DEMO AI SOLUTIONS AB
RISK MITIGATION ACTIONS LOG
Document ID: DEMOAI-LOG-MITI-001
Version: 2.0 | Updated by: Erik Johansson, Risk Manager | Date: 2026-04-15

=== FICTIONAL DOCUMENT — FOR DEMONSTRATION PURPOSES ONLY ===

MITI-001: Dual-threshold detection with human fallback
  Risk:        RISK-001 (False negative)
  Type:        Design-level risk elimination
  Description: Implemented dual confidence threshold: components scoring
               below 0.95 confidence are routed to human inspector. This is
               a design-level risk elimination measure that removes the risk
               of undetected critical defects for low-confidence predictions.
  Feasibility: Technical feasibility assessment: adds 3ms latency per image.
               Human inspection capacity sufficient for estimated 8% fallback.
  Implemented: 2026-01-20 by Anna Svensson, Lead Developer
  Status:      Active
  Verified:    Zero critical defects passed at 0.95 threshold in validation

MITI-002: Secondary model cross-validation
  Risk:        RISK-001 (False negative)
  Type:        Mitigation measure
  Description: A secondary lightweight model performs cross-validation on
               safety-critical components. Both models must agree. This
               mitigation measure reduces false negative risk.
  Implemented: 2026-02-10 by Anna Svensson
  Status:      Active
  Verified:    Disagreement rate 2.1% — all routed to human

MITI-003: Operator override for false positives
  Risk:        RISK-002 (False positive)
  Type:        Control measure
  Description: Quality control operators can override false positive
               rejections through a documented review process. Each override
               is logged with operator ID, timestamp, and justification.
  Implemented: 2025-12-15 by Karl Nilsson, QA Manager
  Status:      Active
  Verified:    Monthly override log review — no systematic misuse detected

MITI-004: Automated drift detection with retraining trigger
  Risk:        RISK-003 (Model drift)
  Type:        Mitigation measure
  Description: Continuous monitoring of classification accuracy. Alert when
               7-day rolling accuracy drops below 97.5%. Retraining pipeline
               initiated automatically with human approval gate.
  Implemented: 2026-03-01 by Anna Svensson
  Status:      Active
  Verified:    Triggered once (2026-03-22), retraining completed successfully

MITI-005: Quarterly training data review
  Risk:        RISK-004 (Bias in training data)
  Type:        Control measure
  Description: Training dataset reviewed quarterly for defect type coverage.
               New defect types from production incidents added within 30 days.
  Implemented: 2026-01-01 by Erik Johansson
  Status:      Active
  Verified:    Q1 2026 review completed — 3 new defect types added

MITI-006: Mandatory manual spot-check protocol
  Risk:        RISK-005 (Worker over-reliance)
  Type:        Control measure
  Description: Operators required to manually inspect random 5% sample per
               shift, regardless of AI classification result.
  Implemented: 2026-01-10 by Karl Nilsson, QA Manager
  Status:      Active
  Verified:    Spot-check compliance rate: 94% across all shifts