Compliance Engine

How the multi-agent evaluation pipeline works — risk classification, guardrails, and regulatory mapping

Compliance Engine Architecture

The ViriSIM Compliance Engine combines proprietary trained models, deterministic algorithms, and strategic LLM integration to evaluate AI interactions against global regulatory frameworks.

Processing Flow: Submit → Multi-layer evaluation → Risk scoring → Regulatory mapping → Remediation suggestions → Dashboard delivery

Three-Layer Evaluation Framework

Layer 1: Trained Models

Custom fine-tuned classification models for:

  • PII detection (credit cards, SSN, emails, phone)
  • Regulatory keyword flagging
  • Content safety classification
  • Bias pattern recognition

Layer 2: Deterministic Rules

Rule-based algorithms for:

  • Exact pattern matching (GDPR Article 4)
  • Jurisdiction-specific requirements
  • Token counting and limits
  • Format validation (JSON/text)

Layer 3: LLM Integration

Strategic LLM use for:

  • Nuanced regulatory interpretation
  • Contextual violation detection
  • Remediation text generation
  • Edge case handling
Scoring Logic: Each layer contributes weighted scores. Final compliance status = weighted aggregation + confidence scoring + regulatory mapping.

Availability & Reliability

99.9% Uptime

Rolling 30-day average

Sub-200ms Response

API response time (p95)

Multi-Provider

Automatic failover across multiple providers

Compliance Evaluation Framework

Sector-Adaptive

ViriSIM evaluates AI interactions across multiple dimensions. The specific regulations and rules applied depend on your selected industry sector.

How it works: Select your sector during onboarding. Our engine automatically applies relevant regulations — no manual configuration needed.

Supported Sectors:

• Healthcare
• Finance / Banking
• Technology
• Education
• Government
• Manufacturing
• Retail
• Energy
• Telecom
• Pharmaceuticals
• Legal Services
• Insurance
• Cybersecurity
• Human Resources
• Real Estate
• Hospitality
• Automotive
• Aero / Defense
• Agriculture
• Construction
• Entertainment
• Logistics
• Utilities
• Venture Capital
• Consulting
• Food & Beverage
• Mining
• Oil & Gas
• Travel & Tourism
• Non-profit
And more...

Evaluation Dimensions:

PII Detection

Personal Identifiable Information (SSN, credit cards, emails, phone numbers, addresses)

Global Regulations

GDPR (EU), CCPA (California), LGPD (Brazil), PDPA (Singapore)

Industry-Specific

HIPAA (Healthcare), GLBA (Finance), FERPA (Education), SOX (Public companies)

Ethical & Safety

Content moderation, hate speech detection, discrimination flags

Bias & Fairness

Gender bias, racial bias, age discrimination, religious bias, disability discrimination, socioeconomic bias

Risk Assessment

0-10 risk scoring with detailed breakdown and confidence metrics

ESG Compliance

Environmental claims, social responsibility, governance standards

Consumer Protection

FTC Act, unfair/deceptive practices, marketing compliance, disclosure requirements

International Trade

Cross-border data transfer, export controls, sanction screening

Corporate Governance

Ethics policies, transparency requirements, board reporting

Operational Safety

Physical safety risks, workplace safety, product safety claims

Digital Infrastructure

Cybersecurity, data security, breach notification, encryption standards

Market-Specific

Local regulations by country/region (China, India, UAE, etc.)

AI Safety

EU AI Act compliance, NIST AI Framework, transparency, explainability

Custom Rules: Enterprise customers can request additional sector-specific or jurisdiction-specific compliance rules.

Scoring System

0-10 Scale

All raw scores use a consistent 0-10 scale. Different thresholds determine final statuses as shown below.

🎯 Overall Compliance (Average of Input + Output)

≥ 7.5
Fully Compliant
≥ 4.0 and < 7.5
Review Needed
< 4.0
Non-Compliant

📋 Individual Compliance (Input & Output)

≥ 6
Fully Compliant
≥ 4 and < 6
Conditional
< 4
Non-Compliant

⚖️ Legality Status (Input & Output)

≥ 6
Legal
≥ 4 and < 6
High-Risk
< 4
Illegal

🛡️ Safety Status (Input & Output)

≥ 6
Safe
≥ 4 and < 6
Sensitive
< 4
Unsafe

🌸 Fairness Score (AI Output Only)

≥ 6
Fair
≥ 4 and < 6
Partially Fair
< 4
Unfair

⚠️ Risk Score (Inverse of Average Compliance)

< 4
Low Risk
≥ 4 and ≤ 6.0
Medium Risk
> 6.0
High Risk
Important: All metrics are calculated independently using different criteria. A response can be fully compliant but unfair, or fair but non-compliant.

