Mind Matrix: Navigation
SOP-1061-01-AI_Incident-Tracking
Title: Incident Tracking
Revision #: 1.0
Effective Date: 2025-01-01
Pages: 1–9
Owner: AI-SDLC Quality/Regulatory Lead
Approved by: AI-IRB Liaison
Signature/Date: ______________________
[Navigation] AI Mind Matrix
1. Objective
This SOP describes the standardized method for incident tracking within the AI-SDLC environment to ensure all production incidents (defects) are reported, triaged, documented, and resolved efficiently, utilizing a Rapid Feedback Loop to inform future model iterations.
2. Scope
- Applies to all AI-related production incidents that occur post-deployment, including software malfunctions, data quality anomalies, or user-facing disruptions.
- Encompasses the entire incident management lifecycle, from initial identification through final closure and post-incident review.
- Aligns with AI-IRB guidelines for ethically and responsibly handling issues that impact model outputs or cause potential user harm.
3. Definitions
| Term | Definition |
|---|---|
| Incident | Any unexpected behavior, defect, or anomaly in production systems requiring immediate action. |
| Incident Logging | The process of recording details about the incident, such as date/time, severity, steps. |
| AI-IRB | The internal/external AI Institutional Review Board ensuring compliance with ethical AI use. |
| Severity Level | Classification of incident urgency (e.g. Emergency, High, Medium, Low) to set resolution priority. |
| Root-Cause | The fundamental technical or procedural reason behind the incident. |
| SLA | Service Level Agreement specifying commitments on incident response & resolution. |
| Drift Anomaly | A deviation in model performance relative to the training baseline, triggering automated alerts. |
| Production | The environment or set of systems actively serving end-users or clients. |
| Authorized AI Agent | A validated AI system or subsystem identified within the Mind Matrix as having the authority to execute specific SDLC or operational tasks. |
4. Roles and Responsibilities
| Role | Responsibilities |
|---|---|
| Defect Reporter | Identifies and reports an incident, providing essential info (symptoms, environment, steps to reproduce). |
| Technical Support | Acts as the funnel for external client incidents; logs or rejects with explanation if incomplete; escalates for triage. |
| Quality Assurance | Oversees the centralized tracking system (e.g., SQA Manager); validates severity, ensures thorough documentation. |
| AI-IRB Liaison | Reviews incidents with ethical implications; ensures compliance with AI responsibility guidelines, including monitoring for recursive self-improvement subroutine deviations. |
| Development | Investigates, fixes code or data issues, re-tests, and coordinates release of patches or emergency pushes. |
| Operations | Confirms production environment readiness for patches; coordinates with QA to deploy fixes safely. |
| Project Manager | Monitors incident resolution progress; communicates updates to stakeholders, ensures SLA adherence. |
5. Procedure Activities
5.1 Incident Identification and Logging
- Initiate Report
- Who: Defect Reporter (internal staff or external user via Tech Support).
- What: Fill out Incident Report with date/time, environment, severity recommendation.
- Technical Support Review
- Who: Tech Support reviews the form within 1 business day.
- If incomplete or unclear, returns to reporter for more info.
5.2 Severity Validation
- Open Defect
- Who: QA logs the incident in the Production Defect Tracking Tool with status “Open.”
- What: QA might override or confirm the recommended severity (Emergency, High, Medium, Low).
- If critical ethical concerns or potential negative user impact → AI-IRB Liaison alerted immediately.
5.3 Assign and Investigate
- Assign Developer
- Who: QA or Project Manager assigns the incident to a developer based on skill and availability.
- Triaging
- Who: Developer replicates the incident in a safe environment, analyzes logs, data, or model behavior.
- Drift Analysis: If the incident relates to model output, perform a statistical drift check against the baseline, including recursive self-improvement subroutine consistency audits.
- If the fix seems to impact AI model usage or ethical constraints → loop in AI-IRB Liaison for guidance.
5.4 Resolution and Testing
- Fix Implementation
- Who: Developer applies code or data fix, runs unit tests.
- Deployment Preparation
- Who: Developer prepares patch or emergency push.
- What: If severity is “Emergency,” follow emergency patch SOP.
