11h • General discussion
📕Lesson 3: Drift Detection & Continuous Risk Scoring
Most AI systems don’t fail instantly.
They drift.
Data distribution changes
User behavior shifts
Model outputs subtly degrade
Confidence remains high while correctness falls
Governance is not a one-time gate.
It is continuous state evaluation.
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Core Idea
We introduce:
1. Baseline Behavior Profile
2. Rolling Statistical Window
3. Risk Score Function
4. Auto Escalation Threshold
5. Audit Trail Update
This moves governance from static enforcement → adaptive monitoring.
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The Architecture
Input
→ Model Output
→ Metric Extraction
→ Drift Engine
→ Risk Score
→ Escalate / Allow / Degrade
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What We Measure
For this lesson we’ll track:
Output Length Variance
Confidence Drift
Rejection Rate
Retry Frequency
Response Time Shift
These are model-agnostic signals.
No secret sauce. Just good engineering.
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Conceptual Metric Design
We define:
Baseline Mean = μ
Baseline Std Dev = σ
For live system:
Z = |Current − μ| / σ
If Z > threshold → drift warning
If cumulative drift > global threshold → escalate
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Risk Function
Let:
Drift Score = weighted sum of Z-scores
Risk Level = f(drift_score, retry_rate, error_rate)
Then:
if risk > 0.8 → hard block
if 0.5 < risk ≤ 0.8 → human review
else → allow
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Why This Is Powerful
This is still downstream governance.
But now:
You are not reacting to catastrophe.
You are detecting instability early.
You are compressing causality.
Drift is entropy. Governance is entropy resistance.
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What This Teaches Engineers
1. Governance is measurable.
2. Drift is statistical, not emotional.
3. Risk must be continuous, not periodic.
4. Escalation must be automated.
5. Audit logs must evolve with state.
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📕Lesson 3: Drift Detection & Continuous Risk Scoring
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