Input Monitoring

[All Actors]
1. Unified Logging and Monitoring: Capture all inbound requests and metadata (timestamps, source, user ID, model endpoint) for AI model usage. Store logs in a secure, centralized platform with role-based access controls (RBAC) and encryption at rest and in transit. Automate real-time monitoring (SIEM, anomaly detection) for suspicious activity or policy violations.

2. Audit Trails and Reporting: Maintain detailed audit trails (user actions, model inputs) for accountability and compliance. Integrate logs into existing reporting frameworks; schedule periodic reviews to identify unauthorized access or unusual patterns. Enforce retention policies and anonymization practices to protect sensitive data.

3. Access Control and Policy Alignment: Apply RBAC to both log data and AI resources, allowing only authorized personnel to view sensitive input content. Align logging with organizational usage policies (legal, ethical) to ensure captured inputs and metadata meet data governance requirements.

4. Incident Response and Collaboration: Define escalation procedures for AI-specific incidents (e.g., suspicious input patterns, security breaches). Coordinate with external providers or internal teams to share logs and relevant data for prompt investigation and resolution.

5. Continuous Improvement: Regularly refine logging configurations, threshold alerts, and monitoring rules to address evolving AI use cases. Conduct periodic reviews of logged data to spot anomalies and adapt controls as threats or operational needs change.

1. Inquiring with Control Owners

1.1 Conduct interviews with personnel responsible for logging and monitoring all input events (content and metadata) to cloud AI processing infrastructure to understand their processes for capturing, storing, and analyzing workload input data for auditing infrastructure usage and reporting on compute resource utilization across customers. Verify their understanding of input event logging requirements for cloud infrastructure, monitoring procedures for customer workload patterns and resource performance, and reporting capabilities that enable comprehensive auditing of AI infrastructure interactions and capacity analytics.

2. Inspecting Records and Documents

2.1 Confirm input logging covers all cloud infrastructure endpoints including compute APIs, storage interfaces, customer portals, and third-party cloud service integrations.

2.2 Verify logs capture customer identity, workload source, timestamp, resource allocation, and input payload structure for AI infrastructure requests.

2.3 Check that logging does not capture sensitive customer workload data unless explicitly required for infrastructure functionality and properly protected under customer agreements.

2.4 Validate logging covers both direct customer inputs to infrastructure services and indirect inputs processed through automated scaling and resource management systems.

2.5 Confirm that cloud infrastructure logs are used to detect resource abuse, security violations, or malformed requests affecting infrastructure security and customer isolation.

2.6 Review retention settings to ensure cloud infrastructure input logs are stored in alignment with customer agreements and regulatory compliance requirements.

2.7 Ensure cloud infrastructure input logs feed into usage analytics dashboards for capacity planning and customer experience monitoring.

2.8 Verify access to cloud infrastructure input logs is role-restricted to authorized infrastructure personnel and fully auditable for customer data protection.

2.9 Examine monitoring capabilities to ensure real-time visibility into cloud infrastructure usage patterns, resource performance metrics, and customer utilization trends.

2.10 Validate that metadata logging includes comprehensive infrastructure context such as resource parameters, service configurations, customer details, and performance metrics.

2.11 Review reporting mechanisms to confirm they provide adequate audit trails for cloud infrastructure governance, customer compliance, and capacity analytics.

2.12 Confirm logging APIs and input collection services provide tenants access to their logs.

2.13 Validate input logging mechanisms are embedded in AI services (e.g., model endpoints).

2.14 Ensure logs capture tenant ID, region, service version, and API path.

2.15 Confirm guardrails prevent inadvertent access to other tenants' input logs.

2.16 Check for logs covering service-to-service input invocations.

2.17 Validate SLAs are met for input log availability and retention.

2.18 Verify CSP personnel access to logs is logged and reviewed.