Implementation Plan: AI Connectors for Microsoft 365#
Document Control#
| Role | Name | Initials |
|---|---|---|
| Prepared by: AI Agent | Claire | CA |
| Approved by: System Owner | [Pending] | [XX] |
| Approved by: QA | [Pending] | [XX] |
Definitions#
| Term | Definition |
|---|---|
| Artifact | Any document, file, or information produced during the software development process |
| CI/CD | Continuous Integration and Continuous Delivery practices |
| Evidence Package | Collection of documentation, test results, and compliance artifacts for regulatory approval |
| Verification Gate | Approval checkpoint where designated reviewers validate compliance requirements |
| MCP | Model Context Protocol - interface standard for AI connectors |
1. Scope#
[IP-2] Describe scope and scale of IT process activities
This document describes the implementation plan for AI Connectors, a suite of secure connectors enabling AI systems to access Microsoft 365 services (Outlook Mail, SharePoint, Outlook Calendar) using OAuth 2.0 authentication.
Project Classification: SDLC-AI (AI System with Limited Risk per EU AI Act) Risk Level: Standard (GDPR-compliant, limited AI risk classification)
In Scope: - Microsoft 365 connector development (OutlookMail, SharePoint, OutlookCalendar) - OAuth 2.0 authentication implementation - Model Context Protocol (MCP) integration - Privacy controls and data minimization - Compliance artifact generation
Out of Scope: - AI model training or inference (connectors only provide data access) - Microsoft 365 tenant configuration - End-user device management
2. Roles and Responsibilities#
[IP-5] Define roles and responsibilities with required competencies
Core Project Roles#
| Organization | Role | Initials | Responsibilities |
|---|---|---|---|
| BIDMA | System Owner | [XX] | Final decision authority for releases, risk acceptance |
| BIDMA | Data Owner | [XX] | Data governance, GDPR compliance oversight |
| BIDMA | Project Manager | [XX] | Workflow management, stakeholder coordination |
| BIDMA | Engineer/Developer | [XX] | Implementation, code review, testing |
| BIDMA | QA | [XX] | Quality assurance, compliance validation |
Additional Roles#
| Organization | Role | Initials | Responsibilities |
|---|---|---|---|
| BIDMA | Security Lead | [XX] | OAuth security review, token management validation |
| BIDMA | DPO (Data Protection Officer) | [XX] | GDPR compliance verification, privacy impact assessment |
| BIDMA | AI Governance | [XX] | EU AI Act compliance, AI risk classification review |
3. Supported Business Process#
This system supports AI-augmented business processes by enabling secure, privacy-preserving access to Microsoft 365 data:
Primary Workflows#
- Email processing: AI systems retrieve and analyze emails for workflow automation
- Document access: AI systems search and retrieve SharePoint documents
- Calendar integration: AI systems read and create calendar events for scheduling automation
Integration Points#
- AI Platforms: Connectors expose Microsoft 365 data to AI agents via Model Context Protocol (MCP)
- OAuth Provider: Microsoft Entra ID (Azure AD) for authentication and authorization
- Microsoft Graph API: Backend service for accessing M365 resources
Regulatory Context#
- GDPR: Personal data processing (email addresses, meeting attendees, document metadata)
- EU AI Act: Limited risk AI system (no high-risk classification)
4. System Overview#
Architecture#
The AI Connectors system consists of three primary components:
- MCP Server Layer: Exposes Microsoft 365 capabilities to AI clients using standardized protocol
- OAuth Authentication Module: Manages token acquisition, refresh, and secure storage
- Microsoft Graph Client: Interfaces with Microsoft 365 APIs (Mail, SharePoint, Calendar)
Key Functional Components#
| Component | Purpose | Technology |
|---|---|---|
| OutlookMail Connector | Email retrieval and search | Python, Microsoft Graph Mail API |
| SharePoint Connector | Document access and search | Python, Microsoft Graph Sites API |
| OutlookCalendar Connector | Calendar event read/write | Python, Microsoft Graph Calendar API |
| OAuth Handler | Token lifecycle management | OAuth 2.0, MSAL (Microsoft Authentication Library) |
