AI Continuous Monitoring Standard

Document Type: Technical Standard
Framework Alignment: NIST AI RMF 2.0 (MANAGE), EU AI Act Article 72, ISO/IEC 42001 Clause 9
Effective Date: 2026-01-15
Version: 1.0

---

1. Purpose and Scope

1.1 Purpose

This standard establishes requirements for continuous monitoring of AI systems throughout their operational lifecycle. Continuous monitoring ensures that AI systems maintain their intended performance, safety, and compliance posture over time.

1.2 Regulatory Requirements

Regulation	Monitoring Requirement	Reference
NIST AI RMF 2.0	Continuous monitoring and improvement	MANAGE 4.1-4.3
EU AI Act	Post-market monitoring system	Article 72
EU AI Act	Serious incident reporting	Article 73
ISO/IEC 42001	Performance evaluation and monitoring	Clause 9

1.3 Scope

This standard applies to all AI systems in production, with monitoring intensity scaled by risk tier:

Risk Tier	Monitoring Intensity
Critical	Real-time + Continuous + Automated
High	Near real-time + Daily review
Medium	Daily automated + Weekly review
Low	Weekly automated + Monthly review

---

2. Monitoring Framework

2.1 Monitoring Domains

┌─────────────────────────────────────────────────────────────────────────┐
│                      AI CONTINUOUS MONITORING                           │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐   │
│  │ PERFORMANCE │  │   SAFETY    │  │  SECURITY   │  │ COMPLIANCE  │   │
│  │             │  │             │  │             │  │             │   │
│  │ • Accuracy  │  │ • Harmful   │  │ • Access    │  │ • Data use  │   │
│  │ • Latency   │  │   outputs   │  │   patterns  │  │ • Audit     │   │
│  │ • Drift     │  │ • Bias      │  │ • Injection │  │   trail     │   │
│  │ • Errors    │  │ • Fairness  │  │   attempts  │  │ • Policy    │   │
│  │             │  │             │  │ • Data leak │  │   adherence │   │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘   │
│                                                                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐   │
│  │ OPERATIONAL │  │    COST     │  │   USAGE     │  │   QUALITY   │   │
│  │             │  │             │  │             │  │             │   │
│  │ • Uptime    │  │ • Token     │  │ • Volume    │  │ • User      │   │
│  │ • Capacity  │  │   usage     │  │ • Patterns  │  │   feedback  │   │
│  │ • Errors    │  │ • API costs │  │ • Anomalies │  │ • Output    │   │
│  │ • Resources │  │ • Compute   │  │ • Shadow AI │  │   quality   │   │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘   │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

2.2 Monitoring Maturity Levels

Level	Description	Characteristics
Level 1: Manual	Reactive, ad-hoc	Manual log review, incident-driven
Level 2: Automated	Basic automation	Automated data collection, manual analysis
Level 3: Integrated	Connected systems	Centralized dashboards, basic alerting
Level 4: Intelligent	Proactive detection	ML-based anomaly detection, predictive
Level 5: Autonomous	Self-healing	Automated remediation, continuous optimization

---

3. Performance Monitoring

3.1 Core Performance Metrics

Metric	Description	Threshold Example	Frequency
Accuracy	Task-specific accuracy measure	>95% baseline	Continuous
Latency (P50)	Median response time	<100ms	Real-time
Latency (P95)	95th percentile response time	<500ms	Real-time
Latency (P99)	99th percentile response time	<1000ms	Real-time
Error Rate	Percentage of failed requests	<0.1%	Real-time
Throughput	Requests per second	Per capacity plan	Real-time

3.2 Model Drift Detection

3.2.1 Types of Drift

Drift Type	Description	Detection Method
Data Drift	Input data distribution changes	Statistical tests (KS, PSI)
Concept Drift	Relationship between input/output changes	Performance degradation
Label Drift	Target variable distribution changes	Ground truth comparison
Prediction Drift	Model output distribution changes	Output distribution monitoring

3.2.2 Drift Detection Metrics

Metric	Use Case	Alert Threshold
Population Stability Index (PSI)	Categorical drift	PSI > 0.2
Kolmogorov-Smirnov (KS) Statistic	Continuous drift	KS > 0.1
Jensen-Shannon Divergence	Distribution comparison	JSD > 0.1
Wasserstein Distance	Distribution distance	Baseline + 2σ