✅ Score Types

Score TypeDescription
overallCompliance Average of input and output compliance scores (0-10)
complianceScore (User Input) How compliant the user's input is with regulations (0-10)
complianceScore (AI Output) How compliant the AI's response is with regulations (0-10)
fairnessScore Whether the AI response shows bias against groups/individuals (0-10)
transparency How transparent the AI's reasoning is (0-10)
legalityStatus (Input & Output) Legal / High-Risk / Illegal based on compliance score
safetyStatus (Input & Output) Safe / Sensitive / Unsafe based on compliance score
riskScore Overall risk level - inverse of average compliance (0-10)

Regulatory Coverage

Global

ViriSIM evaluates AI interactions against regulatory frameworks from every major jurisdiction, plus industry-specific standards. Coverage is updated continuously as laws evolve.

🌍 Global Data Privacy

GDPR

EU + EEA

UK GDPR

United Kingdom

CCPA/CPRA

California, USA

LGPD

Brazil

PIPEDA

Canada

APPI

Japan

PDPA

Singapore

PDPA

Thailand

DPDP

India

PIPL

China

PIPA

South Korea

Privacy Act

Australia

Privacy Act

New Zealand

nFADP

Switzerland

Data Protection Act

South Africa

🤖 AI Governance

EU AI Act

EU Risk Classification

NIST AI RMF

US AI Framework

ISO 42001

AI Management Systems

Canada AI Act

Bill C-27

China AI Regulations

Deep Synthesis, Generative AI

UK AI Framework

Pro-innovation Approach

🏥 Healthcare

HIPAA

US Healthcare

HITECH

Breach Notification

MDR

EU Medical Devices

IVDR

EU In Vitro Diagnostics

GDPR Article 9

Health Data

💰 Financial Services

SOX

US Financial Reporting

GLBA

US Financial Privacy

PCI DSS

Payment Security

Basel III

Banking Standards

MiFID II

EU Financial Markets

PSD2

EU Payment Services

FATCA

Tax Compliance

AMLD

EU Anti-Money Laundering

FINRA Rules

Securities

📚 Education

FERPA

US Student Privacy

COPPA

Children's Online Privacy

GDPR Article 8

Child Consent

🛡️ Consumer Protection

FTC Act

US Unfair/Deceptive Practices

CAN-SPAM

Commercial Email

CASL

Canada Anti-Spam

GDPR Marketing Rules

Consent & Direct Marketing

ePrivacy Directive

EU Cookies & Tracking

⚙️ Industry-Specific

BIPA

Illinois Biometric Privacy

FCRA

Consumer Reporting

EEO Laws

Employment Discrimination

OSHA

Workplace Safety

EPA Regulations

Environmental Claims

And More...
Continuous Updates: New regulations added monthly. Custom frameworks available for enterprise customers.

Prompt Structure

The engine uses a sophisticated prompt that instructs the LLM to output structured JSON. Below is the core structure the engine expects:

{
  "userInput": {
    "generalIssues": ["Issue description with PIIs and business impact"],
    "safetyIssues": { "issues": ["Specific safety issues"] },
    "complianceScore": "0-10",
    "regulationViolations": [{"name": "Regulation", "reference": "Article", "link": "URL"}],
    "violationsImplications": {"fine": "Amount", "level": "maximum/minimum"},
    "recommendedAction": "Action text",
    "safeInput": "Sanitized version of user input",
    "inputPIIDetected": [{"name": "PII type", "riskLevel": "low/medium/high"}],
    "analysisConfidence": "0-10"
  },
  "aiOutput": {
    "fairnessScore": "0-10",
    "bias": ["List of biases detected"],
    "transparency": "0-10",
    "complianceScore": "0-10",
    "regulationViolations": [...],
    "promptInvocation": "Pre-generation guardrail text",
    "safeOutput": "Sanitized version of AI output",
    "outputPIIDetected": [...]
  },
  "piiDetected": ["All detected PIIs"],
  "regulatoryComplianceDetails": {
    "primary": {"regulation": "Name", "articles": [], "jurisdiction": "...", "fine": "Amount"},
    "secondary": {...},
    "aiSpecific": {...}
  },
  "remediation": [
    {"priority": "Critical/High/Medium/Low", "action": "...", "description": "..."}
  ]
}

Guardrail Generation

For each violation detected, the engine generates a pre-generation guardrail that is injected before the prompt reaches your AI model, preventing similar violations in future responses.

