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Summary

  • Generative AI boosts productivity but poses data breach risks if misused.
  • As the adoption of generative AI continues to expand, organizations must act decisively to mitigate risks.
  • To address these growing risks, employee education about AI systems should be a top priority

Generative AI boosts productivity but poses data breach risks if misused. Organizations must set clear AI usage policies, educate employees, and adopt secure enterprise-grade tools. Proactive steps, like restricting sensitive inputs and enhancing cybersecurity, ensure innovation without compromising data security.

Generative AI tools are rapidly reshaping the way we work. But the promise of enhanced productivity also comes with a significant risk: improper use of these tools can lead to catastrophic data breaches.

As the adoption of generative AI continues to expand, organizations must act decisively to mitigate risks. This includes establishing clear policies around AI use, investing in employee training to raise awareness about data security, and adopting enterprise-grade AI solutions with built-in safeguards. The stakes are high, but with proactive measures, businesses can harness the benefits of generative AI while safeguarding one of the most critical pillars of any modern organization: their data.

Unlocking innovation and unleashing risk

Generative AI tools have become widely accessible, making them indispensable for businesses and individuals alike. With easy-to-use interfaces and freemium models, these tools are now available to anyone with an internet connection, democratizing access to powerful AI-driven capabilities. 

According to Salesforce research, nearly half of the US population surveyed is using generative AI as of 2024. Yet many remain unaware of or indifferent to its risks. While this accessibility has opened doors to innovation and productivity, it has also significantly increased the likelihood of misuse, particularly in professional environments. 

As generative AI tools become as ubiquitous as email, the potential for misuse cannot be ignored. Organizations must recognize that the convenience of these platforms comes with significant risks, and they must act proactively to establish boundaries, educate employees, and ensure that data security remains a top priority.

To address these growing risks, employee education about AI systems should be a top priority, including clarifying data retention policies, and implementing robust safeguards to minimize the chances of accidental misuse. Awareness and proactive measures are the first line of defense in protecting sensitive information in the age of generative AI.

Why generative AI needs guardrails in the workplace

Generative AI systems operate by absorbing vast amounts of input data to continuously refine their performance. Every query or piece of information entered into these systems becomes part of their training data, either temporarily or permanently, depending on the model and its configurations. This data absorption is fundamental to how these tools function, enabling them to improve over time. However, it also introduces a significant risk: sensitive or confidential information entered into the system can inadvertently become accessible to others.

In many organizations, generative AI usage remains unsanctioned, and employees often adopt these tools without proper oversight or understanding of the risks, using personal accounts to solve work-related problems, optimize tasks, or generate content. Without organizational policies or training, employees may not realize the potential for their inputs to be shared or accessed by others, leading to unintended data exposure. This lack of control creates an environment where sensitive data can easily be entered into AI systems without safeguards, as seen in the Samsung incident. 

For instance, proprietary business data, customer information, or intellectual property input into generative AI tools could potentially surface in unrelated outputs or even be accessed by malicious actors exploiting vulnerabilities in the platform. This risk is exacerbated by the sheer scale of these tools’ user bases; ChatGPT boasts 300 million weekly active users as of December 2024. With such widespread use, these models are an attractive target for cybercriminals, who can use open-source AI platforms or APIs to create sophisticated phishing schemes, automate ransomware attacks, or mine sensitive data input by unsuspecting users. 

Amazon was early to address the risks of generative AI misuse, as reported in January 2023, when it cautioned its employees against sharing confidential information with tools like ChatGPT after observing instances where the AI’s responses closely resembled internal Amazon data.

Major financial institutions like Citigroup, JPMorgan Chase, and Bank of America have also restricted their employees’ use of ChatGPT, either partially or entirely. These measures reflect growing concerns about data security in industries where confidentiality is paramount. Financial firms handle vast amounts of sensitive client information, proprietary strategies, and regulatory data, making the potential risks of unmonitored AI use especially significant. 

Unlike traditional software solutions where data is stored in private databases with clear security protocols, many generative AI systems operate on cloud-based infrastructure with less transparent data retention policies. As a result, even well-meaning users may unknowingly contribute sensitive information to an ecosystem that lacks adequate protections against accidental sharing or targeted attacks.

In 2023, Samsung was exposed to three separate data leaks that could be traced back to employee use of ChatGPT. Despite urging their employees to safeguard internal data when using AI tools, multiple Samsung engineers entered sensitive company information into ChatGPT while attempting to streamline tasks like debugging, summarizing meetings, and optimizing workflows. Since ChatGPT absorbs and uses all inputted data to train its algorithm, these seemingly well-intentioned actions ultimately resulted in the exposure of confidential intellectual property. 

