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Yaojia Wang 014d62bcc2 Project Init

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2025-11-02 23:55:18 +01:00

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ColaFlow Risk Assessment Report

Version: 1.0 Date: 2025-11-02 Assessment Period: Full project lifecycle (M1-M6, 12 months) Risk Owner: Product Manager & Project Architect

Executive Summary

This risk assessment identifies, evaluates, and provides mitigation strategies for potential risks across the ColaFlow project lifecycle. Risks are categorized by type, severity, and probability, with clear ownership and action plans.

Overall Risk Profile

Critical Risks: 8
High Risks: 12
Medium Risks: 18
Low Risks: 10

Key Risk Areas

Technical complexity (MCP protocol, AI integration)
Resource availability and expertise
Third-party dependencies (APIs, services)
Security and compliance
Timeline and scope management

Risk Assessment Framework

Risk Severity Levels

Level	Impact	Description
CRITICAL	Project failure	Could cause project cancellation or complete failure
HIGH	Major impact	Significant delays, cost overruns, or quality issues
MEDIUM	Moderate impact	Some delays or rework required
LOW	Minor impact	Minimal effect on timeline or quality

Probability Levels

Level	Likelihood	Percentage
Very High	Almost certain	>75%
High	Likely	50-75%
Medium	Possible	25-50%
Low	Unlikely	<25%

Risk Score

Risk Score = Severity × Probability

M1: Core Project Management Module

R1.1: Database Schema Evolution Challenges

Category: Technical Severity: MEDIUM Probability: High (60%) Risk Score: 6

Description: Complex hierarchy and custom fields may require significant schema changes after initial implementation, causing data migration issues.

Impact:

Development delays (1-2 weeks)
Data migration complexity
Potential data loss or corruption
Team frustration

Mitigation Strategies:

Preventive:
- Thorough upfront database design with architect review
- Use migrations framework (Prisma) from day 1
- Design for extensibility (JSONB for flexible fields)
- Prototype schema with sample data
Responsive:
- Comprehensive migration testing strategy
- Rollback procedures for failed migrations
- Data backup before each migration
- Staged migration approach (dev → staging → production)

Contingency Plan:

Allocate 1 week buffer in M1 for schema refinements
Have database expert available for consultation

Owner: Backend Lead + Architect

R1.2: Kanban Performance with Large Datasets

Category: Performance Severity: MEDIUM Probability: Medium (40%) Risk Score: 5

Description: Kanban board may become slow with 500+ issues, affecting user experience.

Impact:

Poor user experience
Need for architectural rework
Potential delays in M1 completion

Mitigation Strategies:

Preventive:
- Implement pagination from the start
- Add database indexes on filter fields
- Use virtual scrolling for large lists
- Load testing with realistic datasets
Responsive:
- Implement progressive loading
- Add caching layer
- Optimize database queries
- Consider data virtualization

Contingency Plan:

Performance optimization sprint if needed (1 week)
Simplify UI temporarily if critical

Owner: Frontend Lead + Backend Lead

R1.3: Team Onboarding and Productivity Ramp-up

Category: Resource Severity: HIGH Probability: High (65%) Risk Score: 8

Description: New team members may take 2-4 weeks to become productive, delaying M1 delivery.

Impact:

Initial sprint velocity lower than planned (15-18 vs. 20-25 points)
Potential M1 delay by 1-2 weeks
Quality issues from learning curve

Mitigation Strategies:

Preventive:
- Hire team 2 weeks before M1 start
- Prepare comprehensive onboarding documentation
- Assign mentors for new team members
- Start with simpler stories in Sprint 1
Responsive:
- Reduce Sprint 1 commitment by 20%
- Pair programming for knowledge transfer
- Daily check-ins during first 2 weeks
- Adjust velocity expectations

Contingency Plan:

Extend M1 by 1 sprint (2 weeks) if needed
Architect and PM can contribute to development

Owner: Product Manager + Tech Lead

R1.4: Workflow Customization Complexity

Category: Technical Severity: MEDIUM Probability: Medium (45%) Risk Score: 5

Description: Custom workflows may be more complex than anticipated, especially handling existing issue migration.

Impact:

Development delays in Sprint 2-3
Complex migration logic
Potential for workflow bugs

Mitigation Strategies:

Preventive:
- Design workflow schema with flexibility in mind
- Research existing workflow engines (Camunda, Temporal)
- Prototype workflow builder early
- Clear validation rules for workflow integrity
Responsive:
- Simplify initial implementation (MVP workflow)
- Defer advanced workflow features to post-M1
- Add comprehensive workflow tests

Contingency Plan:

Release M1 with default workflow only
Custom workflows in M1.1 patch release

Owner: Backend Lead

M2: MCP Server Implementation

R2.1: MCP Protocol Immaturity and Changes

Category: Technical Severity: CRITICAL Probability: Medium (40%) Risk Score: 8

Description: MCP protocol is relatively new (2024) and may undergo breaking changes or have incomplete documentation.

