AI and ML can address underlying challenges to compliance, classification, and data insight, and shift the paradigm for data security, data privacy, and data management. BigID leverages ML and AI across the platform, accelerating accuracy, insight, and time to value with minimal human resources, less error, and more actionability.

By weaving ML capabilities across discovery, classification, and data intelligence, customers can get more accurate results and deeper insights than traditional techniques.

How? From NLP to graph technology to customizable classifiers, these unique ML capabilities make it easier than ever to gain a deep understanding of your data, cut through the noise, and drive accurate and scalable strategies for data security, privacy, and governance.

NLP

BigID leverages Natural Language Processing (NLP) methods like Name Entity Recognition (NER) and Deep Learning to add ML based classifiers to automatically identify, categorize, contextualize, label, tag, and classify data. By using ML-driven classifiers, customers can go beyond regular expressions and pattern matching, and easily and automatically identify the data that’s most important to them: including names, geolocations, intellectual property, customer IDs, and more – all while uncovering dark data and data that they don’t know about.

Customizable NLP capabilities reduce costs, time, and resources across data classification and management – improving accuracy, reducing noise, and saving customers’ time and money while accelerating time to insight. These NLP capabilities automatically add and derive context-sensitivity for the data itself via a neural network, to be able to better understand and apply granular and accurate classification.

BigID’s NLP models can also automatically adapt and react to previously unseen data, based on previous analysis – this means that advanced classification can automatically recognize data as names, as ids, or as a specific type of data based on context.

With these models, organizations can get accurate results at scale, customized for their data challenges with capabilities that include:

  • Fine-tune existing NLP classifiers for specific data environments
  • Create additional classifiers for new entity types
  • Extend NLP classifier coverage for additional languages.

Confidence Scoring

BigID leverages patented ML to apply confidence scoring across data sets, accelerating accuracy and (confident) decision-making when populating and establishing a data inventory or data map. These confidence scores validate and verify the accuracy of results.

Patented Classifier Tuning

BigID’s classifier tuning allows human interaction to adjust ML models in real-time, without coding, for improved accuracy in data classification. Classifier Tuning combines human interaction with ML to tweak or guide automated engines for increased accuracy. BigID provides an intuitive, user-friendly interface to interact with automated classifiers to accept or reject classifiers for specific data objects without complex coding.

With BigID’s classifier tuning, organizations can:

  • Increase trust in data for privacy, security, and governance initiatives
  • Adjust AI models to scale work across the data environment
  • Deliver highly accurate results with speed for business advantage

Graph Technology

ML-driven graph technology enables customers to automatically identify data that’s related to one another: whether that’s a set of data that’s related to a single identity, or a set of data that’s all related or referencing the same thing. This is critical for building identity and entity graphs for a number of business purposes – from automation data rights fulfillment to generating customer 360 to automating data lineage.

BigID’s graph tech uniquely correlates and maps related, inferred, and interconnected data across data sources, employing patented machine learning models to classify that data as corresponding to related data sets. This type of ML application can also enrich data – automatically identifying additional elements that aren’t directly related, creating additional value in the data inventory alongside additional context.

It means customers can distinguish seemingly generic data – like an ip address or geolocation – and automatically understand who it relates to; or even connect a product SKU to project plans that are stored in another data source entirely. With this technology, customers can easily understand what data they have, whose data they have, and what it means.

Cluster Analysis

BigID’s differentiated cluster analysis makes it easy to profile data accurately and at scale. This patented ML technique enables customers to automatically identify duplicate, similar, and redundant data.

Cluster analysis is a unique algorithm that automatically groups similar files or data together based on their content. Customers can easily visualize similar and related data – and even determine the original file. It’s developed to be efficient and scalable, while easily mapping similar files together. From there, customers can easily reduce redundant, trivial, and obsolete (ROT) data, reduce their attack surface with data minimization, and accelerate cloud migrations by understanding the data they have.

Document Classifiers

Automatically classify entire documents according to type: BigID’s ML-driven document classifiers easily identify a type of document – from contracts to financial statements to health forms and more. Customers can leverage out of the box document classifiers or easily create their own.

Supervised Learning

BigID applies a novel method for customers to review and tune findings. This enables non technical users to review findings and make simple assessments on the classification accuracy – which will in turn automatically retrain the identification and classification based on expert input for even more consistent results.

Predictive Discovery

Identifying sensitive and regulated data inside unstructured data has always proved challenging: it’s difficult to accurately discover and classify sensitive data at scale, and scanning unstructured data is both resource heavy and slow to achieve results. Traditional methods of scanning enterprise data can take months or years: on average, 10 PB of unstructured data takes up to 14 years with one scanner, or 280 days with 100 scanners.

BigID’s Hyperscan technology is a transformative ML-based approach to scan large volumes of unstructured data for faster time to value and deeper data insight.

Hyperscan intelligently identifies where sensitive data is across a customer’s data landscape, enabling them to discover and classify their sensitive, personal, and regulated data faster and more accurately, while dramatically reducing scan time.

The patented machine learning algorithm discovers hidden relationships between sensitive data in files and metadata, identifying if a file or data set contains sensitive data based on metadata only. By automatically identifying hotspots of sensitive data, this significantly reduces overall scan time required for discovery.

ML-driven Data Intelligence

BigID’s extensive use of ML and AI across the platform refines data discovery, automates actions, and makes it easy to get granular and actionable insight across all types of data, wherever it lives.

With BigID’s ML capabilities, customers can make better decisions with their data, achieve compliance, scale with evolving data privacy and protection landscape, and ultimately reimagine how they manage their data.