The Four Phases
A continuous cycle that never ends - each iteration builds on the last, creating ever-improving AI safety practices.
Identify & Plan
Identify AI safety risks, set objectives, and develop comprehensive action plans based on frameworks like EU AI Act, NIST AI RMF, and ISO 42001.
- Risk assessment and categorization
- Objective setting with measurable KPIs
- Resource allocation and timeline planning
- Stakeholder identification and engagement
Implement & Execute
Execute the planned safety measures, deploy monitoring systems, and implement controls across the AI lifecycle.
- Deploy safety controls and guardrails
- Implement monitoring and logging
- Train teams on safety protocols
- Document all implementation steps
Monitor & Measure
Continuously monitor AI systems, measure performance against objectives, and validate effectiveness of safety controls.
- Real-time system monitoring
- Performance metric tracking
- Compliance audits and reviews
- Incident detection and logging
Improve & Iterate
Analyze findings, implement corrective actions, standardize successful practices, and continuously improve safety measures.
- Root cause analysis of issues
- Corrective and preventive actions
- Process optimization and refinement
- Knowledge sharing and documentation
Why SOAI-PDCA?
A proven methodology adapted for the unique challenges of AI safety management.
Continuous Improvement
Never settle for "good enough" - constantly evolve safety measures as AI systems and risks change.
Data-Driven Decisions
Make informed decisions based on real metrics and evidence, not assumptions or guesswork.
Proactive Risk Management
Identify and address potential issues before they become actual problems or incidents.
Regulatory Alignment
Demonstrate systematic approach to compliance with EU AI Act, NIST AI RMF, and ISO 42001.
Organizational Learning
Build institutional knowledge and improve team capabilities through structured reflection.
Stakeholder Confidence
Show customers, regulators, and partners your commitment to responsible AI through transparent processes.
PDCA in Action
See how the SOAI-PDCA framework applies to real AI safety scenarios.
Example: Bias Detection in Hiring AI
PLAN
Identify potential gender bias in resume screening AI. Set objective: Achieve parity in interview callback rates across genders within 90 days.
DO
Implement bias detection algorithms, add fairness constraints to model training, and deploy monitoring dashboard tracking callback rates by demographic.
CHECK
Monitor for 30 days. Discover callback rate gap reduced from 15% to 5%, but new bias emerged in technical role recommendations.
ACT
Standardize successful bias mitigation techniques. Address new technical role bias in next PDCA cycle. Document lessons learned for future AI systems.
