Durga Analytics • AI Governance • 500 Chapters
AI Governance & Responsible AI
500-Chapter Extended Training Course
Enterprise-grade course covering governance frameworks, regulations, risk, LLM governance, MLOps, audits, industry case-studies, templates and capstones — designed for self-paced learners and corporate cohorts.
Delivery: Self-paced & cohort options · Level: Foundation → Expert
Quick Course Snapshot
- • 500 expert-crafted chapters across 20 modules
- • Hands-on labs, templates, playbooks & capstones
- • Industry case studies: Finance, Healthcare, Public Sector, Retail
- • LLM governance, prompt governance, red-teaming and tooling
- • Enterprise-ready templates (Model Cards, AI Policy, AIA, Incident Playbooks)
Price: Self-paced & Pro/Enterprise pricing. Student discounts available.
Why this AI Governance course?
Practical, regulation-aligned and tooling-focused training that translates Responsible AI principles into operational controls, risk processes, and enterprise templates.
Regulation-Ready
Mapped to EU AI Act, GDPR, NIST RMF, ISO/IEC 42001 and major jurisdiction approaches.
Practical & Technical
Model cards, data cards, explainability tooling, monitoring pipelines and incident playbooks.
Hands-on Labs
Red-teaming LLMs, bias testing, AIA templates and end-to-end governance simulations.
Enterprise Value
Templates and playbooks to operationalize governance at scale across product and platform teams.
Curriculum — 20 Modules, 500 Chapters
Modules are collapsed for readability — expand any module to view chapter lists. Each chapter is expandable into a full extended-manual with objectives, labs and templates.
MODULE 1: Foundations of AI Governance (Ch 1–25)
1. What Is AI Governance
2. Why AI Governance Matters in 2025+
3. Enterprise Risks from AI
4. Key Governance Concepts
5. Responsible AI Core Principles
6. The AI Governance Lifecycle
7. Stakeholder Roles & Accountability
8. AI Strategy vs AI Governance
9. Ethics vs Compliance vs Regulation
10. The 7 Pillars of Responsible AI (Durga Model)
11. AI Decision Rights & RACI
12. Common AI Failure Modes
13. Corporate AI Incidents Case Studies
14. Consequences of Poor Governance
15. AI Governance Maturity Models
16. Building an AI Governance Office (AIGO)
17. AI Governance for Startups vs Enterprises
18. Challenges in Scaling RAI
19. AI Governance in Multinational Corporations
20. Cross-border Risk & Data Residency
21. AI Governance ROI & Business Value
22. Organizational Cultural Transformation
23. Executive Communication & Adoption
24. AI Governance KPIs & OKRs
25. End-of-Module Simulation
MODULE 2: GLOBAL REGULATIONS (Ch 26–50)
26. Overview of Global AI Regulations
27. EU AI Act — Deep Dive
28. EU AI Act — High Risk Requirements
29. EU AI Act — Provider vs Deployers
30. EU AI Act — Documentation Pack
31. EU AI Act — CE Marking
32. GDPR for AI
33. Privacy-by-Design for AI
34. US AI Executive Order
35. NIST AI Risk Management Framework
36. ISO/IEC 42001 AI Management System
37. Singapore Model AI Governance Framework
38. UK AI Regulation Approach
39. Canada AIDA
40. India’s AI Regulatory Direction
41. China AI Regulation & Algorithmic Filing
42. Brazil/LatAm AI Standards
43. AI Safety Institutes (US/UK)
44. Cross-jurisdiction Conflicts
45. Responsible AI in Financial Services
46. Healthcare AI Regulation
47. Employment & HR Tech Regulation
48. Consumer Protection Laws
49. Compliance Mapping Templates
50. End-of-Module Lab: Regulatory Assessment
MODULE 3: AI RISK MANAGEMENT (Ch 51–75)
51. What Is AI Risk
52. Risk Categories in AI Systems
53. Model Risk vs AI Risk
54. Data Risks
55. Privacy Risks
56. Bias & Fairness Risks
57. Explainability Risks
58. Drift & Performance Risks