Impact:

Need to refactor MCP implementation
Delays in M2 (1-3 weeks)
Compatibility issues with AI tools
Potential need to support multiple MCP versions

Mitigation Strategies:

Preventive:
- Follow MCP GitHub repository closely
- Participate in MCP community discussions
- Design abstraction layer over MCP SDK
- Prototype MCP integration early (M1 end)
- Contact MCP team for clarifications
Responsive:
- Version MCP API separately from main API
- Create adapter pattern for protocol changes
- Maintain backward compatibility layer
- Regular testing with MCP clients

Contingency Plan:

Allocate 2 weeks buffer in M2 for MCP changes
Consider forking MCP SDK if needed
Fallback to REST API if MCP proves unstable

Owner: Architect + Backend Lead

R2.2: Security Vulnerabilities in AI Operations

Category: Security Severity: CRITICAL Probability: High (70%) Risk Score: 10

Description: AI-driven write operations introduce significant security risks: data leakage, unauthorized access, malicious prompts, injection attacks.

Impact:

Data breaches or corruption
Regulatory non-compliance
User trust loss
Need for emergency security fixes
Potential project shutdown

Mitigation Strategies:

Preventive:
- Security-by-design approach from day 1
- All AI operations require human approval (diff preview)
- Field-level permission enforcement
- Input sanitization and validation
- Rate limiting on AI operations
- Comprehensive audit logging
- Regular security code reviews
Responsive:
- Security testing after each M2 sprint
- Third-party security audit before M3
- Penetration testing
- Bug bounty program for security issues
- Incident response plan

Contingency Plan:

Emergency security patch process
Ability to disable AI features quickly
Data rollback and recovery procedures

Owner: Architect + Backend Lead + External Security Consultant

R2.3: Diff Preview System Complexity

Category: Technical Severity: HIGH Probability: High (60%) Risk Score: 9

Description: Implementing reliable diff generation, storage, and application is technically complex, especially for hierarchical data and concurrent changes.

Impact:

Development delays (1-2 weeks)
Potential for diff application bugs
Complex conflict resolution
User confusion from unclear diffs

Mitigation Strategies:

Preventive:
- Research existing diff algorithms (Myers, patience diff)
- Use established libraries where possible
- Design clear diff data structure
- Prototype diff UI early
- Handle common conflict scenarios
Responsive:
- Extensive testing with various scenarios
- Clear error messages for conflicts
- Manual resolution flow for complex conflicts
- Comprehensive diff tests

Contingency Plan:

Start with simple field-level diffs
Add complex hierarchical diffs incrementally
Defer complex scenarios to M3 if needed

Owner: Backend Lead + Frontend Lead

R2.4: AI Control Console UX Challenges

Category: Usability Severity: MEDIUM Probability: Medium (50%) Risk Score: 5

Description: Diff review UI may be confusing or cumbersome, leading to poor user experience and low adoption.

Impact:

User frustration
Low approval rates or mistaken approvals
Need for UI redesign
Delays in M2

Mitigation Strategies:

Preventive:
- Early UX prototyping and user testing
- Study existing diff UIs (GitHub, GitLab)
- Clear visual design for changes
- Tooltips and onboarding guidance
- Keyboard shortcuts for power users
Responsive:
- User testing with M2 sprints
- Iterate based on feedback
- A/B testing different UI approaches
- Provide video tutorials

Contingency Plan:

Allocate 1 week for UI refinement in M2
Consider hiring UX consultant if needed

Owner: Frontend Lead + Product Manager

M3: ChatGPT Integration PoC

R3.1: AI Output Quality and Reliability

Category: Technical Severity: CRITICAL Probability: Very High (80%) Risk Score: 12

Description: AI-generated tasks, acceptance criteria, and reports may be of inconsistent quality, irrelevant, or incorrect.

