Automate POAM generation, intelligent risk scoring, and predictive risk analysis while empowering risk professionals to make informed decisions with AI insights.
AI processes vast amounts of risk data and provides intelligent recommendations, but human risk professionals make all critical risk decisions and approve final risk ratings.
// AI-Powered POAM Generation Engine
class AutomatedPOAMEngine {
constructor() {
this.riskAnalyzer = new RiskAnalyzer();
this.impactCalculator = new ImpactCalculator();
this.remediationPlanner = new RemediationPlanner();
this.humanInterface = new RiskDecisionInterface();
this.mlPredictor = new MLRiskPredictor();
}
async generatePOAM(assessmentGap, clientContext) {
// Step 1: AI analyzes the compliance gap
const riskAnalysis = await this.riskAnalyzer.analyzeGap({
gap: assessmentGap,
evidence: assessmentGap.relatedEvidence,
controlContext: assessmentGap.controlRequirements,
threat landscape: await this.getThreatIntelligence()
});
// Step 2: AI calculates impact using multiple models
const impactAssessment = await this.impactCalculator.calculateImpact({
riskType: riskAnalysis.riskCategory,
assetValue: clientContext.affectedAssets,
businessContext: clientContext.businessImpact,
complianceFrameworks: clientContext.applicableFrameworks
});
// Step 3: AI generates remediation recommendations
const remediationOptions = await this.remediationPlanner.generateOptions({
risk: riskAnalysis,
impact: impactAssessment,
clientCapabilities: clientContext.technicalCapabilities,
budgetConstraints: clientContext.budgetLimits,
timeConstraints: clientContext.timeline
});
// Step 4: AI creates draft POAM with all recommendations
const draftPOAM = {
id: generatePOAMId(),
title: riskAnalysis.riskTitle,
description: riskAnalysis.detailedDescription,
// AI-generated risk assessment
aiRiskAssessment: {
likelihood: riskAnalysis.likelihoodScore,
impact: impactAssessment.impactScore,
overallRisk: this.calculateOverallRisk(riskAnalysis, impactAssessment),
confidence: riskAnalysis.confidence
},
// AI-recommended remediation
remediationPlan: {
recommendedOption: remediationOptions.optimal,
alternativeOptions: remediationOptions.alternatives,
estimatedCost: remediationOptions.optimal.costEstimate,
estimatedTimeline: remediationOptions.optimal.timeline,
requiredResources: remediationOptions.optimal.resources
},
// Supporting evidence and context
evidence: riskAnalysis.supportingEvidence,
affectedSystems: impactAssessment.affectedAssets,
complianceImpact: impactAssessment.frameworkImpact,
// AI insights and predictions
aiInsights: {
similarRisks: await this.findSimilarRisks(riskAnalysis),
trendAnalysis: await this.analyzeTrends(riskAnalysis),
successPrediction: await this.mlPredictor.predictSuccess(remediationOptions.optimal)
}
};
// Step 5: Present to human risk professional for review and approval
const humanDecision = await this.humanInterface.reviewPOAM({
draftPOAM: draftPOAM,
aiRecommendations: {
riskRating: draftPOAM.aiRiskAssessment.overallRisk,
priority: this.calculatePriority(draftPOAM),
approach: remediationOptions.optimal.approach
},
businessContext: clientContext,
alternativeOptions: remediationOptions.alternatives
});
// Step 6: Finalize POAM with human-approved decisions
const finalPOAM = await this.finalizePOAM({
draft: draftPOAM,
humanDecisions: humanDecision,
approvedBy: humanDecision.userId,
approvalDate: new Date(),
finalRiskRating: humanDecision.approvedRiskRating,
selectedRemediation: humanDecision.selectedRemediationPlan,
priority: humanDecision.approvedPriority
});
return finalPOAM;
}
async monitorPOAMProgress(poamId) {
// AI continuously monitors POAM implementation progress
const progress = await this.trackProgress(poamId);
const risks = await this.assessProgressRisks(progress);
// Alert humans only when intervention is needed
if (risks.requiresAttention) {
await this.alertRiskManager({
poam: poamId,
issue: risks.identifiedIssues,
recommendations: risks.suggestedActions
});
}
return progress;
}
}
| Likelihood | Low Impact | Medium Impact | High Impact | Critical Impact |
|---|---|---|---|---|
| Very High | Medium | High | Critical | Critical |
| High | Low | Medium | High | Critical |
| Medium | Low | Low | Medium | High |
| Low | Low | Low | Low | Medium |
// ML Risk Scoring Model
class MLRiskScorer {
async calculateRiskScore(riskData) {
const factors = {
// Technical factors (AI-analyzed)
vulnerabilityScore: await this.analyzeVulnerabilities(riskData.technical),
exposureLevel: await this.calculateExposure(riskData.network),
assetCriticality: await this.assessAssetValue(riskData.assets),
// Business factors (AI-assessed)
businessImpact: await this.calculateBusinessImpact(riskData.business),
complianceImpact: await this.assessComplianceRisk(riskData.frameworks),
reputationRisk: await this.evaluateReputationImpact(riskData.context),
// Threat factors (AI-monitored)
threatLevel: await this.getCurrentThreatLevel(riskData.threatType),
attackTrends: await this.analyzeThreatTrends(riskData.industry),
exploitability: await this.assessExploitability(riskData.vulnerabilities)
};
// AI generates initial risk score
const aiScore = await this.mlModel.predict({
factors: factors,
historicalData: await this.getHistoricalRisks(),
industryBenchmarks: await this.getIndustryData()
});
return {
aiRecommendedScore: aiScore.score,
confidence: aiScore.confidence,
factors: factors,
reasoning: aiScore.explanation,
similarRisks: aiScore.comparableRisks
};
}
}
// Human Risk Decision Interface
class RiskDecisionInterface {
async presentRiskAssessment(aiAssessment, contextData) {
const humanDecision = await this.displayDecisionInterface({
aiRecommendation: {
score: aiAssessment.aiRecommendedScore,
level: this.scoreToLevel(aiAssessment.aiRecommendedScore),
confidence: aiAssessment.confidence,
reasoning: aiAssessment.reasoning
},
supportingData: {
technicalFactors: aiAssessment.factors.technical,
businessContext: contextData.businessImpact,
complianceImplications: contextData.complianceRisk,
industryComparison: aiAssessment.similarRisks
},
options: [
{ action: "Accept AI recommendation", riskLevel: aiAssessment.level },
{ action: "Override with custom rating", allowCustom: true },
{ action: "Request additional analysis", deferDecision: true }
],
requiredFields: {
finalRiskRating: "required",
businessJustification: "required",
riskTolerance: "required",
approvalLevel: "required"
}
});
return {
approvedRiskRating: humanDecision.finalRating,
rationale: humanDecision.justification,
riskTolerance: humanDecision.tolerance,
approvedBy: humanDecision.userId,
overrideReason: humanDecision.overrideReason || null
};
}
}
AI analyzes risk patterns to predict future threats and compliance drift.
AI monitors indicators to provide early warnings of potential risk events.
AI predicts likelihood of successful risk remediation based on various factors.