59. Cybersecurity Risks
60. Third-party AI Risk
61. Prompt Injection & LLM-Specific Risks
62. Jailbreak Detection
63. Hallucination Risk Framework
64. Red-Teaming Overview
65. Harm Taxonomy
66. Impact Assessment Method
67. Residual Risk Calculation
68. AI Risk Register
69. Risk Heatmaps
70. Risk Appetite Statements
71. Severity & Likelihood Scoring
72. Business Impact Analysis
73. Mitigation Planning
74. Residual Risk Approval
75. Risk Reporting Dashboard
MODULE 4: AI ETHICS (Ch 76–100)
76. Foundations of AI Ethics
77. Fairness & Equity
78. Accountability Models
79. Transparency
80. Explainability
81. Contestability & Redress
82. Autonomy & Human Oversight
83. Ethical Decision Frameworks
84. Case Study: COMPAS
85. Case Study: Amazon Hiring AI
86. Case Study: Healthcare Bias
87. Aligning AI Behaviors
88. Value Alignment Techniques
89. Ethical Data Collection
90. Ethics for LLMs
91. Ethics in Reinforcement Learning
92. Ethics in Autonomous Systems
93. Ethics for Biometrics
94. Ethics for Generative AI
95. Ethical Review Committees
96. Ethical Risk Escalation Paths
97. Designing Ethical AI Products
98. Ethical Research Practices
99. Ethical AI Scorecard
100. End-of-Module Reflection Exercise
MODULE 5: DATA GOVERNANCE FOR AI (Ch 101–125)
101. Data Foundations for AI
102. Data Quality for AI
103. Data Lineage for AI Models
104. Data Provenance
105. Metadata for AI
106. Feature Store Governance
107. Synthetic Data Governance
108. PII Handling Controls
109. Consent Management
110. Data Minimization
111. Data Preparation Risks
112. LLM Data Filtering Strategies
113. Training Data Documentation
114. Data Sensitivity Classification
115. Data Access Controls
116. Role-based Access (RBAC)
117. Attribute-based Access (ABAC)
118. Data Encryption for AI
119. Data Retention Policies
120. Data Deletion & Model Forgetting
121. Data Sharing Governance
122. Vendor Data Accountability
123. Dataset Risk Scorecard
124. Data Governance Metrics
125. End-of-Module Lab
MODULE 6: MODEL LIFECYCLE GOVERNANCE (Ch 126–150)
126. MLOps vs AIOps vs GovOps
127. Model Lifecycle Overview
128. Model Development Standards
129. Model Documentation (Model Cards)
130. Data Cards
131. Risk Cards
132. Explainability Cards
133. Version Control
134. Feature Documentation
135. Model Validation Procedures
136. Pre-deployment Testing
137. Stress Testing
138. Scenario Simulation
139. Model Approval Workflows
140. Human-in-the-loop Design
141. Deployment Controls
142. Monitoring Setup
143. Performance Evaluation Metrics
144. Drift Monitoring
145. Trigger Conditions
146. Audit Trail Requirements
147. Change Management
148. Incident Monitoring
149. Model Decommissioning
150. Hands-on lab: Deployment governance
MODULE 7: LLM GOVERNANCE (Ch 151–175)
151. LLM Development Lifecycle
152. LLM Fine-tuning Governance
153. LLM Prompt Governance
154. Prompt Injection Controls
155. Guardrail Design
156. Content Safety Frameworks
157. LLM Red-Teaming
158. Hallucination Reduction
159. Retrieval-Augmented Generation Governance
160. Memory Governance
161. LLM Output Filtering
162. Safety Thresholds
163. LLM Alignment Techniques
164. RLHF Governance
165. Agentic AI Governance
166. Autonomous AI Risk
167. LLM Ethics
168. Open-source LLM Governance
169. Closed-source LLM Governance
170. Multimodal AI Governance
171. Voice AI Governance
172. Image/Video AI Governance
173. Deepfake Risk Controls
174. LLM Evaluation Scorecards
175. End-of-Module Lab
MODULE 8: AI SAFETY ENGINEERING (Ch 176–200)
176. Safety Engineering Concepts
177. Failure Mode Analysis
178. Test Coverage for AI
179. Adversarial Testing
180. Jailbreak Testing Protocols
181. Red Team Operations
182. Secure AI Architectures
183. AI Sandbox Environment