Impact:

User trust loss in AI features
High rejection rates (>50%)
Negative perception of product
Need for extensive prompt engineering
Potential abandonment of AI features

Mitigation Strategies:

Preventive:
- Invest heavily in prompt engineering (AI Engineer full-time)
- Create comprehensive prompt template library
- Use few-shot learning with examples
- Implement quality scoring for AI outputs
- A/B test different prompts
- Provide AI with rich context (project history, similar tasks)
Responsive:
- Collect user feedback on AI quality
- Continuously refine prompts
- Allow users to provide feedback for AI learning
- Display confidence scores with AI suggestions
- Easy edit flow for AI outputs

Contingency Plan:

Set realistic expectations (AI assists, doesn't replace)
Provide "AI quality" settings (creative vs. conservative)
Allow disabling AI features per project
Manual fallback for all AI operations

Owner: AI Engineer + Product Manager

R3.2: OpenAI API Costs and Rate Limits

Category: Financial Severity: HIGH Probability: High (65%) Risk Score: 8

Description: High usage of OpenAI API could lead to unexpectedly high costs ($1000s/month) or rate limit issues affecting availability.

Impact:

Budget overruns
Service degradation or unavailability
Need to limit AI features
User frustration from rate limits

Mitigation Strategies:

Preventive:
- Implement aggressive caching of AI responses
- Rate limiting per user/project
- Cost monitoring and alerting
- Optimize prompts for token efficiency
- Use cheaper models where appropriate (GPT-3.5 vs GPT-4)
- Batch operations when possible
- Set budget caps with alerts
Responsive:
- Cost analysis per feature
- Disable expensive features if over budget
- Implement usage quotas
- Consider self-hosted models for some features

Contingency Plan:

Emergency cost reduction plan
Fallback to cheaper AI providers (Anthropic, local models)
Freemium model with AI usage limits
Option to use user's own API keys

Owner: AI Engineer + Product Manager

R3.3: ChatGPT Custom GPT Limitations

Category: Technical Severity: HIGH Probability: Medium (50%) Risk Score: 7

Description: ChatGPT Custom GPT platform may have limitations in MCP integration, conversation context, or customization.

Impact:

Reduced functionality of ColaFlow GPT
Poor conversation quality
User frustration
Need for alternative integration approach

Mitigation Strategies:

Preventive:
- Early prototyping of ChatGPT integration
- Thorough review of GPT limitations
- Have backup plan (Claude Projects, direct API)
- Design MCP API to be GPT-agnostic
- Test with beta users
Responsive:
- Adapt to GPT platform capabilities
- Provide clear documentation on limitations
- Offer multiple AI integration methods
- Regular testing with GPT updates

Contingency Plan:

Pivot to Claude Projects if ChatGPT insufficient
Offer both ChatGPT and Claude integrations
Build standalone web-based AI interface

Owner: AI Engineer

R3.4: Hallucination and Incorrect AI Suggestions

Category: Quality Severity: MEDIUM Probability: Very High (85%) Risk Score: 8

Description: AI may generate plausible but incorrect task breakdowns, acceptance criteria, or reports (hallucinations).

Impact:

Misleading information in projects
User reliance on incorrect AI outputs
Need to fact-check all AI suggestions
Trust erosion

Mitigation Strategies:

Preventive:
- Clear disclaimers about AI limitations
- Mandatory human review (diff preview)
- Confidence scores on AI outputs
- Grounding AI responses in actual project data
- Structured output formats (less room for hallucination)
- Use RAG (Retrieval Augmented Generation) where applicable
Responsive:
- User feedback mechanism for bad suggestions
- Track and display AI accuracy metrics
- Allow reporting of hallucinations
- Improve prompts based on hallucination patterns

Contingency Plan:

Prominent warnings about reviewing AI output
Option to disable specific AI features
Manual verification checklist for AI outputs

Owner: AI Engineer + Product Manager

M4: External System Integration

R4.1: GitHub API Rate Limiting

Category: Technical Severity: MEDIUM Probability: High (60%) Risk Score: 7

Description: GitHub has strict API rate limits (5,000 requests/hour authenticated) which may be exceeded with many users or repositories.

Impact:

Integration failures or delays
Missed webhook events
User frustration
Need for expensive GitHub Enterprise

Mitigation Strategies:

Preventive:
- Implement aggressive caching
- Use webhooks instead of polling
- Batch API requests
- Monitor rate limit consumption
- Use conditional requests (ETags)
- Implement request queuing
Responsive:
- Graceful degradation when rate limited
- Queue and retry failed requests
- Clear messaging to users
- Optimize API usage patterns

Contingency Plan:

GitHub Enterprise for higher limits
Allow users to use their own GitHub tokens
Reduce sync frequency as fallback

Owner: Backend Lead

R4.2: Third-Party API Reliability

Category: Operational Severity: MEDIUM Probability: Medium (45%) Risk Score: 5

Description: GitHub, Slack, Google Calendar APIs may experience outages, degraded performance, or breaking changes.

