Top 5 risks of not redacting sensitive business information when machine learning

Safeguarding sensitive information when machine learning is more than a best practice—it’s a necessity. Yet, many businesses overlook a critical component of data security when training models: sensitive data redaction. This oversight could lead to significant legal, financial, and reputational risks. Here, we explore these perils and introduce Tonic Textual, a synthetic data platform designed to protect your business from these very risks when working with machine learning models.

In today’s rapidly evolving technological landscape, especially with the advent of generative artificial intelligence, data privacy has catapulted from a back-office concern to front-page news. A business’s data is one of its most valuable assets, so it’s no wonder that protecting this asset is paramount to the success of the organization. Using sensitive data with large language models can lead to model memorization where a machine learning model with large parameter capacity remembers data. this can then lead to sensitive data leakage when the LLM is prompted because the LLM may regurgitate sensitive information it has memorized Failing to protect sensitive data when machine learning has the potential to break a business. For many smaller businesses, a data leak is one of the only things that could destroy the company’s future. At the heart of securing sensitive data is the practice of redacting sensitive information from data stores and data types such as free text, documents, and PDFs—a critical step that’s often overlooked with dire consequences.

1. Legal and Compliance Risks

The legal landscape around data privacy is both vast and varied, encompassing laws like the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations mandate stringent handling of personal and sensitive information. Failing to properly redact this data can lead to legal battles, hefty fines, and a tangled web of compliance issues. It’s a legal minefield that no company wants to navigate unprepared. 

2. Financial Implications

The financial fallout from a data breach can be staggering. Direct costs such as fines and litigation fees are enough to set a company back. Facebook was ordered to pay $5 billion in 2019, Amazon was ordered to pay $866 million in 2021, and Equifax forked over $700 million for its data breach in 2017.  Now, these are some of the largest companies in the world. Just think about what might happen if a data breach happened to a startup or pre-IPO company.  While direct fees can be cumbersome, the indirect costs, including remediation efforts, increased insurance premiums, and even potential ransom payments, can cripple a company’s finances. Protecting your business’s sensitive data is critical to ensuring the long-term financial viability of the company.

3. Reputational Damage

In business, it takes years of hard work to earn your customers’ trust and to establish credibility, but it can take a minute to lose it all. A single incident involving exposed sensitive information can shatter customer trust and tarnish a brand’s reputation for years. Recovering from such reputational damage is a long, uphill battle that many businesses struggle to overcome. It’s an intangible yet invaluable asset that’s easily lost and hard to regain.

4. Operational Disruptions

The ripple effects of a data breach can cause operational disruptions that stretch far beyond the IT department. Resources are diverted to manage the crisis, impacting productivity and halting normal business operations. This diversion can stall growth, innovation, and the execution of strategic initiatives, setting a business back significantly.

5. Threat to Intellectual Property and Sensitive Business Information

In the competitive landscape of business, intellectual property (IP) and sensitive information are invaluable assets. Even though these don’t always include Personally Identifiable Information (PII) or Personal Healthcare Information (PHI), this type of data may contain important trade secrets and other business know-how that is confidential and important to keep secure. Without proper protections such as document redaction, this information may leak internally or externally, potentially endangering a company’s market position and future viability. This risk not only affects the company’s bottom line but also its strategic positioning and long-term success.

Safeguarding Your Data with Tonic Textual

One well-known technique to protect sensitive data is document redaction, the process of censoring or obscuring a part of a text for security purposes. Now imagine that you have hundreds of thousands or even millions of documents of different types, shapes and styles. First, identifying all sensitive words and then actually redacting them becomes incredibly complex and cumbersome. Enter Tonic Textual—a modern redaction and synthetic data platform that uses proprietary models to identify, redact, and synthesize sensitive data from your free-text, PDF, and Word documents at scale. Tonic Textual has the ability to refill redacted parts with contextually relevant synthetic data, ensuring data privacy is maintained, while the utility of the data for secure document sharing, ETL and data pipelines, and model training remains intact.

This dual focus on privacy and utility positions Tonic Textual as a productivity booster for businesses aiming to uphold the highest standards of data privacy while maintaining the speed and agility of their operations. By safeguarding data from breach, exfiltration, and model memorization, Tonic Textual empowers businesses to navigate the complexities of the modern data landscape confidently.

Ready to Protect Your Data? Dive deeper into how Tonic Textual can transform your approach to data privacy and protection. Visit www.tonic.ai/textual to learn more and try redacting your documents for free today.