EP029
Implement asynchronous, risk-tiered logging architectures to satisfy audit and security monitoring obligations without degrading production system performance.
CyberTRIZ analysis · Process contradiction EP029 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Higher Data Granularity for Process Mining vs. System Performance Impact
Business Context. Capturing highly granular event data for process mining provides richer analytical detail, but instrumenting production systems to log every granular event can degrade the performance of the very systems being monitored.
Process TRIZ Resolution. Rather than logging maximum granularity from every system continuously, organizations should capture granular data through asynchronous logging that does not block the main transaction path, or sample granular detail periodically rather than continuously for lower-priority systems.
Applicable TRIZ Principles
Principle 17 (Another Dimension) captures granular logging asynchronously in parallel with the main transaction path.
Principle 3 (Local Quality) applies full granularity only to systems where the analytical value justifies the performance cost.
Principle 19 (Periodic Action) samples granular detail periodically rather than logging it continuously for lower-priority systems.
Expected Outcome
Rich granular data for process mining
Preserved production system performance
Reduced instrumentation overhead
Sustainable long-term monitoring
Decision Indicators
Production system performance has degraded since granular logging was introduced.
Logging occurs synchronously, blocking the main transaction path.
The same logging granularity is applied to every system regardless of priority.
System teams have disabled granular logging due to performance impact.
No sampling strategy exists to reduce logging overhead for lower-priority systems.
If several of these indicators are present, the contradiction is likely active and the Process TRIZ resolution above should be evaluated.