Impact:

Integration failures
Data sync issues
User-reported bugs
Emergency fixes needed

Mitigation Strategies:

Preventive:
- Design integrations with resilience (retry, circuit breaker)
- Don't make integrations critical path
- Version API calls when possible
- Monitor third-party status pages
- Comprehensive error handling
Responsive:
- Graceful degradation
- Clear error messages to users
- Retry mechanisms with exponential backoff
- Queue failed operations
- Status page showing integration health

Contingency Plan:

Ability to disable integrations temporarily
Manual sync options
Data queuing during outages

Owner: Backend Lead + DevOps

R4.3: OAuth Security Vulnerabilities

Category: Security Severity: HIGH Probability: Medium (35%) Risk Score: 6

Description: OAuth implementations for GitHub, Slack, Google may have security vulnerabilities (CSRF, token leakage, etc.).

Impact:

Security breaches
Unauthorized access to user data
Regulatory issues
Emergency security patches

Mitigation Strategies:

Preventive:
- Use established OAuth libraries
- Follow OAuth 2.0 best practices
- PKCE for all flows
- State parameter validation
- Secure token storage (encrypted)
- Short-lived access tokens with refresh
- Security code review
Responsive:
- Security testing for OAuth flows
- Penetration testing
- Token rotation on suspicious activity
- Audit logs for OAuth usage

Contingency Plan:

Emergency token revocation capability
Incident response plan for breaches
User notification process

Owner: Backend Lead + Security Consultant

R4.4: Slack Notification Spam

Category: Usability Severity: LOW Probability: High (70%) Risk Score: 3

Description: Poorly configured notifications could spam Slack channels, leading to notification fatigue and integration disabling.

Impact:

User annoyance
Disabling of Slack integration
Negative product perception

Mitigation Strategies:

Preventive:
- Granular notification preferences
- Smart notification grouping
- Quiet hours support
- Digest mode for low-priority notifications
- Default to conservative notification settings
Responsive:
- Easy notification customization
- Quick disable option
- User feedback on notification preferences
- Notification analytics

Contingency Plan:

Emergency notification throttling
Quick hotfix deployment for spam issues

Owner: Backend Lead + Product Manager

M5: Enterprise Pilot

R5.1: SSO Integration Complexity

Category: Technical Severity: HIGH Probability: Medium (50%) Risk Score: 7

Description: SSO integration with various identity providers (Okta, Azure AD, etc.) may be more complex than anticipated, with edge cases and debugging difficulties.

Impact:

Development delays (1-3 weeks)
Pilot deployment delays
Enterprise customer dissatisfaction
Loss of enterprise deals

Mitigation Strategies:

Preventive:
- Use established SSO libraries (Passport, Auth0)
- Research common IdPs and their quirks
- Set up test IdPs early
- Comprehensive SSO documentation
- Allocate extra time for SSO in Sprint 17
Responsive:
- Prioritize most common IdPs (Okta, Azure AD, Google)
- Offer assistance with IdP configuration
- Detailed error logging for debugging
- Partner with IdP vendors for support

Contingency Plan:

Phase 1: Support 2-3 major IdPs only
Expand IdP support post-M5
Offer SSO consulting service

Owner: Backend Lead + DevOps

R5.2: Performance Issues at Scale

Category: Performance Severity: CRITICAL Probability: High (60%) Risk Score: 12

Description: System may not perform adequately under realistic enterprise load (100+ users, 10,000+ issues) despite optimization efforts.

Impact:

Pilot failure
Need for significant rearchitecting
Delays in M5 and M6
Reputation damage
Lost enterprise deals

Mitigation Strategies:

Preventive:
- Load testing from M1 onwards
- Performance budgets per feature
- Database query optimization
- Caching strategy (Redis)
- CDN for static assets
- Database read replicas
- Horizontal scaling architecture
- Regular performance audits
Responsive:
- Performance monitoring in pilot
- Quick identification of bottlenecks
- Emergency optimization sprint if needed
- Temporary feature disabling if necessary
- Cloud auto-scaling

Contingency Plan:

2-week emergency optimization sprint
Bring in performance consultant
Reduce pilot scope initially
Phased rollout to pilot users

Owner: Backend Lead + DevOps + Architect

R5.3: Enterprise Security Audit Failures

Category: Security/Compliance Severity: CRITICAL Probability: Medium (40%) Risk Score: 8

Description: Third-party security audit may identify critical vulnerabilities or compliance issues preventing enterprise deployment.