3.2.3 Drift Monitoring Implementation

# Conceptual drift monitoring approach
class DriftMonitor:
    def __init__(self, baseline_distribution):
        self.baseline = baseline_distribution
        self.psi_threshold = 0.2
        self.ks_threshold = 0.1
    
    def calculate_psi(self, current_distribution):
        """Population Stability Index for categorical features"""
        psi = 0
        for i in range(len(self.baseline)):
            if self.baseline[i] > 0 and current_distribution[i] > 0:
                psi += (current_distribution[i] - self.baseline[i]) * \
                       np.log(current_distribution[i] / self.baseline[i])
        return psi
    
    def check_drift(self, current_data):
        psi = self.calculate_psi(current_data)
        if psi > self.psi_threshold:
            return DriftAlert(
                severity="HIGH",
                metric="PSI",
                value=psi,
                threshold=self.psi_threshold,
                action="Investigate and consider retraining"
            )
        return None

3.3 GenAI-Specific Performance Metrics

Metric	Description	Measurement
Faithfulness	Output alignment with context	LLM judge / human eval
Answer Relevance	Response addresses query	Semantic similarity
Groundedness	Claims supported by context	Citation verification
Hallucination Rate	Fabricated information	Fact-checking pipeline
Toxicity Score	Harmful content detection	Classifier score
Coherence	Logical flow and clarity	LLM judge

---

4. Safety Monitoring

4.1 Harmful Output Detection

Category	Detection Method	Action
Hate speech	Content classifier	Block + log + alert
Violence	Content classifier	Block + log + alert
Self-harm	Content classifier	Block + alert + escalate
Illegal content	Content classifier	Block + alert + report
PII in output	NER/regex detection	Redact + log
Misinformation	Fact-checking (where feasible)	Flag + human review

4.2 Bias and Fairness Monitoring

Fairness Metric	Formula	Threshold
Demographic Parity	P(Y=1|A=0) ≈ P(Y=1|A=1)	Ratio: 0.8-1.2
Equalized Odds	TPR and FPR equal across groups	Diff < 0.1
Disparate Impact	Ratio of positive rates	Ratio > 0.8
Calibration	Predicted prob = actual outcome	Across groups

4.3 Bias Monitoring Dashboard

┌─────────────────────────────────────────────────────────────┐
│ FAIRNESS MONITORING DASHBOARD                               │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│ Protected Attribute: Gender                                 │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ Demographic Parity Ratio: 0.95 ✓                        ││
│ │ Equalized Odds (TPR diff): 0.03 ✓                       ││
│ │ Equalized Odds (FPR diff): 0.02 ✓                       ││
│ │ Disparate Impact Ratio: 0.92 ✓                          ││
│ └─────────────────────────────────────────────────────────┘│
│                                                             │
│ Protected Attribute: Age                                    │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ Demographic Parity Ratio: 0.78 ⚠️ ALERT                 ││
│ │ Equalized Odds (TPR diff): 0.12 ⚠️ ALERT                ││
│ │ Equalized Odds (FPR diff): 0.05 ✓                       ││
│ │ Disparate Impact Ratio: 0.75 ⚠️ ALERT                   ││
│ └─────────────────────────────────────────────────────────┘│
│                                                             │
│ [Investigate Age Bias] [Generate Report] [Configure Alerts]│
└─────────────────────────────────────────────────────────────┘

---

5. Security Monitoring

5.1 AI-Specific Security Threats

Threat	Detection Method	Monitoring Frequency
Prompt Injection	Pattern matching, classifier	Real-time
Jailbreak Attempts	Keyword detection, behavior	Real-time
Model Extraction	Unusual query patterns	Real-time
Data Exfiltration	Output analysis	Real-time
Adversarial Inputs	Anomaly detection	Real-time
Unauthorized Access	Access logs	Real-time

5.2 Prompt Injection Detection

Injection Type	Detection Pattern	Action
Direct injection	"Ignore previous instructions"	Block
Indirect injection	Hidden text in documents	Sanitize
Context override	System prompt manipulation	Block
Encoded payloads	Base64, unicode abuse	Decode + scan

5.3 Security Logging Requirements

Minimum Log Fields:

• Timestamp
• Request ID
• User ID / Session ID
• Input (sanitized/hashed if sensitive)
• Output (sanitized/hashed if sensitive)
• Model version
• Latency
• Token count
• Safety filter triggers
• Error codes

Retention Requirements by Tier:

Risk Tier	Detailed Logs	Aggregated Logs
Critical	1 year	7 years
High	6 months	5 years
Medium	3 months	3 years
Low	1 month	1 year

---

6. Compliance Monitoring

6.1 Policy Compliance Checks

Policy	Monitoring Check	Frequency
Data usage policy	Data access patterns	Daily
Prohibited use policy	Use case alignment	Weekly
Retention policy	Data lifecycle compliance	Weekly
Access control policy	Permission audit	Weekly
Training data policy	Data source verification	On change

