Audio Redaction: How to Detect, Classify and Remediate Sensitive Data in Audio Files ?

The use of audio files, including voice call recordings, has become increasingly prevalent. However, with the rise in data privacy concerns, it is essential to ensure that sensitive information within these audio files is properly protected.

This article will explore the challenges of identifying and redacting sensitive data in audio files, the various sources of unsafe audio files, the potential regulatory fines for failing to redact such files, and different methods for implementing redaction. Additionally, we will discuss the disadvantages of using audio redaction software and why choosing a Data Loss Prevention (DLP) solution may be a better option.

Sources of Unsafe Audio Files

Unsafe sensitive audio files can originate from various sources, including:

 1. Voice Call Recordings: Businesses often record voice calls for quality assurance, training, or legal compliance purposes. If these recordings are not properly redacted, sensitive information shared during the calls, such as financial details or personal identifiers, can be exposed.

 2. Voice Messages Shared Across SaaS Apps: With the increasing use of SaaS applications like Slack, customer support tools like Zendesk, Salesforce, Intercom, HubSpot, Email, voice messages are commonly shared within teams and across different platforms. If sensitive information is shared in these voice messages, failing to redact the content can lead to data breaches or privacy violations.

  3. Audio Files Shared in Endpoints: Audio files can be shared through various endpoints, such as email attachments or file-sharing platforms. If these files contain sensitive information, it is crucial to redact the data to prevent unauthorized access.

Need for Audio Redaction

Redacting voice call recordings means removing or obscuring sensitive information to protect the privacy and security of the individuals involved. This is crucial, especially when sharing recordings through customer support, email, Slack, or file-sharing platforms.

For example, consider a customer support call where customers provide their credit card information for billing purposes. If this call is recorded and shared without redaction, the sensitive data may be exposed, leading to potential identity theft and financial fraud. Redacting the recording ensures that only the necessary information is retained while sensitive data remains protected.

Challenges of Identifying and Redacting Sensitive Data in Audio Files

Identifying and redacting sensitive data in audio files can be challenging due to several factors. Here are some of the key challenges:

1. Speech Recognition

Unlike text-based documents, audio files require speech recognition technology to convert speech into text for analysis and redaction. However, accurately transcribing speech and identifying sensitive information within it can be complex, especially considering variations in accents, background noise, and other factors.

2. Contextual Understanding

Identifying sensitive data in an audio file also requires a contextual understanding of the conversation. For example, in a voice call recording, it is crucial to differentiate between sensitive information, such as credit card numbers or social security numbers, and non-sensitive information, like product names or general conversation.

3. Language Support

Audio files can contain conversations in different languages, making it necessary for redaction tools to support multiple languages and accurately identify sensitive information across various linguistic contexts.

Are There any Fines if Audio or Voice Call Recordings are not Redacted?

Yes, there can be fines and penalties if audio messages/voice call recordings containing sensitive or personal information are not redacted, leading to a data breach or privacy violation. The fines and penalties depend on the jurisdiction and specific regulations governing data privacy in your area.

Here are some examples of regulatory compliances that impose fines for mishandling audio files:

1. General Data Protection Regulation (GDPR): 

Under the GDPR, organizations can face fines of up to €20 million or 4% of their annual global turnover, whichever is higher, for non-compliance with data protection requirements. Mishandling personal data in voice call recordings can result in significant fines.

2. California Consumer Privacy Act (CCPA): 

The CCPA imposes fines for privacy violations related to mishandling personal information, including audio files. The fines can range from $2,500 to $7,500 per violation, depending on the nature of the violation.

To avoid fines and protect the privacy of individuals involved, it's essential to follow best practices for redacting sensitive information from voice call recordings, adhere to relevant data protection laws and regulations, and have a robust data security plan in place.

Methods for Implementing Audio Redaction

To effectively redact sensitive data in audio files, consider the following methods:

1. Manual Redaction: 

Manual redaction involves listening to the audio file and manually removing or obscuring sensitive information. While this method can be accurate, it is time-consuming and prone to human error. It may not be practical for large volumes of audio files.

