Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103)
Security Governance and Responsible AI
Apply security, privacy, compliance, and responsible AI controls to exam scenarios.
Official Scope and Verification
This lesson is mapped to the verified Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) outline. Official sources and public status were rechecked on 2026-07-13. Provider pages remain authoritative for late-breaking blueprint, availability, scheduling, price, language, delivery, and retake changes.
Azure AI Apps and Agents Developer Associate beta-era exam path. Microsoft Learn says practice assessments are usually available within 8 weeks after an exam is out of beta and generally available.
Official Objectives Emphasized Here
| Domain or objective area | Published weight | Key objective groups | Official source |
|---|---|---|---|
| Plan and manage an Azure AI solution | 25-30% | Choose the appropriate Foundry services for generative AI and agents; Set up AI solutions in Foundry; Manage, monitor, and secure AI systems; Implement responsible AI across generative AI and agentic systems | Microsoft Learn official AI-103 study guide as of April 16, 2026 |
| Implement generative AI and agentic solutions | 30-35% | Build generative applications by using Foundry; Build agents by using Foundry; Optimize and operationalize generative AI systems | Microsoft Learn official AI-103 study guide as of April 16, 2026 |
| Implement computer vision solutions | 10-15% | Design and implement image- and video-generation solutions; Design and implement multimodal understanding workflows; Implement responsible AI for multimodal content | Microsoft Learn official AI-103 study guide as of April 16, 2026 |
| Implement text analysis solutions | 10-15% | Apply language model text analysis; Implement speech solutions | Microsoft Learn official AI-103 study guide as of April 16, 2026 |
Authoritative Sources for This Scope
- Microsoft Learn official AI-103 study guide as of April 16, 2026 - Official source; accessed 2026-07-13.
Security, governance, and responsible AI questions ask whether the solution can be trusted, controlled, and explained. For Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103), treat governance as part of the design, not a separate cleanup task after the model works.
Controls To Recognize
| Control area | What it protects | What to look for in a scenario |
|---|---|---|
| Identity and access | Systems, documents, tools, models, and administrative actions. | Least privilege, role-based access, service identities, approval boundaries, and separation of duties. |
| Data protection | Training data, prompts, uploaded files, retrieved documents, logs, and outputs. | Classification, encryption, masking, retention, residency, and deletion requirements. |
| Output quality and safety | Users, customers, business decisions, and public trust. | Grounding, citations, evaluations, content filters, policy checks, and human review. |
| Responsible AI | Fairness, transparency, accountability, and social impact. | Bias testing, explainability, consent, documentation, stakeholder review, and appeal paths. |
| Auditability | Evidence that the system was governed and operated responsibly. | Logs, versioning, approvals, risk registers, control tests, and incident records. |
Provider-Specific Risk Lens
Use Microsoft Entra ID, role-based access, data loss prevention, Purview, logging, content filters, and human review for high-impact decisions.
For Microsoft, a governance answer is strongest when it matches the provider's identity model, logging approach, data controls, and official responsible AI guidance instead of describing safety in general terms only.
Track-Specific Risk Checks
- privacy leakage through prompts, files, logs, retrieved documents, or generated outputs
- hallucinated or ungrounded answers used without review
- unclear accountability when an AI recommendation affects people, money, security, or compliance
- unsafe tool execution
- prompt injection through retrieved content
- missing human approval for high-impact actions
Responsible AI Scenario Checklist
- Purpose: Is the use case appropriate, useful, and clearly bounded?
- People: Who is affected, who can challenge the output, and who owns the decision?
- Data: Was the data collected, used, stored, and shared appropriately?
- Model behavior: Are hallucination, bias, toxicity, privacy leakage, and misuse tested?
- Operations: Are monitoring, incident response, change control, and retirement plans defined?
Example: Prompt Injection And Data Leakage
Scenario: an AI assistant can read internal knowledge articles and call workflow tools. A user tries to make it ignore its instructions and reveal restricted information. The best answer is not just 'write a better prompt.' It should combine access control, tool permission limits, input and output filtering, retrieval permissions, logging, testing, and human escalation for sensitive actions.
How To Study Governance
- Write one governance control for each lifecycle stage: design, data, build, test, deploy, monitor, and retire.
- Practice rejecting answers that rely on user trust, prompt wording, or policy documents without enforcement.
- Use NIST AI RMF and OWASP GenAI security resources as general reference points, then map them back to the provider-specific credential objectives.
Useful Links
- Microsoft Credentials - Official catalog for Microsoft Certifications and Applied Skills.
- Microsoft Certification Renewal - Renewal rules for eligible Microsoft certifications.
- NIST AI Risk Management Framework - General reference for AI risk management practices.
- OWASP GenAI Security Project - General reference for LLM and GenAI application risks.