A competitive rush for high-end technology is transforming the world of business. AI-driven tools such as ChatGPT, Microsoft Copilot, Gemini, and Claude are now being extensively used in both conventional and innovative business operations, ranging from HR to R&D and customer services. Since such tools can generate quality work in no time, they are extremely desirable for enhancing both productivity and creativity. But when businesses leverage generative AI (genAI), they must ask a basic question too: Where does the data we feed into these platforms end up, and what are the protections for it?
Feeding private corporate data into genAI systems poses severe dangers, ranging from breaches of regulatory compliance and a company’s reputation to the unintentional disclosure of important or proprietary business information. As the demarcation line between internal and external AI services is quickly becoming obsolete, organizations must be sensitive to and circumvent these threats.
What Exactly Is at Stake?
Confidential and Proprietary Information
AI prompts may use internal documentation, system commands, legal language, or performance data to generate accurate and relevant output. Confidential business data and unannounced products could thus become public. In case of logging by the provider, or even a situation where the information is unintentionally revealed to other users, companies may experience IP theft, loss of competitive edge, or even litigation.
Personally Identifiable Information
Inputting names, email addresses, social security numbers, or medical records into genAI systems is a breach of compliance with the GDPR, HIPAA, or CPRA. Organizations are most often not aware of this risk when using AI to generate customer emails, survey documents, or CVs.
Regulatory Non-Compliance
In March 2023, ChatGPT was briefly banned in Italy after the data protection authority decided it was not GDPR-compliant because it did not meet data protection regulations. No sooner had the ban been removed than OpenAI had to make numerous important changes. Regulatory scrutiny by data protection authorities for the use of AI is increasing, especially in sectors such as finance, healthcare, and public administration, where privacy is most essential.
Model Leakage and Hallucination
Though genAI providers claim that only public inputs are used in model updates for enterprise subscriptions, consumer interfaces may not always meet that promise. A related problem is that of model “leakage” — in which AI unintentionally draws on past user prompts — has been documented several times. Accidentally disclosing data, even in anonymized format, can harm an organization’s reputation and put it in legal peril.
Enterprise Case Studies: Mistakes and Fallout
According to a report from The Economist Korea, Samsung engineers employed ChatGPT to fix sensitive code by typing internal information. As the data was not secure enough, a complete organizational review was undertaken instantly. Samsung acted rapidly by prohibiting all usage of ChatGPT throughout its departments.
Additionally, a few of the largest investment banks and law firms have taken strong steps to limit or encapsulate AI use by arguing that data guidelines and client privilege are at the forefront. In 2023, JPMorgan restricted employees from using ChatGPT. Such actions offer evidence of a sizable difference between business-grade security elements and how AI is practically being applied.
Also in 2023, cloud security provider Cyberheaven analyzed ChatGPT usage for 1.6 million workers at companies across different industries that use their products. Despite a growing number of companies outright blocking access to generative AI, Cyberheaven detected a record 7,999 attempts to paste corporate data into ChatGPT per 100,000 employees. Simply put, the report showed that 11% of the data employees paste into ChatGPT is confidential.
Relevant Actions for Keeping Your Data Safe
Mitigating these risks involves organizations advancing beyond awareness programs and developing an enforceable and sound AI governance strategy. Key actions include:
Defining Acceptable Use Policies
Companies should outline what kinds of data should be allowed in AI systems and which should never be used there. They should also develop guidelines for profession-specific use within the legal, HR, engineering, and marketing domains.
Deploying Enterprise-Grade AI Solutions
AI vendors increasingly provide enterprise solutions that ensure data will be not be stored, data in transit is encrypted, and large language models run in walled, secure environments. Microsoft Azure, OpenAI and other private deployments of open models, such as Llama and Mistral, give organizations greater control.
Auditing and Monitoring Usage
Employing data loss prevention solutions together with AI prompt monitoring in real time means that companies can detect and avoid the movement of sensitive information to external systems. Ongoing use of monitoring allows identification of preferred practices and identification of riskier departments.
Training and Educating Employees
Building a security-minded company also means possessing the right organizational culture and the proper tools. Organizations should show employees concrete, real-world examples of problems that can be caused by pasting the wrong data into AI tools.
Reviewing Vendor Contracts
Checking that the terms and conditions of AI vendors’ contracts can help companies keep their data safe. They should be careful to look at the wording in the contract to include data ownership, storage and retention policies, compliance certifications (ISO 27001, SOC 2, etc.), and incident response timelines.
Feeding Data into GenAI: What the Regulators Think
The EU AI Act classifies AI systems by their risk level and submits higher-risk ones, such as those that influence HR decisions or operate with personal data or safety systems, to stricter regulations. Under the act, companies utilizing generative AI for hiring, finance, or customer analytics must be transparent, protect data, and provide controls through human review—all of which can affect their business considerably.
NIST’s AI Risk Management Framework and other state-level privacy laws cause US organizations to handle AI with the same seriousness as their other digital assets.
Organizations that act quickly to align their generative AI operations with these standards will stand out, both in compliance as well as in the trust they build with their consumers.
Conclusion: Trust Is the Precious Asset in the AI Era
Generative AI can indeed live up to its hype. It makes breakthroughs possible, automates routine work, and creates new ways of working more efficiently. But trust — the glue between an organization and its most significant stakeholders — is the essential enabler of long-term digital transformation.
If a business relies on AI instruments beyond a rule-based framework, then trust will collapse and risks will increase. Using artificial intelligence wisely means safeguarding data. Secure infrastructure deployments, the clear definition of usage policies, and promoting responsible behavior from employees enable organizations to be viewed as tech pioneers and data guardians.