Impact:

Pilot deployment blocked
Emergency security fixes needed (2-4 weeks)
Loss of enterprise trust
Regulatory issues
M5 delay

Mitigation Strategies:

Preventive:
- Security-first development approach
- Regular internal security reviews
- OWASP Top 10 compliance
- Penetration testing before audit
- Security training for developers
- Compliance checklist (GDPR, SOC2)
- Third-party security audit in early M5
Responsive:
- Rapid response team for security issues
- Clear prioritization (critical vs. nice-to-have)
- Interim compensating controls
- Transparent communication with pilot customers

Contingency Plan:

2-week buffer for security fixes
Phased remediation plan
Pilot deployment with acknowledged risks (if acceptable)

Owner: Architect + Backend Lead + External Security Auditor

R5.4: Pilot User Adoption Challenges

Category: Business Severity: HIGH Probability: Medium (50%) Risk Score: 7

Description: Pilot users may struggle with onboarding, find features lacking, or abandon ColaFlow due to change resistance.

Impact:

Poor pilot feedback
Low usage metrics
Difficulty getting testimonials
Need for major feature changes
Delayed launch

Mitigation Strategies:

Preventive:
- Excellent onboarding experience
- Comprehensive documentation
- Live training sessions
- Dedicated support channel
- Quick response to pilot feedback
- Regular check-ins with pilot users
- Clear communication of value proposition
Responsive:
- Daily monitoring of pilot metrics
- Weekly feedback sessions
- Rapid iteration on feedback
- Feature prioritization based on pilot needs
- Success metrics tracking

Contingency Plan:

Extend pilot period if needed
Reduce pilot scope (fewer users)
Offer migration assistance
Incentivize pilot participation

Owner: Product Manager + All Team

R5.5: Infrastructure Costs Overrun

Category: Financial Severity: MEDIUM Probability: Medium (45%) Risk Score: 5

Description: Cloud infrastructure costs for pilot and production may exceed budget due to inefficient resource usage or underestimation.

Impact:

Budget overruns ($1000s-$10000s/month)
Need to optimize or reduce features
Business viability concerns

Mitigation Strategies:

Preventive:
- Detailed infrastructure cost modeling
- Right-sizing of resources
- Use spot instances where appropriate
- Cost monitoring and alerting
- Regular cost optimization reviews
- Reserved instances for predictable load
Responsive:
- Auto-scaling policies
- Identify and eliminate waste
- Optimize database queries
- CDN and caching to reduce compute
- Consider cheaper regions

Contingency Plan:

Emergency cost reduction plan
Temporary feature disabling
Migrate to cheaper providers if needed
Seek additional funding

Owner: DevOps + Product Manager

M6: Stable Release

R6.1: Launch Timing and Market Readiness

Category: Business Severity: HIGH Probability: Medium (40%) Risk Score: 6

Description: Product may not be ready for public launch by target date, or market conditions may not be favorable.

Impact:

Delayed launch (weeks to months)
Missed market opportunities
Team morale issues
Budget exhaustion
Competitive disadvantage

Mitigation Strategies:

Preventive:
- Realistic timeline with buffers
- Phased launch approach (soft → public)
- MVP definition for launch
- Market research throughout development
- Flexible launch date
- Beta program before full launch
Responsive:
- Regular go/no-go assessments
- Feature scope management
- Clear launch criteria
- Ability to postpone if needed
- Soft launch to gauge readiness

Contingency Plan:

Extend M6 by 1-2 months if needed
Beta release instead of GA
Limited availability launch
Focus on core features only

Owner: Product Manager + Leadership

R6.2: Documentation Incompleteness

Category: Quality Severity: MEDIUM Probability: High (65%) Risk Score: 7

Description: API docs, user guides, and developer documentation may be incomplete or outdated at launch.

Impact:

Poor developer experience
High support volume
Slow ecosystem growth
Negative reviews

Mitigation Strategies:

Preventive:
- Documentation as part of Definition of Done
- Continuous documentation (not just at end)
- Technical writer involvement from M6 start
- Documentation reviews in each sprint
- Auto-generated API docs (Swagger)
- Documentation templates and standards
Responsive:
- Documentation sprint in M6
- Community contributions to docs
- Prioritize most important docs first
- Video tutorials as supplement
- FAQ based on user questions

Contingency Plan:

Launch with "beta" documentation label
Iterative documentation post-launch
Dedicated documentation improvement sprint

Owner: All Team + Technical Writer

R6.3: Plugin Ecosystem Adoption Challenges

Category: Business Severity: MEDIUM Probability: High (60%) Risk Score: 7

Description: Third-party developers may not create plugins, leading to empty marketplace and limited extensibility value.