| MCP Server | Protocol adapter for AI clients | Model Context Protocol |
Data Flows#
AI Client → MCP Server → OAuth Handler → Microsoft Graph API → M365 Data
↓ ↓
User Consent Token Storage (encrypted)
User Populations#
- AI Operators: Users who configure and run AI workflows using connectors
- Data Subjects: M365 users whose data is accessed by AI systems (consent-driven)
5. Risk Assessment#
[IP-3, IP-4] Risk-based implementation strategy with references to ITRA and functional risk assessment
Risk Management References#
| Assessment Type | Reference ID | Status |
|---|---|---|
| IT Risk Assessment (ITRA) | [To be completed] | Pending |
| Functional Risk Assessment - OutlookMail | RISK:OutlookMail |
Complete |
| Functional Risk Assessment - OAuthAuthentication | RISK:OAuthAuthentication |
Complete |
| Functional Risk Assessment - SharePoint | RISK:SharePointAccess |
[To be created] |
| Functional Risk Assessment - OutlookCalendar | RISK:OutlookCalendar |
[To be created] |
Risk-Based Strategy#
Key Risks Identified:
- Privacy Risk (High):
- Threat: Unauthorized access to personal emails, calendar events, documents
- Mitigation: OAuth per-user consent, minimum scope principle, encrypted token storage
-
Residual: Low (OAuth enforces per-user authorization)
-
Data Integrity Risk (Medium):
- Threat: AI system modifies or deletes calendar events/emails without user awareness
- Mitigation: Read-only scopes where possible, audit logging, explicit write permissions
-
Residual: Low (scope controls prevent unintended writes)
-
Authentication Risk (High):
- Threat: OAuth token theft enabling persistent unauthorized access
- Mitigation: Token encryption at rest, short-lived access tokens, refresh token rotation
-
Residual: Medium (token storage is encrypted but not hardware-backed)
-
AI Risk - Limited Classification (Low):
- Threat: AI misinterprets data leading to incorrect automation
- Mitigation: Human oversight for critical actions, explainability logs
- Residual: Low (limited risk system per EU AI Act)
Residual Risk Acceptance: All residual risks are accepted by System Owner provided mitigations are implemented and validated.
6. Implementation Methodology#
[IP-6] Decision on implementation methodology
This implementation follows an agile methodology with continuous integration and continuous delivery (CI/CD).
Delivery Workflow#
| Stage | Activities | Verification Gate |
|---|---|---|
| 1. Requirements & Design | BDD feature files (URS), risk assessment, design specification | Design Review |
| 2. Development & Testing | Python implementation, unit tests, security scanning | Code Review |
| 3. Integration Testing | OAuth flow testing, MCP protocol validation, API integration | QA Verification |
| 4. Validation & Release | Compliance verification, evidence consolidation | Release Approval |
| 5. Deployment | Production deployment, smoke tests | Deployment Verification |
| 6. Operations | Monitoring, token renewal, incident management | Operational Review (quarterly) |
7. Verification Gates#
Compliance checkpoints implemented via CI/CD pipeline and approval workflows
Gate Structure#
Gates are enforced through: - CI/CD Pipeline: Automated checks (tests, linters, security scans) must pass before merge - Pull Request Reviews: Minimum 1 peer approval required for code changes - Release Approval Workflow: System Owner + QA approval before production deployment
Gate Definitions#
Gate 1: Design Review (Stage 1)#
- Required Approvers: Technical Lead, Security Lead
- Criteria:
- OAuth architecture validated against security best practices
- Microsoft Graph API scopes justified (minimum necessary principle)
- Data flow diagram approved
- Risk assessments complete for all connectors
Gate 2: Code Review (Stage 2)#
- Required Approvers: Peer Engineer
- Criteria:
- Unit test coverage ≥ 80%
- No high/critical security findings (Bandit, safety checks)
- OAuth token storage uses encryption
- Code follows project style guide (Black, Ruff)
- Commit messages reference URS tags
Gate 3: QA Verification (Stage 3)#
- Required Approvers: QA
- Criteria:
- Integration tests pass (OAuth flow, API calls)
- MCP protocol compliance validated
- No blocking defects
