breakpilot-compliance/ai-compliance-sdk/internal/iace/engine.go

package iace

import (
	"fmt"
	"math"
)

// RiskLevel, AssessRiskRequest, and RiskAssessment types are defined in models.go.
// This file only contains calculation methods.

// RiskComputeInput contains the input parameters for the engine's risk computation.
type RiskComputeInput struct {
	Severity         int     `json:"severity"`          // 1-5
	Exposure         int     `json:"exposure"`          // 1-5
	Probability      int     `json:"probability"`       // 1-5
	Avoidance        int     `json:"avoidance"`         // 0=disabled, 1-5 (3=neutral)
	ControlMaturity  int     `json:"control_maturity"`  // 0-4
	ControlCoverage  float64 `json:"control_coverage"`  // 0-1
	TestEvidence     float64 `json:"test_evidence"`     // 0-1
	HasJustification bool    `json:"has_justification"`
}

// RiskComputeResult contains the output of the engine's risk computation.
type RiskComputeResult struct {
	InherentRisk         float64   `json:"inherent_risk"`
	ControlEffectiveness float64   `json:"control_effectiveness"`
	ResidualRisk         float64   `json:"residual_risk"`
	RiskLevel            RiskLevel `json:"risk_level"`
	IsAcceptable         bool      `json:"is_acceptable"`
	AcceptanceReason     string    `json:"acceptance_reason"`
}

// ============================================================================
// RiskEngine
// ============================================================================

// RiskEngine provides methods for mathematical risk calculations
// according to the IACE (Inherent-risk Adjusted Control Effectiveness) model.
type RiskEngine struct{}

// NewRiskEngine creates a new RiskEngine instance.
func NewRiskEngine() *RiskEngine {
	return &RiskEngine{}
}

// ============================================================================
// Calculations
// ============================================================================

// clamp restricts v to the range [lo, hi].
func clamp(v, lo, hi int) int {
	if v < lo {
		return lo
	}
	if v > hi {
		return hi
	}
	return v
}

// clampFloat restricts v to the range [lo, hi].
func clampFloat(v, lo, hi float64) float64 {
	if v < lo {
		return lo
	}
	if v > hi {
		return hi
	}
	return v
}

// CalculateInherentRisk computes the inherent risk score.
//
// Formula:
//   - avoidance == 0: S × E × P  (backward-compatible, no avoidance factor)
//   - avoidance  > 0: S × E × P × (A / 3.0)  (3 = neutral, no influence)
//
// Avoidance scale: 1=leicht vermeidbar, 3=neutral, 5=nicht vermeidbar.
// Each factor is expected in the range 1-5 and will be clamped if out of range.
func (e *RiskEngine) CalculateInherentRisk(severity, exposure, probability, avoidance int) float64 {
	s := clamp(severity, 1, 5)
	ex := clamp(exposure, 1, 5)
	p := clamp(probability, 1, 5)
	base := float64(s) * float64(ex) * float64(p)
	if avoidance <= 0 {
		return base
	}
	a := clamp(avoidance, 1, 5)
	return base * (float64(a) / 3.0)
}

// CalculateControlEffectiveness computes the control effectiveness score.
//
// Formula: C_eff = min(1, 0.2*(maturity/4.0) + 0.5*coverage + 0.3*testEvidence)
//
// Parameters:
//   - maturity:     0-4, clamped if out of range
//   - coverage:     0-1, clamped if out of range
//   - testEvidence: 0-1, clamped if out of range
//
// Returns a value between 0 and 1.
func (e *RiskEngine) CalculateControlEffectiveness(maturity int, coverage, testEvidence float64) float64 {
	m := clamp(maturity, 0, 4)
	cov := clampFloat(coverage, 0, 1)
	te := clampFloat(testEvidence, 0, 1)

	cEff := 0.2*(float64(m)/4.0) + 0.5*cov + 0.3*te
	return math.Min(1, cEff)
}

// CalculateResidualRisk computes the residual risk after applying controls.
//
// Formula: R_residual = S * E * P * (1 - cEff)
//
// Parameters:
//   - severity, exposure, probability: 1-5, clamped if out of range
//   - cEff: control effectiveness, 0-1
func (e *RiskEngine) CalculateResidualRisk(severity, exposure, probability int, cEff float64) float64 {
	inherent := e.CalculateInherentRisk(severity, exposure, probability, 0)
	return inherent * (1 - cEff)
}

