ToolsSeptember 10, 2026via AWS Machine Learning Blog
Model-agnostic PII detection with LLMs
Why it matters
A practical pattern for practitioners: define PII detection rules in prompts rather than code, enabling rapid adaptation to new entity types and model switching without ML ops overhead. Outperforms off-the-shelf tools and nine LLM baselines across five benchmarks.
Key signals
- Model-agnostic detector works with any LLM on Amazon Bedrock
- Entity types defined in prompt, not code—enables rapid reconfiguration
- No retraining required to detect new entity types
- Outperforms off-the-shelf PII detection tools
- Outperforms nine LLM-based detectors
- Evaluated across five public corpora
- Model-agnostic approach: works with any LLM on Bedrock
- Prompt-configurable entity detection (no retraining required)
- Outperforms off-the-shelf tool and nine LLM-based detectors
- Evaluated across five public PII corpora
- Configurable for new entity types without model updates
The hook
AWS ships model-agnostic PII detector on Bedrock—swap models, swap entity types, no retraining needed.
A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five pub…