Navigating AI Compliance: Should Employees Be Permitted to Install AI Tools?

Article Summary:

The blog highlights the importance of AI compliance policies in organizations, focusing on the risks associated with employees installing public GenAI tools that could expose sensitive corporate data and violate regulatory requirements.

AI Compliance and Risk Management Key Points:

• AI tools increase productivity but pose compliance risks: Many organizations restrict the use of public AI tools (e.g., ChatGPT, Bard) due to potential data privacy, security, and regulatory violations.
• Outdated security policies aren’t enough: Traditional methods like banning AI tools or holding employees accountable are ineffective in today’s fast-paced AI-driven work environment.
• Modern AI compliance policies are essential: Companies should implement automated, dynamic solutions to monitor, detect, and block risky AI apps and browser extensions in real time.
• Industries taking action: Financial institutions, healthcare providers, tech companies, and government agencies have already implemented AI-free policies to protect sensitive data and comply with regulations like GDPR and HIPAA.
• Best practices for AI risk management: Enforce clear AI usage policies, deploy enterprise-grade AI tools, implement DLP solutions, train employees, and adopt continuous monitoring and auditing to safeguard corporate data.

GenAI tools are helpful, no doubt, speeding up routine tasks and enabling much higher levels of productivity. So, why would some organizations want to develop a company policy preventing installation of browser extensions and apps that may be inadvertently granting consent to applications to send corporate data to third-party LLMs. Security teams understand this external connection can put the entire enterprise at risk without the end-user even realizing it.

As artificial intelligence (AI) becomes increasingly integrated into business operations, ensuring compliance with applicable laws and regulations is crucial. AI compliance refers to the process of verifying that AI systems adhere to legal, ethical, and privacy standards throughout their lifecycle. This includes ensuring that data used for training AI is legal, unbiased, and non-discriminatory.

Despite the benefits of AI tools in enhancing productivity, security teams must be vigilant. Some tools may inadvertently expose organizations to risks, allowing third-party applications access to corporate data. Therefore, it is essential to implement policies that can differentiate between safe and potentially harmful AI applications.

Is there a way to implement policies that can automatically allow cloud apps that don’t pose a risk, while blocking those likely to compromise corporate secrets, violate privacy laws, and create legal risks?

We’ll walk through an overview of the issue, examples, and best practices for data protection:

What is AI Compliance?

AI compliance is the process of ensuring that AI-powered systems adhere to applicable laws and regulations, focusing on ethical and responsible use of data and technology.

Reasons Companies May Want set up AI Regulatory Compliance policies:

• Legal Protection: To avoid legal repercussions, fines, and liabilities associated with non-compliance with regulations such as GDPR or the EU AI Act.
• AI Governance Ethical Standards: To ensure an AI system is used ethically, preventing discrimination, manipulation, and privacy invasions.
• Public Trust: To build and maintain trust with customers and stakeholders by demonstrating responsible and transparent use of AI technology.
• Risk Management: To mitigate risks associated with biased or inaccurate AI outcomes that could harm individuals or the organization’s reputation.
• Competitive Advantage: To position the organization as a leader in ethical AI practices, potentially attracting more clients and partners focused on sustainability and social responsibility.

AI compliance is critically important for several compelling reasons that extend beyond mere adherence to regulations.

An AI Regulation policy serves as a foundational element for building trust with consumers and stakeholders; when organizations demonstrate a commitment to responsible AI practices, they instill confidence in their use of technology. This trust is essential in an era where consumers are increasingly concerned about privacy, data security, and ethical implications of AI.

The Challenge With Outdated Policy Processes and Risky AI Tools

In the past, the quickest way to prevent installation of risky applications and extensions was to simply update policies to disallow them, holding individual employees accountable with threats of serious consequences for installing disallowed apps. But this created new headaches  in the compliance process for security teams.