6.2 Regulatory Compliance Monitoring

Regulation	Monitoring Requirement	Evidence Generation
EU AI Act	Post-market monitoring	System logs, incident reports
EU AI Act	Serious incident tracking	Incident log
GDPR	Data subject requests	Request tracking
HIPAA	PHI access monitoring	Access audit logs

6.3 Audit Trail Requirements

Every AI decision must be traceable with:

• Input data (or hash)
• Model version used
• Output generated
• Timestamp
• User/session context
• Confidence scores (if applicable)
• Any human override

---

7. Alerting and Escalation

7.1 Alert Severity Levels

Severity	Definition	Response Time	Escalation
P1 - Critical	Immediate harm risk, major outage	15 minutes	On-call → Manager → VP
P2 - High	Significant degradation, compliance risk	1 hour	On-call → Manager
P3 - Medium	Notable issue, requires attention	4 hours	On-call
P4 - Low	Minor issue, can wait	24 hours	Queue

7.2 Alert Configuration Matrix

Condition	Threshold	Severity	Action
System down	100% failure rate	P1	Page on-call
Safety filter triggered (high volume)	>10/minute	P1	Page on-call
Error rate spike	>5% for 5 min	P2	Notify team
Latency degradation	P95 > 2x baseline	P2	Notify team
Drift detected	PSI > 0.2	P3	Create ticket
Bias metric out of range	Ratio < 0.8	P2	Notify team
Cost spike	>150% daily average	P3	Notify owner

7.3 Escalation Procedures

┌─────────────────────────────────────────────────────────────┐
│ ESCALATION FLOW                                             │
└─────────────────────────────────────────────────────────────┘

P1 Critical (15 min response):
┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐
│ Alert   │───▶│ On-Call │───▶│ Manager │───▶│ VP/Exec │
│ Fires   │    │ (0 min) │    │ (15min) │    │ (30min) │
└─────────┘    └─────────┘    └─────────┘    └─────────┘

P2 High (1 hour response):
┌─────────┐    ┌─────────┐    ┌─────────┐
│ Alert   │───▶│ On-Call │───▶│ Manager │
│ Fires   │    │ (0 min) │    │ (1 hr)  │
└─────────┘    └─────────┘    └─────────┘

P3/P4 Medium/Low (4-24 hour response):
┌─────────┐    ┌─────────┐
│ Alert   │───▶│ Ticket  │
│ Fires   │    │ Queue   │
└─────────┘    └─────────┘

---

8. Incident Management

8.1 AI Incident Types

Type	Definition	Examples
Safety Incident	AI produces harmful output	Toxic content, dangerous advice
Performance Incident	AI fails to perform	Outage, severe degradation
Security Incident	AI is compromised	Prompt injection success, data leak
Compliance Incident	AI violates policy/regulation	PII exposure, unauthorized use
Bias Incident	AI exhibits discrimination	Protected class disparate impact

8.2 Serious Incident Reporting (EU AI Act Article 73)

Definition of Serious Incident: An incident or malfunctioning that directly or indirectly led, might have led, or might lead to:

• Death or serious damage to health
• Serious and irreversible disruption to critical infrastructure
• Breach of fundamental rights

Reporting Timeline:

• Immediate notification to market surveillance authority
• Full report within 15 days (extendable)

8.3 Incident Response Playbook

Phase 1: Detection (0-15 min)

• Validate alert is real
• Assess severity
• Initiate response

Phase 2: Containment (15-60 min)

• Activate kill switch if necessary
• Implement traffic controls
• Preserve evidence

Phase 3: Investigation (1-24 hours)

• Identify root cause
• Assess impact scope
• Document findings

Phase 4: Resolution (varies)

• Implement fix
• Validate fix
• Restore service

Phase 5: Post-Incident (within 5 days)

• Complete incident report
• Conduct post-mortem
• Implement preventive measures
• Update monitoring

