AI Accuracy, Robustness and Security Standard

Document Type: Standard
Standard ID: STD-AI-008
Standard Title: AI Accuracy, Robustness and Security Standard
Version: 1.0
Effective Date: 2025-08-01
Next Review Date: 2026-08-01
Review Frequency: Annually or upon regulatory change
Parent Policy: POL-AI-001 - Artificial Intelligence Policy
Owner: Chief Technology Officer
Approved By: AI Governance Committee Chair
Status: Draft
Classification: Internal Use Only

---

TABLE OF CONTENTS

1. Document History
2. Objective
3. Scope and Applicability
4. Control Standard
5. Supporting Procedures
6. Compliance
7. Roles and Responsibilities
8. Exceptions
9. Enforcement
10. Key Performance Indicators (KPIs)
11. Training Requirements
12. Definitions
13. Link with AI Act and ISO42001

---

DOCUMENT HISTORY

Version	Date	Author	Changes	Approval Date	Approved By
0.1	2025-07-05	David Lee, Chief Technology Officer	Initial draft	-	-
0.2	2025-07-20	David Lee, Chief Technology Officer	Added security details	-	-
0.3	2025-08-01	David Lee, Chief Technology Officer	Incorporated security feedback	-	-
1.0	2025-08-01	David Lee, Chief Technology Officer	Final version approved - GRC restructured	2025-07-25	Jane Doe, AI Governance Committee Chair

---

OBJECTIVE

This standard defines requirements for ensuring AI systems achieve appropriate levels of accuracy, robustness, and cybersecurity in compliance with EU AI Act Article 15.

Primary Goals:

• Ensure AI systems achieve appropriate accuracy levels per Article 15(1)
• Ensure AI systems are robust per Article 15(4)
• Ensure AI systems are resilient against cybersecurity threats per Article 15(5)

---

SCOPE AND APPLICABILITY

2.1 Mandatory Applicability

This standard is mandatory for:

• All high-risk AI systems (EU AI Act Article 15)
• All AI systems with significant impact on health, safety, or fundamental rights

2.2 Recommended Applicability

This standard is recommended for:

• All AI systems for best practices
• Limited-risk and minimal-risk AI systems (voluntary)

2.3 Requirements Covered

• Accuracy requirements, testing, and monitoring
• Robustness requirements, testing, and drift detection
• Cybersecurity requirements, threat assessment, and security testing

2.4 Out of Scope

• General software quality (covered by software development standards)
• Non-AI system security (covered by IT security standards)
• Requirements outside EU AI Act scope

---

CONTROL STANDARD

Control ARS-001: Accuracy Requirements Definition

Control ID: ARS-001
Control Name: Accuracy Requirements and Metrics Definition
Control Type: Preventive
Control Frequency: Per high-risk AI system, during design phase
Risk Level: High

Control Objective

Define accuracy requirements based on intended purpose per Article 15(1) to ensure AI systems achieve appropriate accuracy levels for their use case, enabling safe and effective deployment.

Control Requirements

CR-001.1: Accuracy Requirements Documentation

Define and document accuracy requirements with appropriate metrics and thresholds.

Accuracy Metrics:

Metric	Definition	Use Case	Target (High-Risk)
Overall Accuracy	% of correct predictions	General classification	≥95%
Precision	% of positive predictions that are correct	When false positives costly	≥95%
Recall	% of actual positives correctly identified	When false negatives costly	≥95%
F1 Score	Harmonic mean of precision and recall	Balanced metric	≥95%
ROC-AUC	Area under ROC curve	Binary classification	≥0.95
Domain-Specific	Custom metrics for specific domain	Domain-specific use cases	Defined per use case

Mandatory Actions:

• Define accuracy requirements based on intended purpose
• Select appropriate metrics
• Set accuracy thresholds
• Document requirements
• Obtain AI System Owner approval
• Review and update annually

Accuracy Requirements by Use Case:

Use Case	Accuracy Metric	Threshold	Justification
Medical Diagnosis	Precision, Recall	≥98%	High safety impact
Credit Scoring	ROC-AUC, Precision	≥0.95, ≥95%	Financial impact
Fraud Detection	Precision, Recall	≥95%, ≥90%	Balance false positives/negatives
Image Classification	Overall Accuracy	≥95%	General use case

Evidence Required:

• Accuracy Requirements Document (DOC-AI-ARS-001)
• Metrics definitions
• Threshold specifications
• Approval records
• Annual review records

Audit Verification:

• Verify accuracy requirements defined for all high-risk AI
• Confirm metrics appropriate for use case
• Check thresholds set and justified
• Validate approval obtained
• Verify annual review completed

---

Control ARS-002: Accuracy Testing

Control ID: ARS-002
Control Name: Accuracy Testing and Validation
Control Type: Preventive
Control Frequency: Before deployment, after model updates
Risk Level: High

Control Objective

Test AI system accuracy before deployment to verify it meets defined accuracy requirements, ensuring safe and effective operation.

Control Requirements

CR-002.1: Comprehensive Accuracy Testing

Conduct comprehensive accuracy testing using representative test data.