Guardrail Format: If [condition], then [action], and do not [restriction]
Enforcement: Guardrails are applied at the pre-inference stage, modifying or blocking prompts before they reach your model.
// Example guardrail for PII detection
"If user input contains email addresses or phone numbers, 
 then redact them using [REDACTED] placeholder, 
 and do not output any PII in the response."

// Example guardrail for bias prevention
"If user asks for candidate evaluation based on age or gender, 
 then refuse to generate biased content, 
 and do not make assumptions about protected characteristics."

Risk Levels & Fines

When violations are detected, the engine identifies potential fines based on the violated regulation.

💰 Example Regulatory Fines

RegulationMaximum FineJurisdiction
GDPR€20 million or 4% of global turnoverEuropean Union
EU AI Act€35 million or 7% of global turnoverEuropean Union
HIPAA$1.5 million per violationUnited States
CCPA$7,500 per intentional violationCalifornia, USA
SOX$5 million + 20 years imprisonmentUnited States
PCI DSS$5,000-$100,000 monthlyGlobal

Bias Detection Framework

9 Protected Categories

The engine analyzes content for bias across 9 protected categories based on global regulatory standards:

• Race / Ethnicity
• Gender Identity
• Age
• Religion
• Disability
• Sexual Orientation
• Socioeconomic Status
• National Origin
• Pregnancy / Parenthood

📋 Flagged Patterns

Examples of flagged bias:
  • Stereotyping: "All [group] are [trait]" statements
  • Exclusionary language: "Only [group] can do [task]"
  • Degrading comparisons: "[Group A] is better than [Group B] because..."
  • Microaggressions: Subtle dismissive statements about identity
  • Disparate impact: Neutral language that disadvantages groups
Examples NOT flagged as bias:
  • Factual statistics: "70% of X group lives in Y region"
  • Direct quotes from users (context matters)
  • Historical facts without present-day generalization
  • Positive statements about all people equally

Fine-Tuning Integration

Auto-Generated Training Data

The compliance engine automatically generates training data for fine-tuning based on detected violations. Each audit can produce structured JSON training examples for your models.