2. Automated Redaction: 

Automated redaction utilizes advanced technologies, such as speech recognition and natural language processing, to automatically identify and redact sensitive data in audio files. This method is faster and more efficient than manual redaction, but it may still require manual review to ensure accuracy. DLP (Data Loss Prevention) solutions like Strac offer comprehensive data protection by automatically identifying and redacting sensitive data in various file types, including audio files.

DLP solutions leverage machine learning algorithms and predefined rules to detect and redact sensitive information, ensuring compliance with regulations and reducing the risk of data breaches.

See Strac in Action

Strac provides APIs and No-Code solutions to automatically detect and redact sensitive data in a voice call recording.

1. Remove voice call recording from SaaS apps & replace with a secure link

Let's take an example: If a customer submits a voice call recording that contains credit card details or customer PII like billing address, name, and identification details, Strac will remove the recording. Strac has built-in integrations with Slack, Zendesk, Intercom, Gmail, Office 365 ,one drive and more.

2. ‎Redact sensitive data elements from voice call recording

You can also configure Strac where only sensitive data elements in the voice call recording are redacted. In that case, the original voice call recording will be removed and a new voice call recording that has redacted information will be uploaded.

Sample Request

curl --location --request POST 'https://api.test.tokenidvault.com/redact' \
--header 'X-Api-Key: <your API key>' \
--header 'Content-Type: application/json' \
--data-raw '{
  "document_id": "doc_65T78zexKxbqUz34gbLGiX",
  "document_type": "generic"  
}'

Sample Response

{
 "detectedEntities": [
   {
     "type": "SOCIAL_SECURITY_NUMBER",
     "token_id": "tkn_lvCJl350FVc4WMmYaPTjquum"
   }
 ],
 "redactedContent": "Hello, please process the user with SSN tkn_lvCJl350FVc4WMmYaPTjquum"
}

If you'd like to replace tokens within redactedContent, you can use the below regular expression:

redactedText.replace(/tkn_[A-Za-z0-9]+/g, "[REDACTED]");

Audio Redaction Software: Why Choose a DLP over Redaction Software?

While audio redaction software may seem like a viable option for redacting sensitive data in audio files, there are some disadvantages to consider. Audio redaction software typically focuses solely on redacting audio files and may not offer the same level of comprehensive data protection as a DLP solution. Here are some reasons to choose a DLP solution over audio redaction software:

1. Comprehensive Data Protection

DLP solutions offer protection for various file types, including audio files, as part of a broader data protection strategy. They can detect and redact sensitive information in real-time, both at rest and in motion, across multiple platforms and applications.

2. Advanced Detection Capabilities

DLP solutions leverage advanced technologies, such as machine learning and natural language processing, to accurately identify sensitive data in audio files. They can adapt to new patterns and evolving threats, ensuring that sensitive information is consistently protected.

3. Integration with Existing Systems

DLP solutions can integrate with existing systems and applications, such as SaaS apps, email clients, file-sharing platforms, endpoints, cloud apps or communication tools, to provide seamless and automated data protection. This integration simplifies the implementation process and reduces the need for additional software.

Automated Sensitive Data Identification and Redaction

Strac is a sophisticated DLP (Data Loss Prevention) solution designed to protect data in all states: in use, in transit, or stored on endpoint devices, saas and cloud applications.

Easy-to-Use No-Code Scanner

The no-code scanner feature of Strac simplifies integration and usage. It enables quick setup and deployment without requiring deep coding skills. The scanner effectively oversees and examines data transfers to prevent accidental exposure of sensitive information.

Limiting Physical Data Exchanges

A key capability of Strac is its control over physical data exchanges, including printing and USB device usage. This is vital for blocking unauthorized physical data transfers.

All-Encompassing Data Security

Strac's all-around data security approach meets rigorous standards like PCI, HIPAA, SOC 2, GDPR, and CCPA. For organizations dealing with sensitive data, this ensures they comply with legal and ethical guidelines.

Compatibility Across Platforms

Strac boasts compatibility with various operating systems and platforms, ensuring easy integration in diverse SaaS environments. 

API for PII Redaction

The PII Scanner and Redaction API in Strac automatically identifies and redacts sensitive data, safeguarding personal information during transfers. This is crucial for privacy and confidentiality, especially when handling large data sets.

Act before a security incident occurs. Schedule a demonstration to discover more.