184. Isolation Layers
185. Endpoint Protection for Models
186. Safety Guardrails
187. Input Validation
188. Output Validation
189. API Threat Detection
190. LLM Safety Benchmarks
191. Scenario-based Safety Design
192. Penetration Testing for AI Systems
193. AI Incident Readiness
194. Kill-Switch Design
195. Safe-AI Runbooks
196. Safety Audits
197. Safety Controls Implementation
198. Safety Metrics
199. Safety Certification Paths
200. Safety Posture Dashboard
MODULE 9: AI SECURITY (Ch 201–225)
201. AI Security Overview
202. Threat Modeling for AI
203. LLM Threat Taxonomy
204. Secure MLOps Architecture
205. Zero Trust for AI
206. API Security Controls
207. Model Theft Protection
208. Data Poisoning Detection
209. Backdoor Attacks
210. Membership Inference Attacks
211. Differential Privacy
212. Homomorphic Encryption
213. Federated Learning Security
214. Secure Training Pipelines
215. Security Logging
216. Anomaly Detection
217. Secure Deployment
218. SOC Integration
219. AI Security Reporting
220. Secure Access Management
221. Vendor AI Security Assessment
222. SaaS AI Security Controls
223. Cloud Security for AI
224. Security Metrics
225. End-of-Module Lab
MODULE 10: FAIRNESS & BIAS (Ch 226–250)
226. Bias in AI — Overview
227. Types of Bias
228. Bias Measurement Techniques
229. Bias Mitigation Models
230. Fairness Metrics
231. Fairness Constraints
232. Fairness in LLMs
233. Societal Bias Propagation
234. Bias in Text Models
235. Bias in CV Models
236. Bias in Speech Models
237. Sensitive Attributes
238. Proxy Detection
239. Ethical Dataset Design
240. Fairness Testing Framework
241. Governance of Bias Ops
242. Human Review for Fairness
243. Fairness Documentation
244. Fairness Audit Checklist
245. Bias Remediation Plans
246. Fairness Hate-speech Case Studies
247. Bias Monitoring in Production
248. Fairness KPIs
249. Enterprise Fairness Policy
250. Hands-on: Bias Testing
MODULE 11: Explainability & Transparency (Ch 251–275)
251. Explainability & Transparency — Overview
252. Why Explainability Matters for AI Governance
253. Regulatory Requirements for Explainability (EU AI Act, GDPR, US EO)
254. Global vs Local Explainability
255. Intrinsic vs Post-hoc Explainability
256. Explainability for LLMs & Generative AI
257. Explainability in High-Risk Sectors (Finance, Health, HR)
258. Model Interpretability Techniques — Overview
259. SHAP Values — Governance Use Cases
260. LIME — Strengths, Weaknesses & Controls
261. Counterfactual Explanations
262. Anchors & Rule-Based Explanations
263. Feature Importance Governance
264. Partial Dependence & ICE Plots
265. Explainability for Deep Learning Models
266. Explainability for Computer Vision Models
267. Explainability for NLP & LLMs
268. Transparency Requirements for AI Deployments
269. Documentation Standards (Model Cards, System Cards)
270. Human-Centric Explanation Design
271. Risk of Misinterpretation & Cognitive Bias
272. Transparency in AI Decision Pipelines
273. Explainability Monitoring in Production
274. Explainability Audit Checklist
275. Capstone Lab: Explainability Assessment & Report
MODULE 12: Human Oversight & Accountability (Ch 276–300)
276. Human Oversight — Overview
277. Why Humans Must Stay in the Loop
278. Human-in-the-Loop (HITL) vs Human-on-the-Loop (HOTL)
279. Human-out-of-the-Loop (HOOTL) Risks
280. Oversight Requirements under EU AI Act
281. Roles & Responsibilities for AI Oversight
282. Defining Accountability in AI Systems
283. Designing Escalation Paths for AI Decisions
284. Human Override Mechanisms
285. Kill Switch & Emergency Shutdown Controls
286. Reviewer Competency & Training Requirements
287. Avoiding Automation Bias
288. Human Judgment vs Model Recommendations