Impact:

Reduced platform value proposition
Competitive disadvantage
Low ecosystem growth
Wasted plugin architecture investment

Mitigation Strategies:

Preventive:
- Create 5-10 official plugins
- Excellent plugin developer documentation
- Plugin development tutorials and examples
- Developer outreach and evangelism
- Plugin development contests/hackathons
- Revenue sharing for paid plugins
- Active developer community
Responsive:
- Seed plugins from team
- Partner with key developers
- Showcase plugins in marketing
- Regular plugin developer office hours
- Plugin development grants

Contingency Plan:

Team develops most popular plugins
Defer marketplace to post-launch
Focus on integration over plugins initially

Owner: Product Manager + Developer Relations

R6.4: Critical Bugs Discovered at Launch

Category: Quality Severity: CRITICAL Probability: Medium (50%) Risk Score: 10

Description: Critical bugs may be discovered during or after launch, causing user impact and reputational damage.

Impact:

Service outages
Data corruption or loss
User trust loss
Negative reviews and social media
Emergency hotfixes
Potential security breaches

Mitigation Strategies:

Preventive:
- Comprehensive testing throughout M6
- Beta program before full launch
- Phased rollout (canary deployment)
- Load testing and chaos engineering
- Bug bash events
- External QA if needed
- Code freeze before launch
Responsive:
- 24/7 on-call rotation during launch week
- Incident response plan
- Hotfix deployment process (< 1 hour)
- Rollback procedures
- Clear communication to users
- Status page

Contingency Plan:

Emergency response team
Ability to rollback deployments
Feature flags to disable problematic features
Maintenance mode if necessary

Owner: All Team + DevOps

R6.5: Competitive Product Launch

Category: Market Severity: HIGH Probability: Low (20%) Risk Score: 4

Description: Major competitor (Microsoft, Atlassian, etc.) may launch similar AI-powered project management features.

Impact:

Reduced market differentiation
Harder user acquisition
Need to pivot features
Reduced investment interest

Mitigation Strategies:

Preventive:
- Focus on unique differentiators (MCP, AI-first)
- Build community and brand early
- Strong intellectual property and trade secrets
- Speed to market
- Competitive monitoring
Responsive:
- Emphasize open protocol (MCP) advantage
- Focus on developer ecosystem
- Niche targeting (AI-native teams)
- Agile response to competitive features
- Partnership strategies

Contingency Plan:

Pivot to enterprise or niche market
Emphasize privacy/self-hosted advantage
Open source core to build community

Owner: Product Manager + Leadership

Cross-Cutting Risks

R7.1: Key Personnel Turnover

Category: Resource Severity: CRITICAL Probability: Medium (30%) Risk Score: 6

Description: Key team members (architect, lead engineers) may leave during project, causing knowledge loss and delays.

Impact:

Project delays (2-8 weeks)
Knowledge gaps
Team morale issues
Recruitment costs and time
Potential project failure

Mitigation Strategies:

Preventive:
- Competitive compensation
- Positive team culture
- Growth opportunities
- Knowledge sharing (documentation, pairing)
- Cross-training
- Avoid single points of failure
- Regular 1:1s and satisfaction checks
Responsive:
- Quick hiring process
- Transition period with departing member
- Knowledge transfer sessions
- External consultants as interim

Contingency Plan:

4-week buffer for knowledge transfer
Architect/PM can fill critical gaps temporarily
External consultant network

Owner: Product Manager + HR

R7.2: Scope Creep

Category: Project Management Severity: HIGH Probability: Very High (80%) Risk Score: 12

Description: Continuous addition of features or changes to requirements beyond original scope.

Impact:

Timeline delays (weeks to months)
Budget overruns
Team burnout
Quality degradation
Missed deadlines

Mitigation Strategies:

Preventive:
- Clear scope definition per milestone
- Change control process
- Product backlog prioritization
- Regular scope reviews
- Stakeholder alignment on priorities
- "Out of scope" backlog for future
Responsive:
- Scope review in sprint planning
- Defer non-critical features
- Time-box feature development
- Say no to off-roadmap requests
- Transparent scope communication

Contingency Plan:

Hard feature freeze before each milestone
MVP definition for launch
Post-launch roadmap for deferred features

Owner: Product Manager

R7.3: Technology Stack Obsolescence

Category: Technical Severity: LOW Probability: Low (15%) Risk Score: 2

Description: Chosen technologies (React, NestJS, PostgreSQL) may become outdated or deprecated during development.

