How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from authorized paperwork at scale

As we speak, personally identifiable information (PII) is all over the place. PII is in emails, slack messages, movies, PDFs, and so forth. It refers to any information or info that can be utilized to determine a selected particular person. PII is delicate in nature and contains numerous sorts of private information, similar to identify, contact info, identification numbers, monetary info, medical info, biometric information, date of start, and so forth.

Discovering and redacting PII is crucial to safeguarding privateness, making certain information safety, complying with legal guidelines and laws, and sustaining belief with clients and stakeholders. It’s a essential part of contemporary information administration and cybersecurity practices. However discovering PII among the many morass of digital information can current challenges for a company. These challenges come up as a result of huge quantity and number of information, information fragmentation, encryption, information sharing, dynamic content material, false positives and negatives, contextual understanding, authorized complexities, useful resource constraints, evolving information, user-generated content material, and adaptive threats. Nonetheless, failure to precisely detect and redact PII can result in extreme penalties for organizations. Penalties may embody authorized penalties, lawsuits, popularity injury, information breach prices, regulatory probes, operational disruption, belief erosion, and sanctions.

Within the authorized system, discovery is the authorized course of governing the suitable to acquire and the duty to provide non-privileged matter related to any social gathering’s claims or defenses in litigation. Digital discovery also called eDiscovery is the digital side of figuring out, amassing, and producing electronically saved info (ESI) in response to a request for manufacturing in a lawsuit or investigation. Within the authorized area, it’s typically required to determine, acquire, and produce ESI throughout a lawsuit or investigation. If organizations are coping with eDiscovery for litigations on subpoena responses, they’re most likely involved about by accident sharing PII. Many organizations together with authorities businesses, faculty districts, and authorized professionals face the problem of detecting and redacting PII precisely at scale. Particularly in the event that they’re a part of a authorities group, redacting PII by means of the Freedom of Info Act and Digital Providers Act is essential for shielding particular person privateness, making certain compliance with information safety legal guidelines, stopping identification theft, and sustaining belief and transparency in authorities and digital companies. It strikes a stability between transparency and privateness whereas mitigating authorized and safety dangers.

Organizations can seek for PII utilizing strategies similar to key phrase searches, sample matching, information loss prevention instruments, machine studying (ML), metadata evaluation, information classification software program, optical character recognition (OCR), doc fingerprinting, and encryption.

Now part of Reveal’s AI-powered eDiscovery platform, Logikcull is a self-service answer that permits authorized professionals to course of, assessment, tag, and produce digital paperwork as a part of a lawsuit or investigation. This distinctive providing helps attorneys uncover helpful info associated to the matter in hand whereas decreasing prices, rushing up resolutions, and mitigating dangers.

On this publish, Reveal consultants showcase how they used Amazon Comprehend of their doc processing pipeline to detect and redact particular person items of PII. Amazon Comprehend is a totally managed and constantly skilled pure language processing (NLP) service that may extract perception concerning the content material of a doc or textual content. You should utilize Amazon Comprehend ML capabilities to detect and redact PII in buyer emails, help tickets, product critiques, social media, and extra.

Overview of answer

The overarching aim for the engineering workforce is to detect and redact PII from thousands and thousands of authorized paperwork for his or her clients. Utilizing Reveal’s Logikcull answer, the engineering workforce carried out two processes, specifically first go PII detection and second go PII detection and redaction. This two go answer was made attainable through the use of the ContainsPiiEntities and DetectPiiEntities APIs.

First go PII detection

The aim of first go PII detection is to search out the paperwork which may include PII.

  1. Customers add the recordsdata on which they want to carry out PII detection and redaction by means of Logikcull’s public web site right into a undertaking folder. These recordsdata might be within the type of workplace paperwork, .pdf recordsdata, emails, or a .zip file containing all of the supported file sorts.
  2. Logikcull shops these undertaking folders securely inside an Amazon Simple Storage Service (Amazon S3) bucket. The recordsdata then go by means of Logikcull’s massively parallel processing pipeline hosted on Amazon Elastic Compute Cloud (Amazon EC2), which processes the recordsdata, extracts the metadata, and generates artifacts in textual content format for information assessment. Logikcull’s processing pipeline helps textual content extraction for all kinds of varieties and recordsdata, together with audio and video recordsdata.
  3. After the recordsdata can be found in textual content format, Logikcull passes the enter textual content together with the language mannequin, which is English, by means of Amazon Comprehend by making the ContainsPiiEntities API name. The processing pipeline servers hosted on Amazon EC2 make the Amazon Comprehend ContainsPiiEntities API name by passing the request parameters as textual content and language code. The ContainsPiiEntities API name analyzes enter textual content for the presence of PII and returns the labels of recognized PII entity sorts, similar to identify, deal with, checking account quantity, or cellphone quantity. The API response additionally features a confidence rating which signifies the extent of confidence that Amazon Comprehend has assigned to the detection accuracy. The arrogance rating has a price between 0 and 1, with 1 signifying one hundred pc confidence. Logikcull makes use of this confidence rating to assign the tag PII Detected to the paperwork. Logikcull solely assigns this tag to paperwork which have a confidence rating of over 0.75.
  4. PII Detected tagged paperwork are fed into Logikcull’s search index cluster for his or her customers to rapidly determine paperwork that include PII entities.