- QA Validation
- Who: QA re-tests fix in staging or test environment.
- If AI-IRB compliance was triggered, AI-IRB Liaison verifies no policy violations remain.
5.5 Release to Production
- Operations
- Confirms readiness to deploy fix.
- Push
- Who: Developer or designated Ops staff merges fix into the production branch, updates version logs.
- Status Update
- Who: Project Manager notifies stakeholders, updates SLA metrics (resolution time, etc.).
5.6 Defect Closure
- Close Defect
- Who: QA sets status to “Closed” once verified in production.
- Post-incident Review
- What: Evaluate root cause, resolution time, potential AI ethics lessons.
- Document in the tracking tool, integrating Exochain Peer Reviews for fairness and security validation of the final fix.
6. Metrics
| Metric | Description |
|---|---|
| Time to Log | Hours between initial detection and incident entry in tracking system. |
| Time to Fix | Hours/days from assignment to resolution. |
| Incident Severity Distribution | Tracking how many incidents are Emergency, High, Medium, Low. |
| Ethical Impact Cases | Number of incidents requiring AI-IRB involvement. |
| Recurring Root Causes | Categorized list of root causes to identify patterns for process improvement. |
7. Forms/Records
- Incident Report: Official form to capture defect details (SQA Manager or ticket system).
- Emergency Push Sign-off: Only if severity is “Emergency.”
- AI-IRB Escalation Form: Documents ethic-related concerns.
8. Tools/Software/Technology Used
- Production Defect Tracking Tool (e.g., SQA Manager, JIRA, etc.)
- Version Control System (e.g. Git/Code Repo for fix merges)
- Continuous Integration (automated builds & quick staging environment)
9. Revision History
| Revision | Date | Description of Change | Author/Change Approver |
|---|---|---|---|
| 1.0 | 2025-01-01 | Initial release of SOP-1061-01-AI for Incident Tracking | AI-SDLC Quality/Regulatory Lead |
Document Ends
@startuml
title SOP-1061-01 Incident Tracking - Sequence Diagram
participant “Defect Reporter” as DR
participant “Technical Support” as TS
participant “Quality Assurance” as QA
participant “AI-IRB Liaison” as IRB
participant “Development” as DEV
participant “Operations” as OPS
participant “Project Manager” as PM
== Incident Logging ==
DR -> TS: Submit Incident Report (description, severity suggestion)
TS -> TS: Review Report for completeness
alt Report incomplete
TS -> DR: Request additional details
DR —> TS: Provide clarifications
end
TS -> QA: Forward Incident with severity suggestion
== Validate Severity / Open Defect ==
QA -> QA: Assess severity level
alt Ethical concerns found
QA -> IRB: Alert AI-IRB Liaison for ethical review
end
QA -> QA: Open defect in system (status=Open)
QA -> PM: Confirm assignment of developer
== Assign Developer and Investigate ==
PM -> DEV: Assign defect to developer
DEV -> DEV: Reproduce issue in safe environment
alt Further ethical risk discovered
DEV -> IRB: Consult AI-IRB Liaison
end
DEV -> QA: Provide triage outcome
== Implement and Test Fix ==
DEV -> DEV: Implement fix and run local unit tests
DEV -> QA: Deliver patch for QA validation
QA -> OPS: Coordinate environment for staging fix
OPS -> QA: Staging environment ready
QA -> QA: Validate fix in staging
alt Fix incomplete
QA -> DEV: Re-open defect for additional corrections
DEV -> QA: Provide updated patch
end
QA -> QA: Confirm fix acceptance (resolved in staging)
== Deploy to Production ==
QA -> OPS: Authorize production deployment
OPS -> DEV: Merge fix into production environment
DEV -> PM: Notifies success of deployment
PM -> TS: Update incident resolution metrics
== Close Incident ==
QA -> QA: Close defect (status=Closed)
QA -> QA: Post-incident review (root cause, lessons)
alt AI-IRB involvement
IRB -> QA: Final compliance check or note
end
@enduml
Short textual explanation: This sequence diagram illustrates how an incident is reported, validated, assigned a developer, fixed, tested (including possible AI-IRB review), and eventually deployed to production. Once verified in production, QA closes the defect and performs a post-incident review.