Testing Requirements:

Requirement	Specification	Implementation
Test Data	Representative of production data	Test dataset validation
Use Case Coverage	Test across all use cases	Use case test matrix
User Group Coverage	Test for all user groups	User group test matrix
Metric Calculation	Calculate all defined metrics	Automated metric calculation
Threshold Verification	Verify all thresholds met	Threshold compliance check
Documentation	Document all test results	Test report

Mandatory Actions:

• Plan accuracy testing
• Prepare representative test data
• Execute tests across all use cases
• Test for all user groups
• Calculate all defined metrics
• Verify threshold compliance
• Document test results
• Block deployment if thresholds not met

Accuracy Test Plan:

Test Component	Description	Success Criteria
Overall Accuracy Test	Test overall accuracy	Meets threshold
Precision Test	Test precision	Meets threshold
Recall Test	Test recall	Meets threshold
Use Case Tests	Test each use case	All use cases meet thresholds
User Group Tests	Test each user group	All groups meet thresholds
Edge Case Tests	Test edge cases	Acceptable performance

Evidence Required:

• Accuracy Test Plan (PLAN-AI-ARS-001)
• Test results (TEST-AI-ARS-001)
• Analysis reports
• Threshold compliance verification
• Approval records

Audit Verification:

• Verify accuracy testing conducted before deployment
• Confirm representative test data used
• Check all use cases tested
• Validate all user groups tested
• Verify thresholds met
• Check deployment blocked if thresholds not met

---

Control ARS-003: Accuracy Monitoring

Control ID: ARS-003
Control Name: Production Accuracy Monitoring
Control Type: Detective
Control Frequency: Continuous
Risk Level: Medium

Control Objective

Monitor accuracy in production to detect accuracy degradation, identify issues early, and enable timely corrective actions to maintain AI system performance.

Control Requirements

CR-003.1: Continuous Accuracy Monitoring

Implement continuous accuracy monitoring with alerting and analysis.

Monitoring Requirements:

Requirement	Specification	Implementation
Real-Time Monitoring	Monitor accuracy metrics in real-time	Real-time monitoring system
Threshold Alerting	Alert when accuracy drops below threshold	Automated alerting
Trend Analysis	Analyze accuracy trends	Trend analysis tools
Issue Investigation	Investigate accuracy issues promptly	Investigation procedures
Corrective Actions	Implement corrective actions	Corrective action procedures

Mandatory Actions:

• Implement accuracy monitoring
• Set up alerting for threshold breaches
• Monitor continuously
• Track accuracy trends
• Investigate accuracy issues
• Implement corrective actions
• Document monitoring results

Accuracy Monitoring Metrics:

Metric	Threshold	Alert Level	Frequency
Overall Accuracy	< 95% (high-risk)	Warning	Daily
Precision	< 95%	Warning	Daily
Recall	< 95%	Warning	Daily
Accuracy Degradation	> 2% drop	Critical	Real-time
Accuracy Stability	> 2% variation	Warning	Weekly

Evidence Required:

• Accuracy monitoring dashboard
• Alert logs
• Trend analysis reports
• Investigation records
• Corrective action records
• Monthly accuracy reports

Audit Verification:

• Verify accuracy monitoring implemented
• Confirm alerting configured
• Check monitoring reviewed regularly
• Validate issues investigated
• Verify corrective actions implemented

---

Control ARS-004: Robustness Requirements

Control ID: ARS-004
Control Name: Robustness Requirements Definition
Control Type: Preventive
Control Frequency: Per high-risk AI system, during design phase
Risk Level: High

Control Objective

Define robustness requirements per Article 15(4) to ensure AI systems are resilient to errors, faults, inconsistencies, and adversarial conditions, maintaining performance across diverse scenarios.

Control Requirements

CR-004.1: Comprehensive Robustness Requirements

Define robustness requirements across all relevant dimensions.

Robustness Dimensions:

Dimension	Description	Requirements	Metrics
Technical Robustness	Resilience to errors, faults, inconsistencies	Error handling, fault tolerance	Error rate, fault recovery time
Input Robustness	Handling of edge cases, outliers, adversarial inputs	Input validation, outlier handling	Edge case success rate, adversarial robustness
Environmental Robustness	Performance across different conditions	Environmental adaptation	Performance across conditions
Temporal Robustness	Stability over time (no drift)	Drift detection, model stability	Drift score, performance stability

Mandatory Actions:

• Define robustness requirements
• Establish robustness metrics
• Set robustness thresholds
• Document requirements
• Obtain approval
• Review and update annually

Robustness Requirements by Dimension:

Dimension	Metric	Threshold	Justification
Technical Robustness	Error rate	< 1%	Acceptable error handling
Input Robustness	Edge case success rate	≥90%	Handle edge cases
Adversarial Robustness	Adversarial success rate	≥85%	Resist adversarial attacks
Environmental Robustness	Performance variation	< 5%	Consistent across conditions
Temporal Robustness	Drift score	< 0.1	Stable over time

Evidence Required:

• Robustness Requirements Document (DOC-AI-ARS-002)
• Metrics and thresholds
• Approval records
• Annual review records

Audit Verification:

• Verify robustness requirements defined
• Confirm all dimensions covered
• Check thresholds set
• Validate approval obtained

---

Control ARS-005: Robustness Testing

Control ID: ARS-005
Control Name: Robustness Testing and Validation
Control Type: Preventive
Control Frequency: Before deployment, after model updates
Risk Level: High

Control Objective

Test AI system robustness to verify it meets defined robustness requirements, ensuring resilience to errors, faults, and adversarial conditions.

