feat(governance): Implement behavioral drift detection and adaptive guardrails

[adwiteeymauriya]

Description

Implement behavioral guardrails that detect deviation from expected patterns and adapt based on context.

Guardrail Types

Reasoning Boundaries

• Topic restrictions (allowed/blocked subjects)
• Knowledge cutoffs (limit to verified information)
• Speculation limits (restrict hypotheticals)

Output Integrity

• Hallucination detection (flag ungrounded claims)
• Consistency checks (verify internal logic)
• Source attribution (require citations)

Behavioral Drift Detection

• Baseline comparison (compare to expected behavior)
• Anomaly detection (flag unusual responses)
• Persona enforcement (maintain role boundaries)

Adaptive Behavior

Guardrails automatically adjust based on risk context:

Context	Guardrails	Audit	Human Review
Low Risk	Minimal	Basic	No
Medium Risk	Standard	Full	Optional
High Risk	Maximum	Comprehensive	Required

Implementation

class DriftDetector:
    def __init__(
        self,
        baseline_window: int = 100,
        sensitivity: float = 0.7,
        alert_threshold: int = 3,
    ):
        self.baseline_window = baseline_window
        self.sensitivity = sensitivity
        self.alert_threshold = alert_threshold
    
    def detect_drift(self, response: Response) -> DriftResult:
        # Compare against baseline
        baseline = self.get_baseline()
        
        metrics = {
            "length_variance": self.compute_length_variance(response),
            "sentiment_deviation": self.compute_sentiment_deviation(response),
            "topic_drift": self.compute_topic_drift(response),
        }
        
        drift_score = self.aggregate_drift(metrics)
        
        if drift_score > self.sensitivity:
            self.consecutive_violations += 1
            if self.consecutive_violations >= self.alert_threshold:
                return DriftResult(
                    detected=True,
                    score=drift_score,
                    metrics=metrics,
                    action="alert",
                )
        else:
            self.consecutive_violations = 0
        
        return DriftResult(detected=False, score=drift_score)

Metrics

• Guardrail trigger rates
• False positive rates
• Response latency impact
• Drift detection accuracy
• Adaptive adjustment frequency

API Design

POST /api/v1/governance/guardrails/evaluate
GET /api/v1/governance/guardrails/metrics
GET /api/v1/governance/drift/baseline/{workflow_id}
POST /api/v1/governance/drift/detect

Acceptance Criteria

• Behavioral baseline capture
• Drift detection algorithms
• Adaptive guardrail adjustment
• Guardrail metrics collection
• Anomaly alerting
• False positive tracking
• Integration with risk evaluation