Samsung’s experience serves as a cautionary tale, emphasizing how employee misuse of generative AI tools can lead to unintended consequences. Research from Deloitte suggests that the global average cost of a data breach increased 10 percent year-over-year, reaching nearly $5 million in 2024.

Without understanding the risks, employees are left vulnerable to making costly mistakes, like the Samsung engineers who unknowingly exposed proprietary data in their attempts to streamline workflows.

6 steps to mitigate AI misuse

By proactively establishing clear guidelines, educating employees, and implementing robust security measures, businesses can harness the benefits of generative AI without compromising data integrity.

1. Establish clear AI usage policies

Organizations must create and enforce detailed policies regarding the use of generative AI tools. These policies should:

  • Specify the types of data that can and cannot be entered into AI platforms. For instance, proprietary information, personal customer details, and internal communications should be strictly off-limits.
  • Identify approved platforms for AI use, ensuring they meet security and privacy standards.
  • Require employees to obtain authorization before using AI tools for work-related tasks.

Clear communication of these policies is critical. Employees need to understand not only the rules but also the rationale behind them to encourage compliance.

2. Provide training and awareness programs

Regular training programs are essential to equip employees with the knowledge they need to use generative AI tools responsibly. These sessions should:

  • Highlight the risks associated with data input, including the potential for data retention and sharing.
  • Offer practical guidance on safe usage, such as anonymizing data when interacting with AI tools.
  • Address common misconceptions, emphasizing that generative AI platforms are not inherently secure.

Training should be an ongoing effort, updated regularly to reflect the latest developments in AI technology and emerging threats.

3. Monitor and control tool access

Restricting access to generative AI tools can significantly reduce the likelihood of misuse. Organizations should:

  • Limit usage to approved AI platforms with enterprise-grade security measures.
  • Implement access controls, ensuring that only authorized personnel can use AI tools for specific tasks.
  • Monitor tool usage to detect any unauthorized activity or potential misuse.

By centralizing control over AI access, businesses can maintain better oversight and reduce risks associated with unregulated use.

4. Leverage AI safeguards

Enterprise-grade AI tools offer advanced security features designed to protect sensitive data. Organizations should:

  • Select vendors with strong data privacy practices, including encryption, limited data retention, and compliance with regulatory standards like GDPR.
  • Use tools that allow for private deployments, ensuring that data remains within the organization’s control.
  • Regularly review and update vendor contracts to align with evolving security requirements.

Careful vendor selection is critical to ensure that third-party AI solutions meet the organization’s security and privacy standards.

5. Implement robust security measures

Beyond AI-specific precautions, organizations should strengthen their overall security posture by:

  • Encrypting sensitive data to prevent unauthorized access.
  • Establishing multi-factor authentication and role-based access controls.
  • Conducting regular audits to identify and address vulnerabilities in their systems.

6. Stay informed and proactive

As generative AI technology evolves, so do the risks associated with its misuse. Organizations must remain vigilant, staying informed about emerging threats and adapting their strategies accordingly. Proactive measures—such as participating in industry forums, attending AI security conferences, and consulting with cybersecurity experts—can help businesses stay ahead of potential vulnerabilities.

By implementing these steps, organizations can strike a balance between leveraging the capabilities of generative AI and safeguarding their sensitive data, creating a secure and responsible environment for innovation.

Balancing innovation and security in the AI era

The rise of generative AI tools presents a double-edged sword for organizations. While these tools offer incredible potential to enhance productivity and streamline operations, their misuse can lead to severe data security breaches with far-reaching consequences. Organizations cannot afford to remain passive in the face of these risks.

To protect sensitive data and intellectual property, businesses must act proactively. Implementing strict AI usage policies, providing regular training programs, and leveraging enterprise-grade AI tools with robust safeguards are essential first steps. By fostering a culture of awareness and accountability, organizations can empower employees to use these powerful tools responsibly while minimizing potential vulnerabilities.

Generative AI holds immense promise for the future, but with great power comes great responsibility. The risks are real, but they are manageable with informed, proactive measures. In the AI age, awareness and action are the first line of defense against data breaches, ensuring that innovation and security go hand in hand.

Curious about how Polymer safeguards businesses against generative AI data breaches? Watch our video on DLP for AI.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.

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