Control Requirements

CR-005.1: Comprehensive Robustness Testing

Conduct comprehensive robustness testing across all dimensions.

Test Types:

Test Type	Purpose	Test Method	Success Criteria
Edge Case Testing	Test handling of edge cases	Edge case test suite	≥90% success rate
Stress Testing	Test under extreme conditions	Stress test scenarios	System remains functional
Adversarial Testing	Test resistance to adversarial attacks	Adversarial attack simulations	≥85% success rate
Fault Injection Testing	Test fault tolerance	Fault injection scenarios	System recovers gracefully
Environmental Variation Testing	Test across different conditions	Environmental test matrix	< 5% performance variation
Drift Testing	Test temporal stability	Drift simulation	Drift score < 0.1

Mandatory Actions:

• Plan robustness testing
• Execute all test types
• Analyze results
• Verify compliance with requirements
• Document results
• Block deployment if requirements not met

Robustness Test Plan:

Test Component	Description	Success Criteria
Edge Case Tests	Test edge case handling	≥90% success rate
Stress Tests	Test under stress	System functional
Adversarial Tests	Test adversarial resistance	≥85% success rate
Fault Injection Tests	Test fault tolerance	Graceful recovery
Environmental Tests	Test across conditions	< 5% variation
Drift Tests	Test temporal stability	Drift score < 0.1

Evidence Required:

• Robustness Test Plan (PLAN-AI-ARS-002)
• Test results (TEST-AI-ARS-002)
• Analysis reports
• Compliance verification
• Approval records

Audit Verification:

• Verify robustness testing conducted
• Confirm all test types executed
• Check results meet requirements
• Validate deployment blocked if requirements not met

---

Control ARS-006: Model Drift Detection

Control ID: ARS-006
Control Name: Model Drift Detection and Response
Control Type: Detective
Control Frequency: Continuous
Risk Level: Medium

Control Objective

Monitor for data drift and concept drift to detect performance degradation early and enable timely model updates or retraining to maintain AI system performance.

Control Requirements

CR-006.1: Drift Detection Implementation

Implement comprehensive drift detection with alerting and response procedures.

Drift Types:

Drift Type	Description	Detection Method	Threshold
Data Drift	Change in input data distribution	Statistical distribution comparison	> 0.1
Concept Drift	Change in relationship between inputs and outputs	Performance monitoring	> 5% performance drop
Label Drift	Change in output distribution	Output distribution comparison	> 0.1

Mandatory Actions:

• Implement drift detection
• Monitor continuously
• Alert on drift detection
• Investigate drift causes
• Retrain or update model if needed
• Document drift events and responses

Drift Detection Configuration:

Detection Method	Metric	Threshold	Alert Level
Statistical Distance	KL divergence, Wasserstein distance	> 0.1	Warning
Performance Monitoring	Accuracy, precision, recall	> 5% drop	Critical
Distribution Comparison	Distribution similarity	< 0.9	Warning

Evidence Required:

• Drift detection configuration
• Drift monitoring dashboard
• Alert logs
• Drift investigation records
• Retraining records
• Monthly drift reports

Audit Verification:

• Verify drift detection implemented
• Confirm monitoring continuous
• Check alerts configured
• Validate drift investigated
• Verify model retrained when needed

---

Control ARS-007: Cybersecurity Requirements

Control ID: ARS-007
Control Name: Cybersecurity Requirements and Threat Assessment
Control Type: Preventive
Control Frequency: Per high-risk AI system, during design phase
Risk Level: High

Control Objective

Define cybersecurity requirements and assess AI-specific security threats per Article 15(5) to ensure AI systems are resilient against cybersecurity threats and protect against AI-specific attack vectors.

Control Requirements

CR-007.1: Security Requirements and Threat Assessment

Define security requirements and assess AI-specific threats.

Security Dimensions:

Dimension	Requirement	Implementation	Verification
Confidentiality	Protect sensitive data and models	Encryption, access controls	Security testing
Integrity	Prevent unauthorized modifications	Integrity checks, access controls	Security testing
Availability	Ensure system availability	Redundancy, DDoS protection	Availability testing
Authentication	Verify user identities	Authentication mechanisms	Security testing
Authorization	Control access appropriately	Role-based access control	Security testing

AI-Specific Security Threats:

Threat Type	Description	Defense Strategy	Testing Method
Adversarial Attacks	Malicious inputs to fool model	Adversarial training, input validation	Adversarial testing
Model Extraction	Stealing model through queries	Rate limiting, query monitoring	Model extraction testing
Data Poisoning	Corrupting training data	Data validation, provenance tracking	Data quality checks
Model Inversion	Extracting training data from model	Differential privacy, access controls	Model inversion testing
Backdoor Attacks	Hidden malicious behavior	Model verification, testing	Backdoor detection testing

Mandatory Actions:

• Define security requirements
• Conduct threat modeling
• Assess AI-specific threats
• Implement security controls
• Test security controls
• Document security architecture

Evidence Required:

• Security Requirements Document (DOC-AI-ARS-003)
• Threat model
• AI threat assessment
• Security control documentation
• Test results

Audit Verification:

• Verify security requirements defined
• Confirm threat modeling conducted
• Check AI-specific threats assessed
• Validate security controls implemented
• Verify security testing completed

---

Control ARS-008: Security Testing

Control ID: ARS-008
Control Name: Cybersecurity Testing and Validation
Control Type: Preventive
Control Frequency: Before deployment, annually, after security updates
Risk Level: High

Control Objective

Test AI system security to verify it meets security requirements and is resilient against cybersecurity threats, including AI-specific attack vectors.