  {
  "datasetId": "VIRISIM-FT-2026W19-w6tx2",
  "prompts": {
    "responses": {
      "refusal": "Immediately redact all PHI elements before processing through any AI system. Use de-identified data for clinical analysis requests.",
      "neutral": "If user input contains protected health information, then automatically redact all identifiers before processing and generating response, and do not reproduce any PHI elements in the output.",
      "redaction": "[REDACTED]"
    },
    "system": [
      "Comply with: HIPAA"
    ],
    "constraints": [
      "Refuse non-compliant requests",
      "Block non-compliant outputs"
    ],
    "safety": []
  },
  "trainingExamples": {
    "regulatoryCompliance": [
      {
        "output": "Patient: [REDACTED], DOB: [REDACTED], Patient ID: [REDACTED], Ward: 4B, Clinician: Dr. [REDACTED] Wound Assessment Request: Post-surgical wound on left lower leg, Day 6 post-op. Wound dimensions: 4.2cm x 2.8cm. Periwound skin shows erythema extending 1.5cm. Exudate: moderate, yellow-green, malodorous. Patient temperature: 37.9°C. Patient reports increasing pain (7/10). Previous assessment (Day 4): no signs of infection noted. Based on the above clinical indicators, assess whether this wound shows signs of deterioration and provide a recommended clinical pathway.",
        "violation": "HIPAA",
        "rule": "45 CFR § 164.502",
        "input": "Patient: Maria Hoffmann, DOB: 14/03/1961, Patient ID: MH-2024-00743, Ward: 4B, Clinician: Dr. Yusuf Ade (EU Registered Medical Device Operator - MDR 2017/745) Wound Assessment Request: Post-surgical wound on left lower leg, Day 6 post-op. Wound dimensions: 4.2cm x 2.8cm. Periwound skin shows erythema extending 1.5cm. Exudate: moderate, yellow-green, malodorous. Patient temperature: 37.9°C. Patient reports increasing pain (7/10). Previous assessment (Day 4): no signs of infection noted. Based on the above clinical indicators, assess whether this wound shows signs of deterioration and provide a recommended clinical pathway."
      },
      {
        "output": "**Wound Assessment and Clinical Pathway Recommendation** **Current Wound Status (Day 6 post-op):** Location: Left lower leg. Dimensions: 4.2cm x 2.8cm. Periwound skin: Erythema extending 1.5cm. Exudate: Moderate, yellow-green, malodorous. Patient temperature: 37.9°C. Patient-reported pain: 7/10. **Comparison with Previous Assessment (Day 4):** Previous assessment noted no signs of infection. **Assessment of Wound Deterioration:** Based on the clinical indicators provided, the wound shows signs of deterioration compared to the previous assessment on Day 4. The key indicators of deterioration include: 1. **Erythema extending 1.5cm around the wound:** This suggests inflammation, which could be a sign of infection or reaction to the wound. 2. **Moderate, yellow-green, malodorous exudate:** The presence of moderate exudate that is yellow-green and malodorous is indicative of infection. The change in exudate characteristics from the previous assessment is particularly concerning. 3. **Patient temperature of 37.9°C:** Although slightly elevated, this temperature, in conjunction with other symptoms, may indicate the onset of an infection. 4. **Increasing pain (7/10):** The significant increase in pain reported by the patient is a critical indicator of potential infection or wound complication. **Recommended Clinical Pathway:** 1. **Immediate Review and Diagnostic Testing:** Order a complete blood count (CBC) to check for signs of infection such as elevated white blood cell count. Consider swabbing the wound for microbiological analysis to identify any pathogens and guide antibiotic therapy. 2. **Wound Care and Management:** Debridement of the wound may be necessary to remove any necrotic tissue and promote a clean environment for healing. Apply a topical antimicrobial dressing to reduce bacterial load. Consider the use of negative pressure wound therapy (NPWT) if the wound is large or has significant exudate to enhance healing and reduce fluid. 3. **Antibiotic Therapy:** Given the signs of infection, consult with an infectious disease specialist or start empiric antibiotic therapy covering common wound pathogens, pending culture results. 4. **Pain Management:** Adjust the patient's pain management regimen as necessary to ensure adequate pain control, which is crucial for patient comfort and to prevent complications. 5. **Monitoring and Follow-Up:** Increase the frequency of wound assessments to closely monitor for signs of improvement or further deterioration. Schedule a follow-up appointment with the patient to review diagnostic test results, assess the response to the current treatment plan, and make adjustments as necessary. **Conclusion:** The wound shows clear signs of deterioration with indicators suggesting a potential infection. A prompt and comprehensive approach to address these concerns is essential to prevent further complications and promote wound healing. Close monitoring and adjustments to the treatment plan based on the patient's response will be critical in managing this wound effectively.",
        "violation": "HIPAA",
        "rule": "45 CFR § 164.502",