289. Oversight in High-Risk Use Cases
290. Oversight for LLMs & Autonomous Agents
291. Documentation of Human Decisions
292. Accountability Matrices (RACI) for AI
293. Governance of Human Review Workflows
294. Psychological & Cognitive Constraints of Reviewers
295. Dual-Control Oversight Models
296. Oversight Metrics & KPIs
297. Ensuring Human Control in Automated Pipelines
298. Auditing Human Oversight Effectiveness
299. Oversight Failure Case Studies
300. Capstone Lab: Designing a Human Oversight Framework
MODULE 13: AI Policy, Standards & Governance Architecture (Ch 301–325)
301. Introduction to AI Policies
302. Enterprise AI Policy Frameworks
303. Components of an AI Governance Policy
304. AI Principles to Policy Translation
305. Drafting AI Acceptable Use Policies
306. AI Development Standards
307. AI Deployment Standards
308. AI Monitoring & Reporting Standards
309. ISO/IEC 42001 — AI Management System
310. NIST AI Risk Management Framework
311. OECD AI Principles
312. Singapore Model AI Governance Framework
313. Mapping Policies to Regulations
314. Internal AI Policy Approval Processes
315. Building an AI Governance Charter
316. Governance Operating Model (GOM)
317. Centralized vs Federated Governance Models
318. AI Control Library (ACL) Design
319. AI Decision Rights & Accountability
320. Policy Implementation Roadmaps
321. Organizational Change Management for AI Policies
322. Policy Compliance Monitoring
323. Policy Exception Handling Procedures
324. Governance Architecture Maturity Levels
325. Capstone Lab: Drafting an Enterprise AI Policy
MODULE 14: AI Program Management & Organizational Design (Ch 326–350)
326. Introduction to AI Program Management
327. AI Program vs AI Projects — Key Differences
328. Enterprise AI Operating Model
329. Organizational Structures for AI Teams
330. Roles in an AI Governance Program
331. Building an AI Center of Excellence (AI CoE)
332. AI Governance Office (AIGO) Setup
333. Skills & Competency Framework for AI Teams
334. Workforce Upskilling Strategy
335. Vendor vs Internal Capability Decisions
336. AI Program Roadmapping
337. Annual & Quarterly AI Planning Cycles
338. Prioritization Frameworks for AI Initiatives
339. Portfolio Governance for AI
340. Budgeting & Funding Models for AI Programs
341. Stakeholder Alignment & Communications
342. Cross-Functional Collaboration Mechanisms
343. AI Change Management
344. AI Adoption Strategy & Barriers
345. Building a Culture of Responsible AI
346. Project Intake & Approval Workflows
347. AI Delivery Lifecycle Governance
348. Performance Measurement & OKRs
349. Maturity Model for AI Program Management
350. Capstone Lab: AI Operating Model Design
MODULE 15: Procurement & Third-Party AI Governance (Ch 351–375)
351. Introduction to Third-Party AI Governance
352. Why Vendor Governance Matters for AI Risk
353. Third-Party AI Risk Categories
354. Supplier Classification for AI Systems
355. AI Procurement Policies & Templates
356. Pre-Procurement Risk Assessment
357. AI Vendor Due Diligence Checklist
358. Evaluating AI Vendor Capabilities
359. Evaluating Model Transparency & Documentation
360. Evaluating Data Handling & Privacy Controls
361. Evaluating Model Risk Management Practices
362. Evaluating LLM Safety & Guardrail Controls
363. Cloud & SaaS AI Provider Assessment
364. Open-Source AI Risk Evaluation
365. Licensing Models for AI Systems
366. Contractual Clauses for AI Governance
367. Data Protection Addendums for AI Vendors
368. Service Level Agreements (SLAs) for AI Systems
369. AI Incident Reporting Requirements for Vendors
370. Continuous Monitoring of Vendor AI Performance
371. Vendor Security Assessment for AI Systems
372. Sub-processor & Supply Chain Risk Management