- Traceability verified (URS → Risk → Design → Tests)
Gate 4: Release Approval (Stage 4)#
- Required Approvers: System Owner, QA, DPO (if privacy-impacting)
- Criteria:
- Evidence package complete (URS, risk, design, test results)
- All verification gates passed
- GDPR compliance verified (data minimization, lawful basis)
- AI Act compliance verified (limited risk classification)
- Deployment runbook reviewed
Additional Gates for AI Systems#
| Gate | Stage | Required Approvers | Criteria |
|---|---|---|---|
| AI Risk Review | 1 | AI Governance | AI risk classification confirmed (limited risk), no high-risk features |
| Privacy Impact Review | 1 | DPO | GDPR compliance verified, data retention policy defined |
8. IT Infrastructure#
[IP-1] Qualification of IT infrastructure
Infrastructure Components#
| Component | Provider | Qualification Status |
|---|---|---|
| Kubernetes Cluster | Azure AKS | Qualified (infrastructure IQ complete) |
| PostgreSQL Database | Azure Database for PostgreSQL | Qualified |
| Key Vault (Token Storage) | Azure Key Vault | Qualified (encryption validated) |
| Container Registry | Azure Container Registry | Qualified |
| CI/CD Pipeline | GitHub Actions | Qualified |
Infrastructure as Code#
Infrastructure is managed as code (Terraform/Helm), ensuring: - Consistency: Same config across dev/test/prod - Repeatability: Automated deployments - Audit Trail: Git history of infrastructure changes
9. Environments#
[IP-19] Environment setup and management
| Environment | Purpose | Configuration |
|---|---|---|
| Development | Local development, unit testing | Docker Compose, local token storage |
| Test (PLTE) | Production-like integration testing | Azure AKS, test Microsoft 365 tenant |
| Production | Live system serving end users | Azure AKS, production M365 tenant, encrypted token storage |
Environment Isolation#
- Separate OAuth app registrations per environment (prevent test tokens from accessing prod data)
- Separate Azure Key Vaults (token storage isolation)
- Network isolation (prod cluster has restricted ingress)
10. Configuration Management#
Version control (Git) manages: - Application Code: Python connectors, MCP server, OAuth handlers - Infrastructure as Code: Kubernetes manifests, Terraform configs - Compliance Artifacts: URS (BDD features), risk assessments, design docs - Configuration Items: Environment variables, OAuth scopes, API endpoints
Change Tracking#
[IP-13] Change control and defect management
- Change Control: All changes require GitHub PR with peer review
- Audit Trail: Git commit history with semantic commit messages
- Defect Management: GitHub Issues with labels (
bug,security,compliance) - Traceability: Commits reference URS tags (e.g.,
Closes #42, implements @URS:OutlookMail)
11. External Suppliers and Internal Services#
External Suppliers#
[IP-7 through IP-11] Supplier requirements
| Supplier | Service | QMS Justification | Documentation Process | Data Integrity Checks |
|---|---|---|---|---|
| Microsoft | Microsoft 365 (Graph API, Entra ID) | Tier-1 cloud provider with ISO 27001, SOC 2 Type II certifications | Microsoft official API docs stored in /docs/references/ |
OAuth token validation, API response schema validation |
Supplier Risk Mitigation (IP-11): - Token Expiry: Implement refresh token rotation (not controlled by supplier) - API Rate Limits: Local rate limiting and retry logic - Service Outages: Circuit breaker pattern, graceful degradation
Supplier Documentation Review (IP-9): - Microsoft Graph API documentation reviewed quarterly - Breaking changes tracked via Microsoft 365 changelog - API deprecation notices trigger impact assessment
Internal IT Services#
[IP-12] Internal IT department services
| Service | Provider | Interface |
|---|---|---|
| Identity and Access Management | IT Security | OAuth integration with Entra ID |
| Key Management | IT Security | Azure Key Vault for token encryption |
| Monitoring and Logging | IT Operations | Azure Monitor, Application Insights |
12. Interface Agreements#
[IP-15] Interface agreements for GxP data exchange
Note: This system does NOT exchange GxP-regulated data. All data accessed is business data subject to GDPR, not FDA 21 CFR Part 11 requirements.
