Navigating Data Privacy in AI: Best Practices for Responsible Corporate AI Systems

Though AI presents exciting new opportunities, companies pursuing responsible AI often struggle with AI data privacy vulnerabilities. The IBM Institute for Business Value found that although  82% of respondents say secure and trustworthy AI is essential, only 24% of current generative AI projects have components aligned with responsible AI practices, like AI governance and secure data management.

In the same report, nearly 70% of respondents claimed that “innovation takes precedence over security.” However, companies risk severe damage by putting safety on the back burner. The global average cost of a data breach in 2024 was $4.88 million—a 10% increase over last year and the highest ever recorded. The costs will only continue to mount in coming years, highlighting the importance of preventing AI data breaches through AI data security best practices.

A responsible corporate AI system is a necessary safeguard. It improves a company's infrastructure and creates checks and balances that protect them and their users from data mismanagement and other dangers. With many users now worrying about how their data is handled, these practices are essential in preserving the trust between companies and consumers. 

The Role of Data in AI Management

AI's massive data requirements are public knowledge, as accessible, high-quality data is essential in creating an efficient system. In a 2022 report, IDC analyst Ritu Jyoti discussed how, by adding additional data, companies “can improve accuracy of models and the eventual impact of applications.”

“For example,” says Jyoti, “a consumer's basic demographic data provides a rough sketch of that person. If you add more context … a more complete picture starts to form. With additional insights … the portrait really comes to life.”

With less quality data, an AI’s outputs may be less reliable, overly specific, or vague. As Moses Guttman, CEO and co-founder of ClearML, notes, “Models are only as good as the data put into them.”

What Are Some of the Data Challenges in Corporate AI? 

The vast amount of information needed for datasets often makes companies vulnerable, underscoring the need for AI data security best practices to prevent leaks and mismanagement. Organizations may spread their data across different environments when legacy data systems such as on-premise and private infrastructures can't manage the volume of their datasets. 

However, while more companies move to cloud and distributed environments, new risks open up: IBM’s Cost of a Data Breach Report has found that 40% of breaches involved data stored across multiple types of environments. Additionally, public cloud environments had the highest average breach cost at $5.17 million when breached. 

The need for large databases has also caused many companies to collect information whenever and wherever possible. However, if a company isn’t mindful about where they source this data, their systems will reflect it, raising concerns about transparency and trust. 

For example, OpenAI may be guilty of questionable data sourcing. In 2023, the company was hit with a class action lawsuit that claimed it “stole and misappropriated vast swaths of peoples' data from the internet to train its AI tools.” The New York Times has also filed a copyright infringement lawsuit against OpenAI, alleging that it scraped millions of their articles for its database. 

While OpenAI responded that “training AI models using publicly available internet materials is fair use, companies that follow in their footsteps may face legal trouble. Additionally, these lawsuits are an extension of ChatGPT’s other data concerns, such as their lack of transparency about collecting and handling data.

Why Are Responsible Data Systems Important for AI? 

A responsible AI system provides valuable checks and balances that improve security, instill trust between companies and their users, and enhance efficiency without compromising ethics. In the absence of far-reaching policy, as with many new technologies, decisions about internal policy rest with the organizations themselves. 

Standardized, responsible AI practices for data ensure that companies have guardrails in place for development. As development teams move forward, it’s essential for leaders to distinguish between what can be done with user data and what should be done with it.

These measures ensure privacy-first principles, acting as a safeguard in an age where data breaches and privacy violations can destroy user trust overnight. 

What Makes an AI System’s Data Management Responsible? 

Microsoft, Google, and IBM have all come forward with responsible AI frameworks. While the exact terms differ between companies, they share a few principles: fairness, accountability, transparency, and data protection. 

These principles build trustworthy AI and secure AI systems, protecting both businesses and consumers.

Data Protection & Management

High-quality datasets are the backbone of a strong corporate AI system, making proper data management and protection essential. Not all companies are as careful as they need to be, however. 35% of breaches this year involved “shadow data:" data stored in unmanaged data sources without proper classification, protection, or oversight. 

Shadow data lacks proper management, making it a glaring data privacy threat. When a breach involves shadow data, companies are less likely to realize it has occurred until it's too late. According to IBM, breaches involving shadow data took 26.2% longer to identify and 20.2% longer to contain, taking an average of 291 days total and costing an average of $5.27 million. 

There are other repercussions that companies may face as well, such as IP theft, reputation damage, and even potential lawsuits. Companies can avoid these disasters by carefully classifying, encrypting, and automatically monitoring their data.

Monitoring & Accountability

While companies celebrate AI’s possibilities, many users worry about its implementation. Accenture reports that “only 35% of global consumers trust how AI technology is being implemented by organizations, and 77% think organizations must be held accountable for their misuse of AI.” 

This caution is warranted. In a recent global survey by MIT Sloan, nearly a quarter of respondents reported that their organization has experienced an AI failure. These failures range from brief lapses in technological performance to outputs that endanger individuals and entire communities. 

For example, Rui Zhu, a Ph.D. candidate at Indiana University Bloomington, found that he could extract a list of business and personal email addresses for over 30 New York Times employees from GPT-3.5 Turbo. These findings indicate that, with additional prompting, the AI could reveal more sensitive and personal information about users.

“They do not have the protections on the fine-tuned data,” Zhu noted. 

While it’s always possible for mistakes to fall through the cracks, setting up AI governance and accountability measures can reduce their risk. These include creating responsible AI guidelines and policies, rolling out basic organization-wide training on responsible AI, and forming a governance structure such as an AI ethics committee. 

Some businesses also incorporate automated monitoring and audit trails, allowing them to keep track of any changes or missteps without 24/7 human supervision. 

By implementing these steps, companies create a culture based on accountability and responsible AI practices. 

Compliance

Responsible AI systems may soon go from a suggestion to an outright requirement. The White House published an executive order enforcing AI regulatory compliance to ensure the safe, secure, and trustworthy development and use of AI technology. The order requires “robust, reliable, repeatable, and standardized evaluations of AI systems” and “post-deployment performance monitoring.”

As other governments worldwide follow suit and release their own policies, companies that fail to comply could face sizable fines and other legal trouble. Organizations lose an average of $5.87 million in revenue from a single non-compliance event and may face additional repercussions depending on the severity of the incident. 

The best way to avoid these possible risks is for companies to prepare their systems in advance. Companies’ legal departments should regularly read up on their local and country-specific rules and regulations about AI, and update their systems accordingly as needed. 

How Can an AI Implementation Consultation Help?

Balancing quality, transparency, and privacy in a corporate AI system is challenging, especially in fields with stricter regulations such as healthcare or finances. An AI implementation consultation streamlines this integration process.

FullStack excels in creating customized solutions that meet your company’s needs while incorporating responsible practices. To learn more about implementing AI for your business, download your free Business Leader's AI Handbook today.