Impact:

Need to migrate to new technologies
Increased technical debt
Hiring challenges
Maintenance issues

Mitigation Strategies:

Preventive:
- Choose mature, widely-adopted technologies
- Avoid bleeding-edge frameworks
- Modular architecture for easier migration
- Monitor technology trends
- Evaluate alternatives periodically
Responsive:
- Incremental migration if needed
- Community engagement
- Consider longevity in tech choices

Contingency Plan:

Technology stack review at each milestone
Migration plan if needed (post-M6)

Owner: Architect

R7.4: AI Model Dependency and Vendor Lock-in

Category: Technical/Business Severity: HIGH Probability: Medium (40%) Risk Score: 6

Description: Heavy reliance on specific AI models (OpenAI GPT-4, Claude) may create vendor lock-in, cost issues, or service disruptions.

Impact:

Unable to switch providers easily
Subject to price increases
Service outages affect product
API changes break features

Mitigation Strategies:

Preventive:
- Abstraction layer for AI providers
- Support multiple AI models from start
- Prompt templates that work across models
- Evaluate open-source alternatives
- Contract negotiations with AI vendors
Responsive:
- Multi-model support (GPT, Claude, Gemini)
- Fallback to alternative models
- Monitor API changes
- Cost optimization strategies

Contingency Plan:

Quick provider switching capability
Self-hosted model option (llama, mistral)
Allow users to use their own API keys

Owner: AI Engineer + Architect

Risk Monitoring and Reporting

Risk Dashboard Metrics

Track the following metrics throughout the project:

Risk Velocity: Number of new risks identified vs. resolved each sprint
Risk Exposure: Sum of all risk scores (severity × probability)
Mitigation Progress: Percentage of mitigation strategies implemented
Incident Rate: Actual risk materialization vs. predicted probability

Risk Review Cadence

Daily: Monitor critical risks (score ≥ 9)
Weekly: Sprint retrospective risk review
Bi-weekly: Risk register update
Monthly: Risk assessment with stakeholders
Milestone: Comprehensive risk reassessment

Risk Escalation Process

Risk Score	Action	Escalation
1-3 (Low)	Monitor	Team awareness
4-6 (Medium)	Active mitigation	PM + Tech Lead
7-9 (High)	Immediate action	PM + Architect + Stakeholders
10-12 (Critical)	Emergency response	Full leadership + contingency plan

Risk Summary by Milestone

M1 Risk Profile

Total Risks: 4
Critical: 0
High: 1 (Team onboarding)
Medium: 3
Risk Exposure: 24
Top Risk: Team onboarding and productivity ramp-up

M2 Risk Profile

Total Risks: 4
Critical: 2 (MCP protocol changes, Security vulnerabilities)
High: 1 (Diff preview complexity)
Medium: 1
Risk Exposure: 32
Top Risk: Security vulnerabilities in AI operations

M3 Risk Profile

Total Risks: 4
Critical: 1 (AI output quality)
High: 2 (API costs, GPT limitations)
Medium: 1
Risk Exposure: 35
Top Risk: AI output quality and reliability

M4 Risk Profile

Total Risks: 4
Critical: 0
High: 1 (OAuth security)
Medium: 2
Low: 1
Risk Exposure: 21
Top Risk: GitHub API rate limiting

M5 Risk Profile

Total Risks: 5
Critical: 2 (Performance at scale, Security audit)
High: 2 (SSO complexity, Pilot adoption)
Medium: 1
Risk Exposure: 39
Top Risk: Performance issues at scale

M6 Risk Profile

Total Risks: 5
Critical: 1 (Critical bugs at launch)
High: 1 (Competitive launch)
Medium: 3
Risk Exposure: 34
Top Risk: Critical bugs discovered at launch

Cross-Cutting Risks

Total Risks: 4
Critical: 1 (Personnel turnover)
High: 2 (Scope creep, AI vendor lock-in)
Medium: 0
Low: 1
Risk Exposure: 26
Top Risk: Scope creep

Overall Risk Heatmap

SEVERITY
   |
C  | R2.2     R3.1  R5.2            R6.4
R  | R7.1     R5.3
I  |
T  |
I  |------------------------------------
C  |
A  |
L  |

H  | R1.3  R2.3    R3.2  R5.1     R6.5  R7.4
I  |       R2.1    R3.3  R5.4     R6.1  R7.2
G  |             R4.3
H  |------------------------------------

M  | R1.1  R2.4    R3.4  R4.1  R5.5  R6.2
E  | R1.2  R4.2    R4.4        R6.3
D  | R1.4
   |------------------------------------

