ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction
Quick summary
arXiv:2609.29191v1 Announce Type: new Abstract: Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recognizers fix a taxonomy at training time, requiring retraining for each new domain. We introduce ASIRF (Agentic Sensitive Information Redaction Framework), which retrieves domain-specific definitions based on the input's domain from a flexible knowledge base at inference time, needing no retraining to adapt. Two architectures, a three-call multi-agent pipeline and a single-agent variant, are evaluated
Key takeaways
- arXiv:2609.29191v1 Announce Type: new Abstract: Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recognizers fix a taxonomy at training time, requiring retraining for each new domain.
- We introduce ASIRF (Agentic Sensitive Information Redaction Framework), which retrieves domain-specific definitions based on the input's domain from a flexible knowledge base at inference time, needing no retraining to adapt.
- Two architectures, a three-call multi-agent pipeline and a single-agent variant, are evaluated
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction” may reshape data collection, model training, output accountability and market access.
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