Sensitive Data Discovery for Audio and Visual Files: A Technical Overview

Data is becoming the backbone of the modern organization. Today, businesses are generating, processing, storing and managing more data than ever thought possible. As the volume of data continues to skyrocket, the importance of protecting that data rises along with it.   

But there’s a significant hurdle to overcome: a vast portion of this data is unstructured. 

Unstructured data lives in many places, even audio recordings of company meetings. Before delving into why this matters and how organizations can address the challenges of identifying sensitive audio data, let’s explore unstructured data further.   

What is unstructured data? Unstructured data lacks a specific format, structure, or schema; unlike structured data, which follows a well-organized and easily searchable format like databases, unstructured data does not conform to traditional data structures. Therefore, it is more difficult to interpret and more challenging to analyze, store, and manage than traditional data management systems. 

What are the types and characteristics of unstructured data?   

Unstructured data lives in more places than you think, including:  

Text documents  

This type of data can be found in Word documents, PDFs, or plain text files that contain information that is not organized in a structured manner, like articles, reports, and contracts. 

Emails 

Emails represent a significant share of an organization’s unstructured data, including the text of the message and any attachments, metadata, and associated communication threads.  

Multimedia files 

This type of unstructured data includes images, audio files, and videos, often containing vast amounts of information but lacking a consistent format.   

Social media posts 

Companies post massive amounts of social media content, which falls under the unstructured category. Content from social media platforms like LinkedIn, Twitter, TikTok, Facebook, and Instagram is unstructured, and includes text, images, videos, and metadata. 

What is driving the unstructured data explosion?  

The volume of unstructured data is growing at an unprecedented rate, driven by factors such as increased digital communication, work from home (WFH) and the hybrid workplace, bring your own device (BYOD), the Internet of Things (IoT), and the proliferation of social media platforms. 

Organizations can gain valuable insights from unstructured data that can drive growth and improve decision-making — revealing trends, patterns, and correlations that are not easily discoverable in structured data. For example, analyzing customer feedback from social media can help identify areas of improvement in products or services. Unstructured data analysis can also uncover market trends, competitive intelligence, and potential risks, enabling organizations to make data-driven decisions.  

Protecting unstructured data: risks and challenges  

The vast amount of unstructured data also poses potential risks to organizations, including:  

Data breaches 

Unprotected or poorly managed unstructured data is vulnerable to cyber attacks, potentially resulting in data breaches and the unauthorized disclosure of sensitive information.  

Compliance issues and risks 

Organizations must ensure they adhere to data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which require proper management and protection of personal data — including unstructured data.  

Storage and management challenges 

The sheer volume and variety of unstructured data can strain organizational resources, as it requires adequate storage, processing power, and efficient management practices to manage.  

Along with the risk come challenges in protecting the data, which include: 

Lack of standardized format  

The lack of a consistent structure makes it difficult to apply uniform security measures.  

Identification and categorization hurdles 

Identifying and classifying sensitive unstructured data is labor-intensive and time-consuming.  

Limited access controls  

Unstructured data often has minimal or inconsistent access controls, greatly increasing the risk of unauthorized access.  

Increased vulnerability to cyber attacks  

As cybercriminals become more sophisticated and resourceful, unstructured data becomes even more attractive. Given the importance and potential risks associated with unstructured data, it is crucial for organizations to invest in effective strategies and solutions to safeguard it.  

The importance of identifying sensitive data in audio and video files 

 When it comes to unstructured data protection, Concentric AI is at the forefront of protecting sensitive data from risk — no matter where it is or in what format — even audio recordings.  

While the sheer versatility and complexity of audio data presents a unique detection and categorization challenge, Concentric’s solution is the only one capable of this complex functionality.    

Use case: identifying sensitive data in Zoom and Teams meetings 

As the workplace continues its shift towards hybrid, tools like Zoom and Teams are a cornerstone of corporate communication. Organizations use them for a myriad of discussions, from routine team check-ins to high-stakes board meetings. A multinational company may host a critical strategy meeting on Zoom, in which top executives share future growth plans, potential acquisitions, and proprietary processes.  

Given the sensitive nature of these discussions, the transcribed text would contain confidential information that, if mishandled, could jeopardize the company’s competitive edge and reputation.

How Concentric AI identifies sensitive audio and video data  

Without going into too much technical detail, here is a brief overview. 

First, our solution seamlessly transforms audio and video recordings into analyzable text. Our process leverages advanced transcription services that converts voice data into words natural language text and uses noise filtering techniques to weed out background disturbances to optimize transcription accuracy.  

Once the audio multimedia recordings are is turned into text, Concentric AI delves into a deep semantic contextual analysis. By understanding the nuances of the conversations — whether casual chats or official discussions — Concentric can clearly identify potentially sensitive information ranging from confidential project mentions to personal data. 

After sensitive content is identified, Concentric classifies the transformed data from audio and video files based on its significance and sensitivity and categorizes it appropriately — perhaps as ‘confidential’ or for ‘internal use’. Automated policy applications kick in, aligning the data management to the organization’s predefined policies — whether that means encryption, restricted access, or managerial reviews.  

But what truly sets Concentric AI apart is our ability for continuous learning. Much like with text-based data, as our large language models process more audio data, Semantic Intelligence continually refines its algorithms. Concentric AI will adapt to new patterns and consistently improve accuracy, ensuring organizations are always a step ahead in protecting their sensitive audio data from risk.  

Want to see firsthand, with your own data, how you can quickly and easily deploy Concentric AI’s solution and identify sensitive data in your audio files? Book a demo today, and you’ll experience the freedom of classifying all your data — structured and unstructured — without rules, regex, or end-user involvement.