Second go PII detection and redaction

The primary go PII detection course of narrows down the scope of the dataset by figuring out which paperwork include PII info. This accelerates the PII detection course of and in addition reduces the general price. The aim of the second go PII detection is to determine the person cases of PII and redact them from the tagged paperwork within the first go.

  1. Customers seek for paperwork by means of the Logikcull’s web site that accommodates PII utilizing Logikcull’s superior search filters characteristic.
  2. The request is dealt with by Logikcull’s utility servers hosted on Amazon EC2 and the servers communicates with the search index cluster to search out the paperwork.
  3. The Logikcull functions servers are capable of determine the person cases of PII by making the DetectPiiEntities API name. The servers make the API name by passing the textual content and language of enter paperwork. The DetectPiiEntities API motion inspects the enter textual content for entities that include PII. For every entity, the response offers the entity sort, the place the entity textual content begins and ends, and the extent of confidence that Amazon Comprehend has in its detection.
  4. The customers then choose the precise entities that they need to redact utilizing Logikcull’s internet interface. The functions server sends these requests to Logikcull’s processing pipeline. The next is a screenshot of a PDF that was uploaded to Logikcull’s utility. From the under screenshot, you possibly can see that completely different PII entities similar to identify, deal with, cellphone quantity, e-mail deal with, and so forth, have been highlighted.

  1. The PII redaction is safely utilized contained in the Logikcull’s processing pipeline utilizing {custom} enterprise logic. From the screenshot that follows, you possibly can see that customers can choose both particular PII entity sorts  or all PII entity sorts that they need to redact after which, with a click on of a single button, redact all of the PII info.


Logikcull, a Reveal know-how, is presently processing over 20 million paperwork every week and was capable of slender down the scope of detection utilizing the ContainsPiiEntities API and show particular person cases of PII entities to their clients through the use of the DetectPiiEntities API.

“With Amazon Comprehend, Logikcull has been capable of quickly deploy highly effective NLP capabilities in a fraction of the time a custom-built answer would have required.”

– Steve Newhouse, VP of Product for Logikcull.


Amazon Comprehend permits Reveal’s Logikcull know-how to run PII detection at massive scale for comparatively low price utilizing Amazon Comprehend. The ContainsPiiEntities API is used to do an preliminary scan of thousands and thousands of paperwork. The DetectPiiEntities API is used to run an in depth evaluation of hundreds of paperwork and determine particular person items of PII of their paperwork.

Check out all of the Amazon Comprehend features. Give the contains a attempt to ship us suggestions both by means of the AWS forum  for Amazon Comprehend or by means of your common AWS help contacts.

In regards to the Authors

Aman Tiwari is a Normal Options Architect working with Worldwide Business Gross sales at AWS. He works with clients within the Digital Native Enterprise phase and helps them design revolutionary, resilient, and cost-effective options utilizing AWS companies. He holds a grasp’s diploma in Telecommunications Networks from Northeastern College. Exterior of labor, he enjoys enjoying garden tennis and studying books.

Jeff Newburn is a Senior Software program Engineering Supervisor main the Information Engineering workforce at Logikcull – A Reveal Know-how.  He oversees the corporate’s information initiatives, together with information warehouses, visualizations, analytics, and machine studying.  With expertise spanning improvement and administration in areas from experience sharing to information methods, he enjoys main groups of good engineers to thrilling merchandise.

Søren Blond Daugaard is a Workers Engineer within the Information Engineering workforce at Logikcull – A Reveal Know-how. He implements extremely scalable AI and ML options into the Logikcull product, enabling our clients to do their work extra effectively and with increased precision. His experience spans information pipelines, web-based methods, and machine studying methods.

Kevin Lufkin is a Senior Software program Engineer on the Search Engineering workforce at Logikcull – A Reveal Know-how, the place he focuses on creating buyer going through and search-related options. His in depth experience in UI/UX is complemented by a background in full-stack internet improvement, with a powerful give attention to bringing product visions to life.

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