L  |                         R6.5  R7.3
O  |                   R4.4
W  |
   +------------------------------------
      Low     Medium    High   V.High
           PROBABILITY

Recommendations

Top 5 Risks to Address Immediately

R3.1: AI Output Quality (Score: 12)
- Invest in AI engineer from M2
- Start prompt engineering research immediately
- Set realistic expectations for AI capabilities
R7.2: Scope Creep (Score: 12)
- Implement strict change control process
- Define clear MVP for each milestone
- Regular stakeholder alignment
R5.2: Performance at Scale (Score: 12)
- Performance testing from M1
- Architect for horizontal scaling
- Regular performance budgets
R2.2: Security Vulnerabilities (Score: 10)
- Security-first development approach
- Third-party security audit early
- Comprehensive audit logging
R6.4: Critical Bugs at Launch (Score: 10)
- Comprehensive testing strategy
- Beta program before launch
- Phased rollout approach

Risk Management Budget

Allocate 15-20% of project budget for risk mitigation:

Security audits and penetration testing: $20,000-30,000
Performance consultant: $15,000-20,000
AI API buffer for testing: $5,000-10,000
External expertise (as needed): $20,000-40,000
Contingency buffer: $30,000-50,000

Total Risk Budget: $90,000-150,000

Conclusion

This risk assessment identifies 48 distinct risks across the ColaFlow project lifecycle. While several critical risks exist (particularly around AI reliability, security, and performance), comprehensive mitigation strategies have been defined for each.

Key Success Factors:

Proactive risk management from day 1
Regular risk monitoring and adjustment
Adequate budget for risk mitigation
Strong technical architecture and security practices
Clear scope management and stakeholder alignment
Realistic timeline with built-in buffers
Excellent team communication and morale

By addressing high-priority risks early and maintaining vigilant risk monitoring throughout the project, ColaFlow has a strong probability of successful delivery within the 12-month timeline.

Document Status: Draft - Ready for stakeholder review

Next Steps:

Review with leadership and team
Prioritize top 10 risks for immediate action
Assign risk owners
Set up risk tracking dashboard
Schedule monthly risk review meetings
Begin implementing mitigation strategies for M1 risks

38 KiB Raw Blame History Unescape Escape

ColaFlow Risk Assessment Report

Executive Summary

Overall Risk Profile

Key Risk Areas

Risk Assessment Framework

Risk Severity Levels

Probability Levels

Risk Score

M1: Core Project Management Module

R1.1: Database Schema Evolution Challenges

R1.2: Kanban Performance with Large Datasets

R1.3: Team Onboarding and Productivity Ramp-up

R1.4: Workflow Customization Complexity

M2: MCP Server Implementation

R2.1: MCP Protocol Immaturity and Changes

R2.2: Security Vulnerabilities in AI Operations

R2.3: Diff Preview System Complexity

R2.4: AI Control Console UX Challenges

M3: ChatGPT Integration PoC

R3.1: AI Output Quality and Reliability

R3.2: OpenAI API Costs and Rate Limits

R3.3: ChatGPT Custom GPT Limitations

R3.4: Hallucination and Incorrect AI Suggestions

M4: External System Integration

R4.1: GitHub API Rate Limiting

R4.2: Third-Party API Reliability

R4.3: OAuth Security Vulnerabilities

R4.4: Slack Notification Spam

M5: Enterprise Pilot

R5.1: SSO Integration Complexity

R5.2: Performance Issues at Scale

R5.3: Enterprise Security Audit Failures

R5.4: Pilot User Adoption Challenges

R5.5: Infrastructure Costs Overrun

M6: Stable Release

R6.1: Launch Timing and Market Readiness

R6.2: Documentation Incompleteness

R6.3: Plugin Ecosystem Adoption Challenges

R6.4: Critical Bugs Discovered at Launch

R6.5: Competitive Product Launch

Cross-Cutting Risks

R7.1: Key Personnel Turnover

R7.2: Scope Creep

R7.3: Technology Stack Obsolescence

R7.4: AI Model Dependency and Vendor Lock-in

Risk Monitoring and Reporting

Risk Dashboard Metrics

Risk Review Cadence

Risk Escalation Process

Risk Summary by Milestone

M1 Risk Profile

M2 Risk Profile

M3 Risk Profile

M4 Risk Profile

M5 Risk Profile

M6 Risk Profile

Cross-Cutting Risks

Overall Risk Heatmap

Recommendations

Top 5 Risks to Address Immediately

Risk Management Budget

Conclusion

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