Control Requirements

CR-008.1: Comprehensive Security Testing

Conduct comprehensive security testing including AI-specific tests.

Test Types:

Test Type	Purpose	Test Method	Success Criteria
Penetration Testing	Test system security	Penetration test	No critical vulnerabilities
Vulnerability Scanning	Identify vulnerabilities	Automated scanning	No critical vulnerabilities
Adversarial Robustness Testing	Test resistance to adversarial attacks	Adversarial attack simulations	≥85% success rate
Security Code Review	Review code for security issues	Manual and automated review	No critical issues
Dependency Scanning	Identify vulnerable dependencies	Dependency scanning tools	No critical vulnerabilities
Model Extraction Testing	Test resistance to model extraction	Model extraction attempts	Model protected
Data Poisoning Testing	Test resistance to data poisoning	Data poisoning simulations	System detects poisoning

Mandatory Actions:

• Plan security testing
• Execute all test types
• Remediate findings
• Verify remediation
• Document results
• Block deployment if critical vulnerabilities found

Security Test Plan:

Test Component	Description	Success Criteria
Penetration Tests	Test system security	No critical vulnerabilities
Vulnerability Scans	Scan for vulnerabilities	No critical vulnerabilities
Adversarial Tests	Test adversarial resistance	≥85% success rate
Code Review	Review code security	No critical issues
Dependency Scans	Scan dependencies	No critical vulnerabilities
AI-Specific Tests	Test AI-specific threats	Threats mitigated

Evidence Required:

• Security Test Plan (PLAN-AI-ARS-003)
• Test results (TEST-AI-ARS-003)
• Remediation records
• Verification results
• Approval records

Audit Verification:

• Verify security testing conducted
• Confirm all test types executed
• Check critical vulnerabilities remediated
• Validate deployment blocked if critical vulnerabilities found

---

SUPPORTING PROCEDURES

This standard is implemented through the following detailed procedures:

Procedure PROC-AI-ARS-001: Accuracy Testing Procedure

Purpose: Define step-by-step process for accuracy testing
Owner: Chief Technology Officer
Implements: Controls ARS-001, ARS-002

Procedure Steps:

1. Define accuracy requirements - Control ARS-001
2. Prepare test data
3. Plan accuracy testing
4. Execute accuracy tests - Control ARS-002
5. Analyze results
6. Verify threshold compliance
7. Document results
8. Obtain approval

Outputs:

• Accuracy Requirements Document
• Accuracy Test Plan
• Test results
• Approval records

---

Procedure PROC-AI-ARS-002: Robustness Testing Procedure

Purpose: Define process for robustness testing
Owner: Chief Technology Officer
Implements: Controls ARS-004, ARS-005, ARS-006

Procedure Steps:

1. Define robustness requirements - Control ARS-004
2. Plan robustness testing
3. Execute robustness tests - Control ARS-005
4. Implement drift detection - Control ARS-006
5. Analyze results
6. Verify compliance
7. Document results

Outputs:

• Robustness Requirements Document
• Robustness Test Plan
• Test results
• Drift detection configuration

---

Procedure PROC-AI-ARS-003: Cybersecurity Assessment Procedure

Purpose: Define process for cybersecurity assessment
Owner: Chief Technology Officer
Implements: Controls ARS-007, ARS-008

Procedure Steps:

1. Define security requirements - Control ARS-007
2. Conduct threat modeling
3. Assess AI-specific threats
4. Plan security testing
5. Execute security tests - Control ARS-008
6. Remediate findings
7. Verify remediation
8. Document results

Outputs:

• Security Requirements Document
• Threat model
• Security Test Plan
• Test results
• Remediation records

---

Procedure PROC-AI-ARS-004: Performance Monitoring Procedure

Purpose: Define process for continuous performance monitoring
Owner: Chief Technology Officer
Implements: Controls ARS-003, ARS-006

Procedure Steps:

1. Configure accuracy monitoring - Control ARS-003
2. Configure drift detection - Control ARS-006
3. Set up alerting
4. Monitor continuously
5. Investigate issues
6. Implement corrective actions
7. Report metrics

Outputs:

• Monitoring configuration
• Alert logs
• Investigation records
• Corrective action records
• Performance reports

---

COMPLIANCE

5.1 Compliance Monitoring

Monitoring Approach: Continuous automated monitoring supplemented by monthly manual reviews and quarterly comprehensive audits.

Compliance Metrics:

Metric	Target	Measurement Method	Frequency	Owner
Accuracy Requirements Coverage	100%	% of high-risk AI with accuracy requirements	Monthly	Chief Technology Officer
Accuracy Threshold Compliance	100%	% of AI systems meeting accuracy thresholds	Daily	Chief Technology Officer
Robustness Requirements Coverage	100%	% of high-risk AI with robustness requirements	Monthly	Chief Technology Officer
Robustness Threshold Compliance	100%	% of AI systems meeting robustness thresholds	Monthly	Chief Technology Officer
Security Requirements Coverage	100%	% of high-risk AI with security requirements	Monthly	IT Security Manager
Security Vulnerability Count	0 critical	Number of critical vulnerabilities	Monthly	IT Security Manager
Drift Detection Coverage	100%	% of AI systems with drift detection	Monthly	Chief Technology Officer
Security Testing Completion	100%	% of required security tests completed	Quarterly	IT Security Manager

Monitoring Tools:

• Performance Monitoring Dashboard
• Security Monitoring Dashboard
• Compliance Reports
• Monthly compliance reports
• Quarterly AI Governance Committee reviews

---

5.2 Internal Audit Requirements

Audit Frequency: Annually (minimum)

Audit Scope:

• Accuracy requirements completeness
• Accuracy testing quality
• Robustness requirements completeness
• Robustness testing quality
• Security requirements completeness
• Security testing quality
• Drift detection effectiveness
• Controls effectiveness (ARS-001 through ARS-008)

Audit Activities:

• Review 100% of high-risk AI for requirements
• Sample 20% of tests for quality review
• Test accuracy monitoring
• Test drift detection
• Test security controls
• Review security test results
• Interview key personnel

Audit Outputs:

• Annual Accuracy, Robustness, and Security Audit Report
• Findings and recommendations
• Corrective action plans for deficiencies

---

5.3 External Audit / Regulatory Inspection

Preparation:

• Maintain audit-ready documentation at all times
• Designate Chief Technology Officer and IT Security Manager as regulatory liaisons
• Prepare standard response procedures for authority requests

Provide to Auditors/Regulators:

• Accuracy requirements and test results
• Robustness requirements and test results
• Security requirements and test results
• Threat models
• Performance monitoring reports
• Security test reports
• Internal audit reports
• Evidence of controls execution

Authority Request Response:

• Acknowledge request within 1 business day
• Provide requested documentation within 5 business days
• Coordinate through Legal, Chief Technology Officer, and IT Security Manager
• Document all interactions with authorities

---

ROLES AND RESPONSIBILITIES

6.1 RACI Matrix

Activity	Chief Technology Officer	AI System Owner	Data Science	IT Security Manager	QA/Testing	Operations
Accuracy Requirements	R/A	A	R	I	C	I
Accuracy Testing	R	A	R	I	R	I
Accuracy Monitoring	R	A	C	I	I	R
Robustness Requirements	R	A	R	C	C	I
Robustness Testing	R	A	R	C	R	I
Drift Detection	R	A	R	I	I	C
Security Requirements	R	A	C	R/A	I	I
Security Testing	R	A	C	R	R	I

RACI Legend:

• R = Responsible (does the work)
• A = Accountable (ultimately answerable)
• C = Consulted (provides input)
• I = Informed (kept up-to-date)

---

6.2 Role Descriptions

Chief Technology Officer

• Primary Responsibility: Owns accuracy, robustness, and security framework
• Key Activities:
  • Establishes requirements framework
  • Approves requirements
  • Monitors compliance
  • Reports metrics
• Required Competencies: EU AI Act Article 15, AI system architecture, performance management

AI System Owner

• Primary Responsibility: Accountable for accuracy, robustness, and security of their AI system
• Key Activities:
  • Ensures requirements defined
  • Ensures testing completed
  • Monitors performance
  • Participates in reviews
• Required Competencies: AI system knowledge, performance requirements

Data Science

• Primary Responsibility: Implements accuracy and robustness requirements
• Key Activities:
  • Defines accuracy/robustness requirements
  • Implements models meeting requirements
  • Conducts testing
  • Monitors performance
• Required Competencies: Machine learning, model development, performance optimization

IT Security Manager

• Primary Responsibility: Owns security requirements and testing
• Key Activities:
  • Defines security requirements
  • Conducts threat modeling
  • Manages security testing
  • Monitors security
• Required Competencies: Cybersecurity, AI security, threat modeling

QA/Testing

• Primary Responsibility: Conducts testing
• Key Activities:
  • Plans testing
  • Executes tests
  • Reports results
• Required Competencies: Testing methodologies, quality assurance

Operations

• Primary Responsibility: Monitors performance in production
• Key Activities:
  • Monitors accuracy
  • Monitors drift
  • Alerts on issues
• Required Competencies: Operations monitoring, alerting

---

EXCEPTIONS

7.1 Exception Philosophy

Accuracy, robustness, and security are critical regulatory compliance activities for high-risk AI systems. Exceptions are granted restrictively and only where compensating controls adequately mitigate risks.