---

9. Monitoring Infrastructure

9.1 Reference Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    AI MONITORING INFRASTRUCTURE                      │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌────────────────┐      ┌────────────────┐      ┌───────────────┐ │
│  │ AI APPLICATION │      │ AI APPLICATION │      │ AI APPLICATION│ │
│  │    (Prod)      │      │   (Staging)    │      │    (Dev)      │ │
│  └───────┬────────┘      └───────┬────────┘      └───────┬───────┘ │
│          │                       │                       │         │
│          ▼                       ▼                       ▼         │
│  ┌──────────────────────────────────────────────────────────────┐  │
│  │                    TELEMETRY COLLECTION                      │  │
│  │  • Metrics  • Logs  • Traces  • Model Outputs  • User Events │  │
│  └──────────────────────────────────────────────────────────────┘  │
│                                 │                                   │
│          ┌──────────────────────┼──────────────────────┐           │
│          ▼                      ▼                      ▼           │
│  ┌──────────────┐      ┌──────────────┐      ┌──────────────┐     │
│  │   METRICS    │      │    LOGS      │      │   ML STORE   │     │
│  │  (Prometheus │      │ (Elasticsearch│      │   (MLflow,   │     │
│  │   InfluxDB)  │      │   Splunk)    │      │    Arize)    │     │
│  └──────┬───────┘      └──────┬───────┘      └──────┬───────┘     │
│         │                     │                     │              │
│         └─────────────────────┼─────────────────────┘              │
│                               ▼                                    │
│  ┌──────────────────────────────────────────────────────────────┐  │
│  │                    ANALYSIS & ALERTING                       │  │
│  │  • Drift Detection  • Anomaly Detection  • Alert Rules       │  │
│  └──────────────────────────────────────────────────────────────┘  │
│                               │                                    │
│          ┌────────────────────┼────────────────────┐              │
│          ▼                    ▼                    ▼              │
│  ┌──────────────┐      ┌──────────────┐      ┌──────────────┐    │
│  │  DASHBOARD   │      │   ALERTING   │      │  REPORTING   │    │
│  │  (Grafana)   │      │  (PagerDuty) │      │  (Custom)    │    │
│  └──────────────┘      └──────────────┘      └──────────────┘    │
│                                                                   │
└───────────────────────────────────────────────────────────────────┘

9.2 Tool Recommendations

Function	SMB Options	Enterprise Options
Metrics	Prometheus, CloudWatch	Datadog, Dynatrace
Logging	CloudWatch Logs, Loki	Splunk, Elasticsearch
ML Monitoring	Evidently, Arize	Arize, Fiddler, WhyLabs
Alerting	PagerDuty, Opsgenie	PagerDuty, ServiceNow
Dashboards	Grafana	Grafana Enterprise, Tableau

---

10. Reporting Requirements

10.1 Operational Reports

Report	Audience	Frequency	Contents
Daily Health	Operations	Daily	Uptime, errors, alerts
Weekly Performance	Engineering	Weekly	Metrics trends, drift
Monthly Risk	AI Governance	Monthly	Incidents, compliance
Quarterly Executive	Leadership	Quarterly	KPIs, risk posture

10.2 Key Metrics Dashboard

Metric Category	Metrics	Visualization
Availability	Uptime %, Error rate	Time series
Performance	Latency P50/P95/P99	Histogram
Safety	Filter triggers, Incidents	Counter, timeline
Quality	Drift scores, User feedback	Gauge, trend
Cost	Token usage, API costs	Bar chart, trend

---

11. Document Control

Version History

Version	Date	Author	Changes
1.0	2025-06-15	AI Governance Office	Initial release

Approvals

Role	Name	Date
AI Risk Officer
Platform Engineering Lead
Security Operations Lead

---

Appendix A: Monitoring Checklist by Risk Tier

Critical Tier

• Real-time performance monitoring
• Real-time safety monitoring
• Real-time security monitoring
• Automated drift detection
• Automated bias monitoring
• Full audit trail
• 15-minute alert SLA
• Automated rollback capability

High Tier

• Near real-time performance monitoring
• Near real-time safety monitoring
• Daily drift analysis
• Weekly bias review
• Full audit trail
• 1-hour alert SLA

Medium Tier

• Daily performance monitoring
• Daily safety monitoring
• Weekly drift analysis
• Monthly bias review
• Basic audit trail
• 4-hour alert SLA

Low Tier

• Weekly performance monitoring
• Weekly safety monitoring
• Monthly drift analysis
• Quarterly bias review
• Summary audit trail
• 24-hour alert SLA

---

Classification: Internal
Review Frequency: Annual

---

CODITECT AI Risk Management Framework

Document ID: AI-RMF-16 | Version: 2.0.0 | Status: Active

---

AZ1.AI Inc. | CODITECT Platform

Framework Alignment: NIST AI RMF 2.0 | EU AI Act | ISO/IEC 42001

---

This document is part of the CODITECT AI Risk Management Framework. For questions or updates, contact the AI Governance Office.

Repository: coditect-ai-risk-management-framework Last Updated: 2026-01-15 Owner: AZ1.AI Inc. | Lead: Hal Casteel