1. Cloud discovery is challenging with legacy tools, even if they are perceived as robust security solutions. The reason for this is they weren’t built to automatically find, classify, analyze, and apply policies to every app that is accessing your core cloud workspace.
2. Existing security tools are often outdated. Most tools used in the enterprise to apply conditional access policies were built to mange user behavior, not application behavior. You need tools that can apply app protection policies, not just access control.
3. Modern employment contexts apply tremendous market pressure to your employees to use AI tools: The AI workforce revolution is here, and your employees are under incredible pressure to do whatever they have to in order to compete and keep their jobs. After all, they are installing these apps for reasons related to aspects of employment. For example, if employees comply with AI-free policies, they face the prospect of failing front-line managers’ expectations for them to be lightening fast and unfailing (like a digital tool is). If they are deemed expendable or replaceable by a tool, as is happening in record numbers, they are at risk. This increases the likelihood of end users willingness to violate policy and install generative AI applications anyway.

Why Organizations Implement No-AI or AI-Free Policies

1.    Data Privacy and Security Risks:

• Public GenAI tools (e.g., ChatGPT, Bard, Copilot, DeepSeek) often require users to input data, which may be stored, processed, or used to train the AI model. This poses a risk of exposing proprietary or sensitive company information within the AI solution.
• For example, employees might inadvertently paste confidential client data, internal code, or strategic plans into these tools, leading to potential data leaks.

2.    Regulatory Compliance:

• Industries like healthcare (HIPAA), finance (GDPR, CCPA), and legal (attorney-client privilege) face strict regulations regarding data handling and ai deployment processes. Using public AI tools could result in non-compliance and hefty fines for any regulatory requirement not adhered to.

3.    Intellectual Property (IP) Protection:

• Organizations want to safeguard their trade secrets, proprietary algorithms, and creative works from being ingested into public AI models.

4.    Lack of Transparency in AI Models:

• Many organizations hesitate to adopt AI tools because they lack visibility into how data is processed or stored. Without clear assurance, they prefer to avoid AI altogether.

5.    Reputation and Trust:

• Data breaches or leaks involving AI tools can damage an organization’s reputation and erode client trust.

How Big is the Problem of GenAI apps and extensions?

In short, the problem is substantial enough that a number of large and influential companies  and compliance professionals are already making changes, and the trend is likely to continue as AI adoption and data privacy concerns grow.

• The problem is significant and growing as GenAI tools become more widely adopted. For example:
  • Financial institutions: Some banks have imposed restrictions on generative AI tools to ensure that confidential client information isn’t exposed.
  • Healthcare providers: Hospitals and research institutions often limit AI usage to comply with HIPAA regulations and avoid sharing patient data in non-secure environments.
  • Government agencies: Many governments are cautious about adopting external AI solutions due to national security concerns and the need to control access to sensitive information.
  • Educational organizations: Students are not allowed to use AI for a particular class or assignment. Instructors may choose this policy if they believe that AI use would hinder students’ ability to learn the course’s objectives.
  • A 2023 report by Cyberhaven Labs found that 11% of data employees paste into ChatGPT is sensitive, including source code, client information, and internal documents.
  • Companies like Samsung, JPMorgan Chase, and Apple have banned or restricted the use of public GenAI tools after incidents where employees leaked sensitive data.

Examples of Organizations with No-AI or AI-Free Policies

1. Samsung:

• Banned the use of ChatGPT after employees leaked sensitive code by pasting it into the tool. The company is now developing its own internal AI tools to ensure data security.

2. JPMorgan Chase:

• Restricted the use of ChatGPT and similar tools to prevent the exposure of financial data and client information.

3. Apple:

• Limited the use of public GenAI tools to protect its intellectual property and proprietary information.

4. Goldman Sachs:

• Implemented strict controls on AI tool usage to comply with financial regulations and protect client data.

5. Healthcare Organizations:

• Many hospitals and healthcare providers prohibit the use of public AI tools to comply with HIPAA and protect patient data.

The fact is, making a policy to protect your organization is one thing. Enforcing it is another. Using a dynamic, automated solution can help you continuously monitor for, identify, and remediate GenAI risks across your entire user base and environment. Automated policy enforcement enables you to set a policy based on risk thresholds for extensions that talk to GenAI apps, whether end users realize it or not when they hit install.

Best Ways to Protect Sensitive Data in your Organization with AI Compliance Policies

1. