AIUC-1
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C008

Monitor AI risk categories

Implement monitoring of AI systems across risk categories

Keywords
Monitoring
High-Risk Outputs
Application
Optional
Frequency
Every 12 months
Type
Detective
Crosswalks
Article 72: Post-Market Monitoring by Providers and Post-Market Monitoring Plan for High-Risk AI Systems
A.5.4: Assessing AI system impact on individuals or groups of individuals
A.6.2.6: AI system operation and monitoring
A.9.4: Intended use of the AI system
6.1.1: Actions to address risks and opportunities — General
6.1.2: AI risk assessment
6.1.3: AI risk treatment
8.2: AI risk assessment
8.3: AI risk treatment
9.1: Monitoring, measurement, analysis and evaluation
A.9.2: Processes for responsible use of AI systems
GOVERN 1.5: Risk monitoring and review
MANAGE 3.1: Third-party monitoring
MANAGE 4.1: Post-deployment monitoring
MEASURE 2.4: Production monitoring
MEASURE 4.3: Performance tracking
GRC-02: Risk Management Program
MDS-11: Model Failure
TVM-03: Vulnerability Remediation Schedule
MDS-12: Open Model Risk Assessment
TVM-07: Vulnerability Remediation Schedule
Establishing ongoing monitoring of AI outputs across risk categories. For example, conducting regular evaluations prioritized by risk severity, sampling outputs for review, and tracking system behavior patterns.
C008.1 Logs: AI risk monitoring

Screenshot of monitoring dashboard, logging system, or evaluation reports showing ongoing AI output tracking - may include output sampling logs with review results, behavior trace logs showing system patterns, prompt-response logging configuration, evaluation schedules prioritized by risk severity, or monitoring metrics dashboard tracking trends over time.

Category

Technical Implementation
Engineering Tooling
Universal
Maintaining documentation. For example, recording identified scenarios with clear examples, updating risk taxonomy based on monitoring findings and incidents.
C008.2 Documentation: Monitoring findings

Document or change log showing identified risk scenarios with examples - may include incident reports triggering taxonomy changes, risk scenario database with concrete examples, or version history of risk taxonomy showing updates with rationale linked to monitoring findings.

Category

Technical Implementation
Engineering Practice
Universal
Integrating AI output monitoring with existing security tools. For example, forwarding alerts and flagged outputs to SIEM platforms, applying standard logging formats (e.g. JSON, syslog) to support automated threat detection workflows.
C008.4 Config: Security tooling

Screenshot of SIEM integration, log forwarding configuration, or security tool settings showing AI monitoring data flowing into existing security infrastructure - may include Splunk/Datadog/Elastic forwarding rules for AI alerts, JSON/syslog format configuration for AI logs, or SIEM dashboard showing AI-related events alongside other security telemetry.

Category

Technical Implementation
Engineering Tooling
Universal

Organizations can submit alternative evidence demonstrating how they meet the requirement.

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MITRE
Dr. Christina Liaghati
MITRE ATLAS lead
Hyrum Anderson

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Hyrum Anderson
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Prof. Sanmi Koyejo

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John Bautista
Partner at Orrick
Lena Smart

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SecurityPal
Lena Smart
Head of Trust for SecurityPal and former CISO of MongoDB

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