373. Vendor Offboarding & AI Asset Recovery
374. Third-Party AI Governance Maturity Model
375. Capstone Lab: Third-Party AI Risk Assessment
MODULE 16: Operationalizing Responsible AI (Ch 376–400)
376. Introduction to Operationalizing Responsible AI
377. Translating Principles into Actionable Controls
378. Building an RAI Operating Framework
379. Embedding RAI in Product Development
380. RAI Requirements Gathering Templates
381. Integrating RAI into MLOps Pipelines
382. Embedding RAI in LLMOps & GenAI Workflows
383. RAI Control Library Design
384. Mapping Controls to Risks
385. Embedding RAI in Data Pipelines
386. Ethical Review Gates & Approvals
387. Pre-Deployment RAI Assessments
388. Responsible AI Testing Framework
389. Safety, Fairness & Bias Testing Integration
390. RAI Metrics & Performance Indicators
391. Monitoring for RAI Drift
392. Incident Detection & RAI Alerts
393. Responsible AI Response & Escalation
394. Re-training & Model Remediation Governance
395. Post-Deployment RAI Audits
396. Embedding RAI in Business Processes
397. RAI Training & Awareness Programs
398. RAI Adoption Barriers & Change Management
399. Enterprise RAI Dashboard & Reporting
400. Capstone Lab: End-to-End RAI Operationalization
MODULE 17: AI Internal Audit & Assurance (Ch 401–425)
401. Introduction to AI Internal Audit
402. Role of Internal Audit in AI Governance
403. Audit Requirements for High-Risk AI Systems
404. AI Audit Frameworks & Standards
405. Designing an AI Audit Charter
406. Audit Planning for AI Programs
407. Control Testing for AI Systems
408. Reviewing AI Governance Policies
409. Auditing Data Governance for AI
410. Auditing Model Development Practices
411. Auditing Documentation (Model Cards, Data Cards)
412. Auditing Explainability & Transparency Controls
413. Auditing Fairness & Bias Mitigation
414. Auditing LLM & Generative AI Systems
415. Auditing AI Security Controls
416. Auditing Third-Party AI Vendors
417. Auditing AI Monitoring & Drift Management
418. Reviewing AI Incident Logs
419. AI Risk Scoring & Materiality Assessment
420. Evidence Collection & Sampling for AI Audits
421. Common Findings in AI Audits
422. Reporting AI Audit Results
423. Remediation & Follow-Up Governance
424. Building AI Audit Maturity
425. Capstone Lab: Conducting an AI Internal Audit
MODULE 18: Industry-Specific AI Governance (Ch 426–450)
426. Industry-Specific Governance — Overview
427. Financial Services — AI Risk Requirements
428. Banking — Model Risk Management (MRM) Alignment
429. Insurance — Underwriting & Claims AI Governance
430. Capital Markets — Algorithmic Trading Controls
431. Payments & Fraud — AI Monitoring & Security
432. Healthcare — Clinical AI Safety & Compliance
433. Pharmaceuticals — AI in Drug Discovery Governance
434. Biotech — GenAI & Biosecurity Controls
435. Retail — Personalization AI & Consumer Protection
436. E-commerce — Recommendation AI Governance
437. Telecommunications — Network Optimization AI Controls
438. Manufacturing — Predictive Maintenance AI Governance
439. Automotive — Autonomous Systems Safety Standards
440. Energy — Grid Optimization AI Controls
441. Oil & Gas — Operational AI Risk Management
442. Public Sector — Algorithmic Accountability Rules
443. Law Enforcement — Facial Recognition Governance
444. Education — AI-Driven Learning Systems Governance
445. HR — Hiring & Promotion AI Controls
446. Marketing — Generative AI Compliance Requirements
447. Media — Deepfake & Synthetic Content Governance
448. Cybersecurity — AI for Threat Detection Governance
449. Cross-Industry Governance Patterns
450. Capstone Lab: Industry AI Risk Assessment
MODULE 19: AI Governance Tools, Platforms & Automation (Ch 451–475)