// DetermineRiskLevel classifies the residual risk into a RiskLevel category.
//
// Thresholds:
//   - >= 75: critical
//   - >= 40: high
//   - >= 15: medium
//   - >= 5:  low
//   - < 5:   negligible
func (e *RiskEngine) DetermineRiskLevel(residualRisk float64) RiskLevel {
	switch {
	case residualRisk >= 75:
		return RiskLevelCritical
	case residualRisk >= 40:
		return RiskLevelHigh
	case residualRisk >= 15:
		return RiskLevelMedium
	case residualRisk >= 5:
		return RiskLevelLow
	default:
		return RiskLevelNegligible
	}
}

// IsAcceptable determines whether the residual risk is acceptable based on
// the ALARP (As Low As Reasonably Practicable) principle and EU AI Act thresholds.
//
// Decision logic:
//   - residualRisk < 15:  acceptable ("Restrisiko unter Schwellwert")
//   - residualRisk < 40 AND allReductionStepsApplied AND hasJustification:
//     acceptable under ALARP ("ALARP-Prinzip: Restrisiko akzeptabel mit vollstaendiger Risikominderung")
//   - residualRisk >= 40: not acceptable ("Restrisiko zu hoch - blockiert CE-Export")
func (e *RiskEngine) IsAcceptable(residualRisk float64, allReductionStepsApplied bool, hasJustification bool) (bool, string) {
	if residualRisk < 15 {
		return true, "Restrisiko unter Schwellwert"
	}
	if residualRisk < 40 && allReductionStepsApplied && hasJustification {
		return true, "ALARP-Prinzip: Restrisiko akzeptabel mit vollstaendiger Risikominderung"
	}
	return false, "Restrisiko zu hoch - blockiert CE-Export"
}

// CalculateCompletenessScore computes a weighted completeness score (0-100).
//
// Formula:
//
//	score = (passedRequired/totalRequired)*80
//	      + (passedRecommended/totalRecommended)*15
//	      + (passedOptional/totalOptional)*5
//
// If any totalX is 0, that component contributes 0 to the score.
func (e *RiskEngine) CalculateCompletenessScore(passedRequired, totalRequired, passedRecommended, totalRecommended, passedOptional, totalOptional int) float64 {
	var score float64

	if totalRequired > 0 {
		score += (float64(passedRequired) / float64(totalRequired)) * 80
	}
	if totalRecommended > 0 {
		score += (float64(passedRecommended) / float64(totalRecommended)) * 15
	}
	if totalOptional > 0 {
		score += (float64(passedOptional) / float64(totalOptional)) * 5
	}

	return score
}

// ComputeRisk performs a complete risk computation using all calculation methods.
// It returns a RiskComputeResult with inherent risk, control effectiveness, residual risk,
// risk level, and acceptability.
//
// The allReductionStepsApplied parameter for IsAcceptable is set to false;
// the caller is responsible for updating acceptance status after reduction steps are applied.
func (e *RiskEngine) ComputeRisk(req RiskComputeInput) (*RiskComputeResult, error) {
	if req.Severity < 1 || req.Exposure < 1 || req.Probability < 1 {
		return nil, fmt.Errorf("severity, exposure, and probability must be >= 1")
	}

	inherentRisk := e.CalculateInherentRisk(req.Severity, req.Exposure, req.Probability, req.Avoidance)
	controlEff := e.CalculateControlEffectiveness(req.ControlMaturity, req.ControlCoverage, req.TestEvidence)
	residualRisk := e.CalculateResidualRisk(req.Severity, req.Exposure, req.Probability, controlEff)
	riskLevel := e.DetermineRiskLevel(residualRisk)
	acceptable, reason := e.IsAcceptable(residualRisk, false, req.HasJustification)

	return &RiskComputeResult{
		InherentRisk:         inherentRisk,
		ControlEffectiveness: controlEff,
		ResidualRisk:         residualRisk,
		RiskLevel:            riskLevel,
		IsAcceptable:         acceptable,
		AcceptanceReason:     reason,
	}, nil
}