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Maintain logs of AI system processes, actions, and agent outputs where permitted to support incident investigation, auditing, and explanation of AI system behavior
Keywords
ExplainabilityLogs
Application
Mandatory
Frequency
Every 12 months
Type
Detective
Capabilities
Universal
Crosswalks

Control activities

Typical evidence

Should include?

Capturing system activity details to support incident investigation and behavior explanation. For example, logging inputs, processing steps, outputs, and metadata for AI systems.

E015.1 Config: Logging implementation

Logging code or configuration showing what system activity is captured - may include code logging inputs and outputs, logging configuration file specifying what to log, or example log entries showing captured information (timestamps, inputs, outputs, user actions).

Typical location
Logs
Capabilities
Universal

Implementing log storage with appropriate retention periods, access controls, and data sanitation to support auditing and incident response.

E015.3 Config: Log storage

Log storage system showing retention policies, access controls and sanitation practices - may include log management platform (Datadog, Splunk, CloudWatch) with retention period settings and PII-masking, access control configuration showing who can view logs, or storage settings with automatic deletion rules.

Typical location
LogsEngineering Tooling
Capabilities
Universal
May include?

Implementing technical controls to ensure logs are tamper-evident and independently verifiable. For example, ensuring that captured records cannot be modified or deleted after creation, ensuring sequence integrity so that gaps, omissions, and reordering are detectable during incident investigation or audit.

E015.4 Config: Log integrity protection

Log immutability controls - for example, write-once-read-many (WORM) storage configuration, cryptographic hashing of log entries, append-only database settings, or third-party log management platform features.

Typical location
LogsEngineering Code
Capabilities
Universal

Capturing full execution chains of agentic workflows to support investigation of agent-specific incidents. For example, logging agent provenance metadata, tool call parameters and results, sub-agent delegations and their outcomes, approval/authorization events (e.g., human-in-the-loop approvals), and reasoning traces where available.

E015.2 Config: AI agent logging implementation

Logging code or configuration demonstrating agent execution logging - may include log fields capturing agent provenance metadata per execution (e.g. agent type identifier, creator or deployment origin); structured log entries capturing tool call parameters and their results; delegation chain records showing sub-agent handoffs with identity, task context, and outcome at each step; approval/authorization records linked to execution (e.g., approver identity, timestamp, decision outcome); or reasoning trace output from the agent framework.

Typical location
Logs
Capabilities
Universal

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