451. Introduction to AI Governance Tooling
452. Categories of AI Governance Platforms
453. Model Risk Management (MRM) Platforms
454. Data Governance Platforms for AI
455. Responsible AI Platforms — Overview
456. LLM Monitoring & Safety Tools
457. Fairness & Bias Detection Tools
458. Explainability Tools (XAI Platforms)
459. AI Security Tools & Threat Detection
460. ML Observability Platforms
461. Continuous Compliance Automation
462. Documentation Automation (Model Cards etc.)
463. Incident Management Automation
464. Red-Team Automation Tools
465. Vendor Risk Automation Platforms
466. Prompt Governance Tools for LLMs
467. RAG Evaluation & Safety Tools
468. Synthetic Data Generation Governance Tools
469. Feature Store Governance Automation
470. AI Sandbox & Testing Environments
471. Enterprise Integration Patterns for AI Governance Tools
472. Build vs Buy Decisions for AI Governance
473. Tooling Maturity Model
474. Evaluating AI Governance Tools (RFP Checklist)
475. Capstone Lab: AI Governance Tooling Blueprint
MODULE 20: Capstones, Templates, Case Studies & Enterprise Implementation (Ch 476–500)
476. Introduction to Enterprise AI Governance Implementation
477. End-to-End Governance Lifecycle Walkthrough
478. Full AI Risk Assessment Capstone
479. Full Responsible AI (RAI) Controls Capstone
480. Full Data Governance for AI Capstone
481. LLM Governance End-to-End Capstone
482. Algorithmic Impact Assessment (AIA) Template Build
483. AI Governance Policy Template Creation
484. Model Card Enterprise Template
485. Data Card Enterprise Template
486. Risk Card Enterprise Template
487. Explainability Report Template
488. Fairness & Bias Review Template
489. Incident Response Playbook Template
490. AI Audit Checklist Template
491. Vendor AI Evaluation Template
492. RAG Safety Evaluation Template
493. Prompt Engineering Governance Template
494. Enterprise AI Governance Dashboard
495. AI Governance Maturity Assessment Toolkit
496. Governance Architecture Design Workshop
497. Executive AI Governance Playbook
498. Enterprise AI Governance Launch Plan
499. Final Comprehensive Project: Build a Full AI Governance System
500. Graduation & Certification Pathways
Hands-on Projects & Capstones
Capstone: Enterprise AI Governance System
Design and implement an end-to-end governance system: policy → AIA → controls → monitoring → audit.
Lab: LLM Red-Teaming
Run red-team exercises, evaluate hallucination and jailbreak risks, and produce remediation plans.
Project: Model Card & AIA Templates
Create enterprise-ready templates for model cards, data cards, risk cards and explainability reports.
Pricing & Plans
Self-Paced
On-demand access
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500-chapter course, video lessons, PDFs and community access.
Pro — Full Enterprise Pack
Complete governance program
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Includes instructor-led sessions, templates, and corporate licensing.
Instructors & Credibility
Course Authors
Practitioners with strong skills in AI governance frameworks, regulatory compliance, responsible ML design, LLM risk controls, MLOps governance, audits, and enterprise policy implementation.
Includes: Labs, runbooks, realistic datasets, and enterprise playbooks.
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Enroll or request a cohort. We'll provide access to curriculum, lab datasets and project briefs.
Email: contact@durgaanalytics.com • For enterprise: contact@durgaanalytics.com