Beyond Trainable Classifiers with Concentric AI

Modern enterprises face a daunting challenge: managing the explosive growth in data that multiplies exponentially. As more of this data transitions to the cloud, organizations are handling a myriad of data types—from intellectual property and financial information to business confidential information and regulated PII/PCI/PHI data—all within increasingly intricate environments. 

 Saying that protecting this data is a business-critical function is an understatement. But how can data be protected if it isn’t identified and classified properly?  

 That’s where data classification comes in. There are numerous ways in which organizations can classify data, and we’ve discussed them here.  

 Many data classifications rely on trainable classifiers to classify data. In this article, we will explore how they work, what their limitations are, and why Concentric AI has a better solution for data classification. 

 The rise of trainable classifiers 

 Trainable classifiers allow organizations to train models on custom data, enabling them to recognize and categorize specific types of content based on user-defined criteria. Whereas pre-trained classifiers come with predefined categories, trainable classifiers offer the flexibility for users to define their own categories based on unique needs. 

 Setting up trainable classifiers typically involves a sequence of four to five steps: 

1. Data Collection: Gather labeled data with examples for each desired category. 
2. Training: Use the data to train the classifier, teaching it to identify category-specific patterns. 
3. Validation: Test the classifier on newly labeled data to ensure accuracy and prevent overfitting. 
4. Deployment: Deploy the validated classifier to categorize new data based on learned patterns. 
5. Continuous Learning: Some classifiers adapt over time, refining accuracy based on feedback. 

Trainable classifiers are useful in scenarios where the categories of interest are specific to a particular organization or domain and may not be applicable to general pre-trained classifiers. 

Limitations of trainable classifiers 

While trainable classifiers offer flexibility, there are inherent sets of challenges. First, the training process requires a substantial amount of labeled data. Plus, the accuracy of that classifier is only as good as the data it’s trained on.  

 More importantly, maintaining and updating these classifiers can be resource-intensive, especially as data evolves and new categories emerge.  

 There’s also the risk of overfitting — a situation where the classifier performs very well on the training data but fails to classify new, unseen data. 

 Concentric AI: Beyond traditional and trainable classifiers 

 To address the mounting challenges of managing massive amounts of diverse data types — from intellectual property and financial records to regulated PII/PCI/PHI data — organizations require a solution that can identify high-value data by categorizing it into specific, meaningful categories. 

 While traditional and trainable classifiers certainly have a place in the data classification marketplace, Concentric AI takes a more holistic approach to address their limitations and challenges. 

 In today’s cloud-first business landscape, traditional data classification methods often fall short. They rely on broad categories, manual processes, and struggle to capture the nuances between data types. 

 Concentric AI’s Semantic Intelligence solution uses advanced machine learning technologies to autonomously scan and categorize data with context. This eliminates the need for rules, regex patterns, or upfront policies, ensuring accurate and efficient data classification. 

 With Concentric AI, there is no need for customers to provide our data models with good and bad data sets or have to validate the results. Simply, point and connect to data repositories and get a highly accurate, contextual view into your data in minutes. No need for training or validation. 

 Our data classification process is so precise that we have introduced concept of archetypes, a specific type of data or file containing sensitive information. Concentric AI can identify the exact type of document, be it a business insurance claim or an auto insurance policy, allowing for more precise risk assessments and data management strategies. 

 When it comes to data classification, Concentric AI offers three key benefits: 

 Autonomous Data Discovery: Concentric AI identifies where your sensitive data resides across various platforms (cloud, on-premises, structured and unstructured) providing a comprehensive overview, with semantic context. 

 Centralized Data Classification: With Concentric AI, security teams can classify sensitive data centrally without relying on end-users or complex rule writing. 

 Integration with Existing Frameworks: Concentric AI seamlessly integrates with leading frameworks, enhancing the effectiveness of existing data security plans. 

 Concentric AI is revolutionizing the way organizations approach data classification. With our unique approach to data classification, we offer a level of granularity and precision unmatched in the industry.  

 Want to see firsthand — with your own data — how Concentric AI can quickly and easily be deployed to identify, classify, and remediate risk for all your sensitive data? No rules, no regex, complex policies, or upfront work for your employees or security team. Book a demo today.