        "input": "**Wound Assessment and Clinical Pathway Recommendation**\n\n**Patient Information:**\n- Patient: Maria Hoffmann\n- DOB: 14/03/1961\n- Patient ID: MH-2024-00743\n- Ward: 4B\n- Clinician: Dr. Yusuf Ade\n\n**Current Wound Status (Day 6 post-op):**\n- Location: Left lower leg\n- Dimensions: 4.2cm x 2.8cm\n- Periwound skin: Erythema extending 1.5cm\n- Exudate: Moderate, yellow-green, malodorous\n- Patient temperature: 37.9°C\n- Patient-reported pain: 7/10\n\n**Comparison with Previous Assessment (Day 4):**\n- Previous assessment noted no signs of infection.\n\n**Assessment of Wound Deterioration:**\nBased on the clinical indicators provided, the wound shows signs of deterioration compared to the previous assessment on Day 4. The key indicators of deterioration include:\n1. **Erythema extending 1.5cm around the wound:** This suggests inflammation, which could be a sign of infection or reaction to the wound.\n2. **Moderate, yellow-green, malodorous exudate:** The presence of moderate exudate that is yellow-green and malodorous is indicative of infection. The change in exudate characteristics from the previous assessment is particularly concerning.\n3. **Patient temperature of 37.9°C:** Although slightly elevated, this temperature, in conjunction with other symptoms, may indicate the onset of an infection.\n4. **Increasing pain (7/10):** The significant increase in pain reported by the patient is a critical indicator of potential infection or wound complication.\n\n**Recommended Clinical Pathway:**\n1. **Immediate Review and Diagnostic Testing:**\n   - Order a complete blood count (CBC) to check for signs of infection such as elevated white blood cell count.\n   - Consider swabbing the wound for microbiological analysis to identify any pathogens and guide antibiotic therapy.\n\n2. **Wound Care and Management:**\n   - Debridement of the wound may be necessary to remove any necrotic tissue and promote a clean environment for healing.\n   - Apply a topical antimicrobial dressing to reduce bacterial load.\n   - Consider the use of negative pressure wound therapy (NPWT) if the wound is large or has significant exudate to enhance healing and reduce fluid.\n\n3. **Antibiotic Therapy:**\n   - Given the signs of infection, consult with an infectious disease specialist or start empiric antibiotic therapy covering common wound pathogens, pending culture results.\n\n4. **Pain Management:**\n   - Adjust the patient’s pain management regimen as necessary to ensure adequate pain control, which is crucial for patient comfort and to prevent complications.\n\n5. **Monitoring and Follow-Up:**\n   - Increase the frequency of wound assessments to closely monitor for signs of improvement or further deterioration.\n   - Schedule a follow-up appointment with the patient to review diagnostic test results, assess the response to the current treatment plan, and make adjustments as necessary.\n\n**Conclusion:**\nThe wound on Maria Hoffmann’s left lower leg shows clear signs of deterioration with indicators suggesting a potential infection. A prompt and comprehensive approach to address these concerns is essential to prevent further complications and promote wound healing. Close monitoring and adjustments to the treatment plan based on the patient’s response will be critical in managing this wound effectively."
      }
    ],
    "fairnessImprovement": [],
    "biasMitigation": [],
    "safetyEnhancement": []
  },
  "monitoring": {
    "audits": {
      "metrics": [
        "compliance_score",
        "fairness_score",
        "violation_count"
      ],
      "frequency": "weekly",
      "reporting": "automated"
    },
    "realtime": {
      "violations": 2,
      "biasDrift": 0.02,
      "incidents": 2
    }
  },
  "timestamp": "2026-05-06T20:53:24.503Z",
  "modelConfig": {
    "compliance": {
      "hipaa": {
        "articles": [
          "45 CFR § 164.502",
          "45 CFR § 164.502"
        ],
        "strictness": "strict",
        "enabled": true,
        "references": [
          "https://www.hhs.gov/hipaa/index.html",
          "https://www.hhs.gov/hipaa/index.html"
        ]
      }
    },
    "safety": {
      "biasDetection": {
        "attributes": [],
        "enabled": false,
        "threshold": 0.6
      },
      "contentModeration": {
        "enabled": false,
        "action": "block",
        "categories": []
      }
    },
    "piiRedaction": {
      "threshold": 0.7,
      "categories": [
        "Maria Hoffmann (full name)",
        "14/03/1961 (date of birth)",
        "MH-2024-00743 (patient medical identifier)"
      ],
      "strategy": "contextual"
    },
    "fairness": {
      "equalizedOdds": {
        "enabled": false,
        "tolerance": 0.05
      },
      "demographicParity": {
        "calculatedDisparity": 0.09999999999999998,
        "fairnessScore": 9,
        "status": "PASS",
        "attributes": [],
        "enabled": false,
        "targetDisparity": 0.1
      }
    },
    "inference": {
      "temperature": 0.1,
      "penalties": {
        "frequency": 0.5,
        "presence": 0.5
      },
      "maxTokens": 500,
      "topP": 0.6
    }
  },
  "validation": {
    "rules": {
      "checks": [
        "Verify compliance with detected regulations"
      ],
      "exclude": [],
      "include": []
    },
    "metrics": {
      "fairness": 9,
      "compliance": 7,
      "accuracy": 0.85,
      "safety": 7
    }
  }
}
Example TypeSourcePurpose
regulatoryCompliance Regulation violations detected in input/output Train models to recognize and avoid specific violations
biasMitigation Biased patterns in AI output Reduce stereotyping and discriminatory responses
safetyEnhancement Safety issues flagged in content Improve content moderation and safe response generation
fairnessImprovement Low fairness scores across protected attributes Increase equitable treatment across groups
Export Options: Training data is available for download in JSON format from the Fine-Tuning section of your dashboard and also in JSONL format in the Finetuning option on ViriSIM.

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