For Heads-of · Practitioner
AI system monitoring and logging
Continuous telemetry, drift detection, and audit logging of AI system inputs, outputs, and overrides.
- detective
- monitoring
- audit-trail
What it does
Captures telemetry, drift signals, and an audit trail of inputs, outputs, and human overrides for every production AI interaction.
Where it fits
Feeds the ISO/IEC 42001 Clause 9 performance-evaluation cycle and gives incident response something to work from.
Risks this mitigates
The risks this control addresses, ranked by effectiveness.
Excessive agency in autonomous agents
An agent is granted more permission, tool access, or autonomy than the task requires.
Sensitive information disclosure
A model surfaces training, fine-tuning, or retrieval-sourced sensitive data in its output.
Training data bias and discriminatory outcomes
Historical or sampling bias in training data is reproduced or amplified in model outputs and decisions.
AI model drift and degradation
A deployed model's performance silently degrades over time as the input distribution shifts away from its training and validation data.
AI system lifecycle controls gap
AI systems lack defined lifecycle controls, so changes ship without a consistent governance checkpoint from requirements through decommissioning.
Model extraction and inference-API abuse
Repeated querying of a model's inference API extracts private training data or effectively replicates the model itself.
Unbounded resource consumption
Unrestricted or excessive inference requests lead to denial of service, denial of wallet, or facilitate model theft.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| ISO/IEC 42001:2023 | Clause 9 | Performance evaluation |