---

7.2 Allowed Exceptions

The following exceptions may be granted with proper justification and approval:

Exception Type	Justification Required	Maximum Duration	Approval Authority	Compensating Controls
Reduced Accuracy Threshold (Minimal-Risk AI)	AI system clearly minimal-risk; lower accuracy acceptable	Permanent	Chief Technology Officer	Document rationale; Annual re-confirmation
Extended Security Testing Timeline	Resource constraints prevent timely testing	30 days	Chief Technology Officer + IT Security Manager	Interim security measures; Accelerated plan

---

7.3 Prohibited Exceptions

The following exceptions cannot be granted under any circumstances:

❌ Skipping accuracy requirements for high-risk AI - Mandatory per Article 15(1), no exceptions
❌ Skipping robustness requirements for high-risk AI - Mandatory per Article 15(4), no exceptions
❌ Skipping security requirements for high-risk AI - Mandatory per Article 15(5), no exceptions
❌ Deploying with accuracy below threshold - Creates safety and compliance risks
❌ Deploying with critical security vulnerabilities - Creates security risks

---

7.4 Exception Request Process

Step 1: Submit Exception Request

• Complete Exception Request Form (FORM-AI-EXCEPTION-001)
• Include business justification
• Propose compensating controls
• Specify duration requested
• Attach risk assessment

Step 2: Risk Assessment

• Chief Technology Officer assesses risk of granting exception
• Evaluates adequacy of compensating controls
• Documents residual risk

Step 3: Approval

• Route to appropriate approval authority based on exception type
• Chief Technology Officer approval: Minor exceptions
• Chief Technology Officer + AI Governance Committee: Significant exceptions
• AI Governance Committee: Critical exceptions

Step 4: Documentation and Monitoring

• Document exception in Exception Register
• Assign exception owner
• Set review date
• Monitor compensating controls
• Report exceptions quarterly to AI Governance Committee

Step 5: Exception Review and Closure

• Review exception at specified review date
• Assess if exception still needed
• Close exception when normal requirements met
• Document lessons learned

---

ENFORCEMENT

8.1 Non-Compliance Consequences

Violation	Severity	Consequence	Remediation Required
High-risk AI without accuracy requirements	Critical	Immediate suspension until requirements defined	Define requirements within 5 business days; Root cause analysis
Deploying with accuracy below threshold	Critical	Immediate halt deployment; Compliance gap assessment	Improve accuracy; Re-test; Re-approve
Missing robustness requirements	High	Escalation to AI Governance Committee	Define requirements within 10 business days
Missing security requirements	Critical	Immediate suspension	Define requirements within 5 business days
Critical security vulnerabilities	Critical	Immediate correction; Security investigation	Remediate within 24 hours; Security review
Missing drift detection	Medium	Written warning	Implement drift detection within 10 business days

---

8.2 Escalation Procedures

Level 1: Chief Technology Officer

• Minor procedural violations
• Documentation deficiencies
• Timeline delays < 5 days
• Action: Written warning, corrective action required

Level 2: Chief Technology Officer + AI Governance Committee

• Repeated violations
• Missing requirements
• Performance below thresholds
• Action: Formal review, corrective action plan, management notification

Level 3: AI Governance Committee

• High-risk AI without requirements
• Critical security vulnerabilities
• Critical compliance failures
• Action: Immediate AI system suspension, investigation, disciplinary action

Level 4: Executive Management + Legal

• Potential regulatory enforcement action
• Significant legal liability
• Reputational risk
• Action: Executive crisis management, legal strategy, regulatory engagement

---

8.3 Immediate Escalation Triggers

Escalate immediately to AI Governance Committee + Legal if:

• ⚠️ High-risk AI system operating without accuracy/robustness/security requirements
• ⚠️ Critical security vulnerability identified
• ⚠️ Security breach or attack
• ⚠️ Regulatory inquiry or inspection related to accuracy/robustness/security
• ⚠️ Significant accuracy degradation affecting safety

---

8.4 Disciplinary Actions

Individuals responsible for violations may be subject to:

• Verbal or written warning
• Mandatory retraining
• Performance improvement plan
• Reassignment of responsibilities
• Suspension (with pay during investigation)
• Termination (for egregious violations, e.g., knowingly deploying with critical vulnerabilities)

Factors Considered:

• Intent (knowing violation vs. honest mistake)
• Severity of violation
• Impact (actual or potential)
• Cooperation with remediation
• Prior violation history

---

KEY PERFORMANCE INDICATORS (KPIs)

9.1 Accuracy, Robustness, and Security KPIs

KPI ID	KPI Name	Definition	Target	Measurement Method	Frequency	Owner	Reporting To
KPI-ARS-001	Accuracy Requirements Coverage	% of high-risk AI with accuracy requirements	100%	(# AI with requirements / # high-risk AI) × 100	Monthly	Chief Technology Officer	AI Governance Committee
KPI-ARS-002	Accuracy Threshold Compliance	% of AI systems meeting accuracy thresholds	100%	(# AI meeting thresholds / # total AI) × 100	Daily	Chief Technology Officer	Management
KPI-ARS-003	Accuracy Stability	Accuracy variation over time	< 2%	Standard deviation of accuracy	Weekly	Chief Technology Officer	Management
KPI-ARS-004	Robustness Requirements Coverage	% of high-risk AI with robustness requirements	100%	(# AI with requirements / # high-risk AI) × 100	Monthly	Chief Technology Officer	AI Governance Committee
KPI-ARS-005	Robustness Score	Composite robustness score	≥90%	Weighted average of robustness metrics	Monthly	Chief Technology Officer	Management
KPI-ARS-006	Drift Detection Coverage	% of AI systems with drift detection	100%	(# AI with drift detection / # total AI) × 100	Monthly	Chief Technology Officer	Management
KPI-ARS-007	Drift Detection Rate	Number of drift events detected	Monitor trends	Count of drift events	Weekly	Chief Technology Officer	Management
KPI-ARS-008	Security Requirements Coverage	% of high-risk AI with security requirements	100%	(# AI with requirements / # high-risk AI) × 100	Monthly	IT Security Manager	AI Governance Committee
KPI-ARS-009	Security Vulnerability Count	Number of critical vulnerabilities	0	Count of critical vulnerabilities	Monthly	IT Security Manager	AI Governance Committee
KPI-ARS-010	Security Testing Completion	% of required security tests completed	100%	(# tests completed / # required tests) × 100	Quarterly	IT Security Manager	Management

---

9.2 KPI Dashboards and Reporting

Real-Time Dashboard (Chief Technology Officer and IT Security Manager access)

• Current accuracy metrics
• Robustness scores
• Drift detection status
• Security vulnerability status
• Performance trends

Monthly Management Report

• KPI-ARS-001, 002, 003, 004, 005, 006, 007, 008, 009
• Trend analysis (vs. previous month)
• Issues and risks
• Planned actions

Quarterly AI Governance Committee Report

• All KPIs
• Accuracy performance assessment
• Robustness performance assessment
• Security posture assessment
• Internal audit findings (if conducted)
• Exception register review

Annual Executive Report

• Full-year KPI performance
• Performance maturity assessment
• Strategic recommendations
• Regulatory outlook

---

9.3 KPI Thresholds and Alerts

KPI	Green (Good)	Yellow (Warning)	Red (Critical)	Alert Action
Accuracy Threshold Compliance	100%	95-99%	< 95%	Red: Immediate escalation to AI Governance Committee Chair
Accuracy Stability	< 2%	2-5%	> 5%	Red: Escalate to AI Governance Committee
Robustness Score	≥90%	85-89%	< 85%	Yellow: Improvement plan; Red: Escalate to AI Governance Committee
Security Vulnerability Count	0	1-2	> 2	Red: Immediate escalation to IT Security Manager + AI Governance Committee

---

TRAINING REQUIREMENTS

10.1 Training Program Overview

All personnel involved in accuracy, robustness, and security must complete role-specific training to ensure competency in EU AI Act Article 15 requirements, performance management, and security practices.

---

10.2 Role-Based Training Requirements

Role	Training Course	Duration	Content	Frequency	Assessment Required
Chief Technology Officer	Performance Management Expert Training	16 hours	EU AI Act Article 15; Accuracy requirements; Robustness requirements; Security requirements	Initial + annually	Yes - Written exam (≥90%)
Data Scientists	Accuracy and Robustness Training	12 hours	Accuracy metrics; Robustness testing; Drift detection; Model optimization	Initial + annually	Yes - Practical exercise
IT Security Manager	AI Security Expert Training	16 hours	AI-specific security threats; Security testing; Threat modeling; Security controls	Initial + annually	Yes - Written exam (≥90%)
QA/Testing	Performance Testing Training	8 hours	Accuracy testing; Robustness testing; Security testing; Test methodologies	Initial + annually	Yes - Practical exercise
AI System Owners	Performance Overview	4 hours	Accuracy requirements; Robustness requirements; Security requirements; Responsibilities	At onboarding + annually	Yes - Knowledge check (≥80%)
All AI Development Staff	Performance Awareness	2 hours	Accuracy basics; Robustness basics; Security basics; Requirements	At onboarding + annually	Yes - Knowledge check (≥80%)

---

10.3 Training Content by Topic

EU AI Act Article 15 Requirements

• Accuracy requirements (Article 15(1))
• Robustness requirements (Article 15(4))
• Security requirements (Article 15(5))
• Compliance obligations

Accuracy Management

• Accuracy metrics
• Accuracy testing
• Accuracy monitoring
• Performance optimization

Robustness Management

• Robustness dimensions
• Robustness testing
• Drift detection
• Model stability

AI Security

• AI-specific security threats
• Security controls
• Security testing
• Threat modeling

---

10.4 Training Delivery Methods

Initial Training:

• Instructor-led classroom or virtual training
• Includes interactive exercises and case studies
• Hands-on practice with testing tools
• Group discussions of complex scenarios

Annual Refresher:

• E-learning modules for core content review
• Live update sessions for regulatory changes
• Case study reviews of recent performance activities
• Knowledge assessment

On-the-Job Training:

• Mentoring for new performance staff
• Job shadowing during testing
• Supervised testing for first 5 AI systems

Just-in-Time Training:

• Quick reference guides and job aids
• Video tutorials on specific topics
• Help desk support from experienced staff

---

10.5 Training Effectiveness Measurement

Assessment Methods:

• Written exams for knowledge retention
• Practical exercises for skill application
• On-the-job observations for competency validation
• Feedback surveys for training quality

Competency Validation:

• Data Scientists: Must demonstrate ability to define requirements and conduct testing for 1 sample AI system with 100% compliance before independent work
• All staff: Must pass knowledge assessments with minimum required scores

