides a 360-degree view of the AI lifecycle through the lens of safety and ethics. Participants will move beyond 'AI hype' to master the frameworks, tools, and legal requirements necessary to deploy AI that is not only powerful but also predictable and compliant.

Core Learning Outcomes

Identify and mitigate algorithmic bias using state-of-the-art technical remediation.
Implement Explainable AI (XAI) libraries (SHAP, LIME) into production pipelines.
Navigate complex regulatory landscapes like the EU AI Act and NIST AI RMF.
Protect AI systems against adversarial attacks like prompt injection and data poisoning.
Establish cross-functional AI oversight boards and incident response protocols.
Quantify the 'Ethical ROI' and treat trust as a competitive advantage.

Curriculum Overview

Practical Simulations & Labs

The Bias Audit Lab

Clean a 'toxic' dataset to meet fairness metrics using Fairlearn.

Python + Fairlearn + AIF360

The Policy Workshop

Draft a 'Company AI Ethics Charter' for Healthcare or Finance.

Framework: NIST AI RMF

Adversarial Simulation

Attempt to 'break' a model in a sandboxed environment to find injection flaws.

Red Teaming Toolkit

Learning Path by Role

Engineers

Code bias-detection scripts and implement XAI libraries into the CI/CD pipeline.

Managers

Frameworks to assess the 'Ethics-to-Revenue' ratio and lead safe AI transitions.

Compliance

Deep understanding of the EU AI Act and NIST to ensure company passes external audits.

Frequently Asked Questions

Student Reviews

Rishabh Mehra

Machine Learning Engineer

"This course finally brought clarity to AI safety beyond theory. The bias mitigation labs using Fairlearn and AIF360 were extremely practical and directly applicable to my production workflows."

Ananya Iyer

AI Product Manager

"I loved how the course connects ethics to business outcomes. The concept of ‘Ethical ROI’ completely changed how I think about trust as a competitive advantage."

Karthik Reddy

Data Scientist

"The Explainable AI module was excellent. Implementing SHAP and LIME in real scenarios helped me explain model decisions clearly to both stakeholders and auditors."

Neha Kapoor

Compliance & Risk Analyst

"This is one of the few courses that explains the EU AI Act and NIST AI RMF in a way that is actually usable. I feel confident participating in AI audits now."

Arjun Malhotra

AI Security Researcher

"The adversarial simulation lab was a highlight. Prompt injection and data poisoning attacks were demonstrated realistically, which helped me understand real-world risks much better."

Pooja Choudhary

Healthcare AI Consultant

"The Policy Workshop was extremely valuable. Drafting an AI Ethics Charter helped me understand governance from an organizational perspective, not just a technical one."

Sandeep Kulkarni

Engineering Manager

"This course bridges the gap between engineering and leadership. The guidance on AI oversight boards and incident response protocols is exactly what enterprises need."

Aditi Sharma

AI Governance Specialist

"The structured roadmap for operationalizing ethics was excellent. It’s rare to find a course that moves beyond awareness and actually shows how to implement ethical AI."

Rohit Verma

Startup Founder – AI SaaS

"As a founder, this course helped me understand how compliance and safety can strengthen market trust. The lessons on treating ethics as a growth strategy were eye-opening."

Vikram Singh

Cybersecurity Architect

"The integration of agentic AI risks and adversarial ML taxonomy was very well done. This course is a must for anyone deploying AI in high-risk or regulated environments."

Mastery Assessment: AI Safety, Ethics & Compliance

Validate your expertise in AI governance, adversarial machine learning, and regulatory frameworks.

Architecting Trust Mastery

50 comprehensive questions covering fundamentals, risk management, and implementation of ethical AI systems.

Fundamentals of Agentic AI in Cybersecurity
The Three Pillars and Design Patterns
Adversarial Machine Learning (AML) Taxonomy
Generative AI and Agentic Risks
Governance, Regulations, and Standards
Implementation, ROI, and Future Trends
21999
Professional Excellence Tier
Professional Certification
Bias Remediation Toolkit
Regulatory Compliance Maps
Incident Response Playbooks
Instructor

Celoris Designs

Celoris

Celoris Designs

Excellence in AI Safety

We specialize in operationalizing AI safety for the modern enterprise. From technical bias mitigation to boardroom governance, we bridge the trust gap.

4.95(1240+)
6-8 Weeks (Self-paced)

Prerequisites

  • Basic understanding of Machine Learning concepts
  • Familiarity with Python (for technical modules)
  • Interest in AI Governance and Corporate Compliance
  • No advanced Math PhD required
Sponsored Content