| System | Data Exchanged | Agreement Reference |
|---|---|---|
| Microsoft 365 | Email metadata, calendar events, document metadata | Microsoft API Terms of Use |
| AI Platforms (clients) | Connector results via MCP | Internal API agreement (no external transfer) |
13. Quality Factors#
[IP-16, IP-17] Quality built into development
Quality Requirements#
| Factor | Requirement | How Achieved |
|---|---|---|
| Reliability | 99.5% uptime, graceful degradation on API failures | Circuit breaker pattern, health checks, automated failover |
| Maintainability | Code complexity < 10 cyclomatic, test coverage ≥ 80% | Automated linting (Ruff), unit tests, code review |
| Usability | Clear error messages, OAuth consent flow explanation | User-facing error handling, inline help text |
| Security | No plaintext token storage, OAuth scope minimization | Azure Key Vault encryption, scope justification in design docs |
| Privacy | Data minimization, per-user consent | Read-only scopes by default, explicit consent UX |
Quality Assurance Process#
- Code Review: Mandatory peer review, no self-merge
- Automated Testing: Unit tests (pytest), integration tests (OAuth flows)
- Security Scanning: Bandit (static analysis), Safety (dependency vulnerabilities)
- Compliance Checks: Semantic tag validation, traceability verification
14. Verification and Validation#
[IP-18] Development verification results
Verification Activities#
| Activity | Evidence | Retention |
|---|---|---|
| Unit Testing | Pytest results in CI/CD | GitHub Actions logs, retained indefinitely |
| Integration Testing | OAuth flow test results, MCP protocol tests | Test reports in /docs/testing/ |
| Security Scanning | Bandit SAST reports, dependency scans | CI/CD artifacts, reviewed in PRs |
| Traceability Analysis | Agent-generated traceability report | Compliance database |
Validation Evidence#
- Installation Verification (IV): Deployment success in prod, health check pass
- Operational Verification (OV): First successful OAuth flow, API call in production
- Performance Verification (PV): API response time < 2s, token refresh < 500ms
Data Integrity from Development (IP-18): - CI/CD pipeline generates cryptographically signed build artifacts - Commit SHAs link deployed code to Git history (audit trail) - Test results timestamped and immutable in GitHub
15. Documentation Structure#
[IP-14] Interrelationships between documentation
| Document | Location | Relationship |
|---|---|---|
| Intended Use | /docs/requirements/01-intended-use.md |
Defines business need for AI connectors |
| Implementation Plan | /docs/implementation-plan.md |
This document |
| User Requirements (URS) | /requirements/features/*.feature |
BDD feature files with @URS:* tags |
| Risk Assessments | /docs/risks/risk-assessment-*.md |
Per-connector risk analysis with RISK:* tags |
| Design Specifications | /docs/design-*.md |
Technical architecture with DESIGN:* tags |
| Test Evidence | CI/CD artifacts, /docs/testing/ |
Generated per release |
Traceability Chain#
Intended Use → Implementation Plan → URS (@URS:OutlookMail)
↓
Risk Assessment (RISK:OutlookMail)
↓
Design Spec (DESIGN:OutlookMail)
↓
Tests (pytest, integration tests)
16. Organizational Change Management#
[IP-20] Change management and training
Training Requirements#
| Role | Training Required | Delivery Method |
|---|---|---|
| AI Operators | OAuth consent flow, connector configuration | Self-service docs + video walkthrough |
| Developers | MCP protocol, OAuth security best practices | Internal workshop (4 hours) |
| QA | Traceability verification, compliance checks | Agent-assisted training session |
Communication Plan#
- Release Notes: Published in GitHub Releases for each version
- Compliance Updates: Email notification to stakeholders when new artifacts created
- Security Advisories: Immediate notification for critical vulnerabilities
17. Release Approval#
Releases are approved when: - ✅ All verification gates passed (Design, Code Review, QA, Release Approval) - ✅ Evidence package complete (URS, risk, design, test results) - ✅ Required approvals obtained (System Owner, QA, DPO if privacy-impacting) - ✅ No blocking defects or security findings - ✅ Deployment runbook reviewed and validated
Evidence Package Contents#
- Requirements Traceability Matrix: URS → Risk → Design → Tests
- Test Execution Results: Unit tests, integration tests, security scans
- Risk Assessment with Mitigations: All risk scenarios verified
- Change Log: Git commit history since last release
- Approval Signatures: System Owner, QA, DPO (if applicable)
- Deployment Plan: Runbook with rollback procedure
Revision History#
| Version | Date | Author | Changes |
|---|---|---|---|
| 1.0 | 2024-04-16 | Claire (AI Agent) | Initial version covering MITI/MITS IP-1 to IP-21 requirements |