Training Metrics:

Metric	Target	Frequency
Training completion rate	100%	Quarterly
Assessment pass rate (first attempt)	≥ 90%	Per training
Training effectiveness score (survey)	≥ 4.0/5.0	Per training
Time to competency (Data Scientists)	< 30 days	Per person

---

10.6 Training Records

Records Maintained:

• Training attendance records
• Assessment scores
• Competency validations
• Refresher training completion
• Individual training transcripts

Retention: 10 years (to align with EU AI Act documentation retention)

Access: HR, Chief Technology Officer, Internal Audit, Competent Authorities (upon request)

---

DEFINITIONS

Term	Definition	Source
Accuracy	Degree to which AI system outputs are correct	EU AI Act Article 15(1)
Robustness	Ability of AI system to maintain performance despite errors, faults, or adversarial conditions	EU AI Act Article 15(4)
Cybersecurity	Protection of AI systems against cybersecurity threats	EU AI Act Article 15(5)
Precision	% of positive predictions that are correct	This Standard
Recall	% of actual positives correctly identified	This Standard
F1 Score	Harmonic mean of precision and recall	This Standard
ROC-AUC	Area under Receiver Operating Characteristic curve	This Standard
Data Drift	Change in input data distribution over time	This Standard
Concept Drift	Change in relationship between inputs and outputs over time	This Standard
Adversarial Attack	Malicious input designed to fool AI model	This Standard
Model Extraction	Attack to steal model through queries	This Standard
Data Poisoning	Attack to corrupt training data	This Standard
Model Inversion	Attack to extract training data from model	This Standard
Backdoor Attack	Attack to introduce hidden malicious behavior	This Standard

---

LINK WITH AI ACT AND ISO42001

12.1 EU AI Act Regulatory Mapping

This standard implements the following EU AI Act requirements:

EU AI Act Provision	Article	Requirement Summary	Implemented By (Controls)
Accuracy, Robustness and Cybersecurity	Article 15	Requirements for high-risk AI	All controls (ARS-001 through ARS-008)
Accuracy	Article 15(1)	Appropriate accuracy levels	ARS-001, ARS-002, ARS-003
Robustness	Article 15(4)	Resilience to errors and faults	ARS-004, ARS-005, ARS-006
Cybersecurity	Article 15(5)	Resilience against cybersecurity threats	ARS-007, ARS-008

---

12.2 ISO/IEC 42001:2023 Alignment

This standard aligns with ISO/IEC 42001:2023 as follows:

ISO 42001 Clause	Requirement	Implementation in This Standard
Clause 6.1.2: AI system impact assessment	Assess accuracy, robustness, security impacts	ARS-001, ARS-004, ARS-007
Clause 8.2: AI system risk assessment	Accuracy, robustness, security in risk management	All controls
Clause 9.1: Monitoring, measurement, analysis, and evaluation	Monitor performance	ARS-003, ARS-006

---

12.3 Relationship to Other Standards

This accuracy, robustness, and security standard integrates with other AI Act standards:

Related Standard	Integration Point	Rationale
STD-AI-001: Classification	Classification determines if requirements apply	High-risk AI requires Article 15 requirements
STD-AI-002: Risk Management	Accuracy, robustness, security risks in risk assessment	Risk management identifies performance risks
STD-AI-004: Technical Documentation	Performance metrics documented in Annex IV	Performance information in technical documentation
STD-AI-013: Incident Management	Performance issues may trigger incidents	Performance degradation may be incidents

---

12.4 References and Related Documents

EU AI Act (Regulation (EU) 2024/1689):

• Article 15: Accuracy, Robustness and Cybersecurity
• Article 15(1): Accuracy
• Article 15(3): Accuracy Declaration in Instructions for Use (cross-reference to STD-AI-006 Control TRANS-001)
• Article 15(4): Robustness
• Article 15(5): Cybersecurity

ISO/IEC Standards:

• ISO/IEC 42001:2023: Information technology — Artificial intelligence — Management system
• ISO/IEC 23894:2023: Information technology — Artificial intelligence — Guidance on risk management
• ISO/IEC 27001:2022: Information security management systems

Internal Documents:

• POL-AI-001: Artificial Intelligence Policy (parent policy)
• STD-AI-001: AI System Classification Standard
• STD-AI-002: AI Risk Management Standard
• STD-AI-004: AI Technical Documentation Standard
• PROC-AI-ARS-001, -002, -003, -004: Performance procedures

---

APPROVAL AND AUTHORIZATION

Role	Name	Title	Signature	Date
Prepared By	David Lee	Chief Technology Officer	_________________	________
Reviewed By	IT Security Manager	IT Security Manager	_________________	________
Reviewed By	Sarah Johnson	AI Act Program Manager	_________________	________
Reviewed By	Jane Doe	Chief Strategy & Risk Officer	_________________	________
Approved By	Jane Doe	AI Governance Committee Chair	_________________	________

Effective Date: 2025-08-01
Next Review Date: 2026-08-01
Review Frequency: Annually or upon regulatory change

---

END OF STANDARD STD-AI-008

---

This standard is a living document. Feedback and improvement suggestions should be directed to the Chief Technology Officer.