Generative AI takes companies into new areas of efficiency, innovation and productivity. Like the technological innovations that came before it—from the Industrial Revolution to the advent of the Internet—businesses in the age of artificial intelligence will continue to adapt to take advantage of the most efficient processes possible.
If a company’s data is unwittingly shared with third parties, or even with third parties after using artificial intelligence tools, it can not only jeopardize customer trust but also weaken its competitive position.
The problem of data security
The business world is now firmly in the age of artificial intelligence and companies are undoubtedly reaping the tangible benefits of this technology. But companies also face significant risks if they misuse this technology. It is increasingly common for AI vendors to mislead their customers when using their data.
For example, OpenAI was fined €15 million for fraudulently processing European users’ data when training its AI model, while the SEC fined investment firm Delphia for misleading its clients by falsely claiming that its AI used their data to create an “unfair investment advantage”.
These recent high-profile cases of breach of trust are ringing alarm bells for businesses. Concerns are growing that artificial intelligence companies are acting fraudulently.
As a result, potential customers are rethinking their use of AI and are hesitant to share their personal data with suppliers. Some companies are even hesitant to invest in artificial intelligence tools.
According to a global survey conducted by KPMG earlier this year, more than half of people are reluctant to trust AI tools, reflecting a conflict between their apparent benefits and perceived threats, such as concerns about their data.
This raises an important question for AI vendors: how can they increase trust in AI and data security?
The road to trust: data localization and transparency
For AI vendors, honesty means transparency—it’s a crucial first step in rebuilding trust. By being open about who data is shared with and what it is used for, people are informed before entrusting their valuable information to AI applications.
This is important regardless of whether the customer agrees with the policy or not.
Giving companies a transparent overview also extends to clarity around data placement. Showing the physical or geographic location where data is stored and processed eliminates the uncertainty and speculation associated with AI.
Providing customers with data usage information reduces fear of the unknown and emphasizes the “invisible” space.
A combination of transparency and residency goes beyond attempts to rebuild trust. From a compliance perspective, it helps, for example, suppliers to take a stronger position.
The purpose of the long-awaited Data Use and Access Act is to enforce disclosure of data sources used by artificial intelligence. By refining these procedures before such laws are implemented, providers can position themselves to benefit from future policy changes.
By implementing these practices, customers can be confident that their data is protected against the risk of fraudulent activity. However, providers must also ensure that this data is protected from other threats.
Ensure data security
Transparency helps build trust between organizations and their customers, but it’s only the first step. Another element in maintaining trust is data security, where cyber security plays a critical role.
The combination of outdated IT infrastructure, insufficient cybersecurity funding and storage of valuable data are key issues driving most cyber attacks.
To show customers that unauthorized access to their data is not an option, AI vendors need to rethink their security systems. This includes implementing security measures such as multi-factor authentication (MFA) and data encryption that prevent illegal access to important customer databases.
Regular updating and strengthening of security systems also prevents malicious actors from identifying and exploiting potential vulnerabilities.
Businesses naturally want to leverage AI’s unparalleled capabilities to increase operational efficiency. However, AI adoption will decline if users cannot trust providers to protect their data, regardless of how transparent their use cases are.
Building responsible AI ecosystems
As AI capabilities continue to evolve and become more integrated into daily operations, the responsibilities of AI vendors continue to grow. When they neglect their obligations to protect customer data, either through bad behavior or by threatening external actors, a viral element of trust between the parties is destroyed.
To strengthen customer trust, AI vendors must significantly improve data localization and transparency, as this demonstrates a serious commitment to the highest ethical standards for current and future customers.
In addition, it ensures that advanced security protocols are clearly considered the foundation of all operational and data protection efforts. Ultimately, this commitment creates trust in the organization.