Post
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In data privacy, 92% accuracy is not an A-grade. Privacy AI needs to be better.
That's the stark takeaway from a recent benchmark by Diego Mouriño
(Making Science), who put today's top PII detection methods to the test on call center transcripts using the Ai4Privacy dataset.
They pitted cutting-edge LLMs (like GPT-4 & Gemini) against traditional systems (like Cloud DLPs). The results show that our trust in these tools might be misplaced.
📊 The Hard Numbers:
Even top-tier LLMs peaked at a reported 92% accuracy, leaving a potential dangerous 8% gap where your customer's data can leak. They particularly struggled with basics like 'last names' and 'street addresses'.
The old guard? Traditional rule-based systems reportedly achieved a shocking 50% accuracy. A coin toss with your customers' privacy.
This tells us that for privacy tasks, off-the-shelf accuracy is a vanity metric. The real metric is the cost of a single failure—one leaked name, one exposed address.
While no tool is perfect, some are better than others. Diego’s full analysis breaks down which models offer the best cost-to-accuracy balance in this flawed landscape. It's a must-read for anyone serious about building trustworthy AI.
#DataPrivacy #AI #LLM #RiskManagement #MetricsThatMatter #InfoSec
Find the full post here:
https://www.makingscience.com/blog/protecting-customer-privacy-how-to-remove-pii-from-call-center-transcripts/
Dataset:
ai4privacy/pii-masking-400k
That's the stark takeaway from a recent benchmark by Diego Mouriño
(Making Science), who put today's top PII detection methods to the test on call center transcripts using the Ai4Privacy dataset.
They pitted cutting-edge LLMs (like GPT-4 & Gemini) against traditional systems (like Cloud DLPs). The results show that our trust in these tools might be misplaced.
📊 The Hard Numbers:
Even top-tier LLMs peaked at a reported 92% accuracy, leaving a potential dangerous 8% gap where your customer's data can leak. They particularly struggled with basics like 'last names' and 'street addresses'.
The old guard? Traditional rule-based systems reportedly achieved a shocking 50% accuracy. A coin toss with your customers' privacy.
This tells us that for privacy tasks, off-the-shelf accuracy is a vanity metric. The real metric is the cost of a single failure—one leaked name, one exposed address.
While no tool is perfect, some are better than others. Diego’s full analysis breaks down which models offer the best cost-to-accuracy balance in this flawed landscape. It's a must-read for anyone serious about building trustworthy AI.
#DataPrivacy #AI #LLM #RiskManagement #MetricsThatMatter #InfoSec
Find the full post here:
https://www.makingscience.com/blog/protecting-customer-privacy-how-to-remove-pii-from-call-center-transcripts/
Dataset:
ai4privacy/pii-masking-400k