Nightfall’s detectors are built on 125 million parameters. As a result, Nightfall makes more accurate predictions and outperforms regex and low-fidelity machine learning approaches by up to 85%.
Natural Language Processing (NLP) helps detectors to understand the context surrounding possible violations—which means fewer false positive alerts.
Nightfall's unparalleled precision empowers SecOps teams to automate remediation. This has a twofold effect: Reducing total cost of ownership while also improving overall security posture.
Identify driver’s licenses, passports, credit cards, and social security cards without needing to scrape images for text.
Outperform Optical Character Recognition (OCR) with Nightfall’s advanced transformer model, which can recognize sensitive documents even when they’re…
Discover social security numbers, driver’s license numbers, credit card numbers, and more with Nightfall’s Convolutional Neural Network (CNN).
Use LLM-generated embeddings to evaluate the context surrounding possible PII violations, resulting in fewer false positive alerts.
Scan for ICD10 diagnosis codes, FDA drug names, healthcare NPIs, and other protected health information using a powerful transformer model.
Map health information to specific individuals to ensure that only HIPAA-defined PHI is flagged.
Detect dozens of types of API keys, cryptographic keys, database connection strings, passwords, and more.
Pick up edge cases with a powerful LLM that's been trained on 125 million parameters.
We studied over 5,000 data samples for each of our top PII, PHI, secret, and image detectors. Then we compared them to the competition.
Greater precision
Reduction in noise
Reduction in costs, plus additional time savings from automation
Compared to major cloud DLP players like Google DLP, AWS Comprehend, and Microsoft Purview, Nightfall’s AI detectors display 2x greater precision for supported data types. What do we mean by “supported data types,” you may ask? In short, we only consider the detectors that compare directly to Nightfall’s.
Nightfall uses LLM-generated embeddings from our Convolutional Neural Networks (CNNs) to more precisely evaluate the context surrounding possible social security numbers, driver’s license numbers, credit card numbers, and more. As a result, Nightfall’s PII detection is 1.5x more precise, and Nightfall’s PCI detection is 2x more precise than AWS, Google, and Microsoft.
Nightfall’s advanced LLM has been fine-tuned on 125 million parameters to detect secrets in even the most complex edge cases. Nightfall’s secrets detection is 2x more precise than AWS, Google, and Microsoft.
HIPAA defines PHI as health data (such as a diagnosis or drug prescription) that can be traced to a uniquely identifiable individual. Nightfall leverages a powerful transformer model to map health information to specific individuals, ensuring that only true PHI is flagged. AWS and Google offer detectors for some of the individual entities needed to detect PHI; however, they can’t be directly compared to Nightfall’s PHI detector, which considers a combination of PII and PHI.
Nightfall’s image classifier model achieves 3-4x better performance than the typical OCR model. Image classification models rely on a much larger corpus of data, and as a result, can contextualize images based on their overall format as opposed to just the text they contain. This means that Nightfall can recognize sensitive documents even when images are blurry, grainy, or otherwise difficult to read.
Find a cloud DLP solution that meets your unique detection needs.
Your go-to cloud DLP solution should leverage AI to cut through the noise, reduce security team workloads, and improve your security posture.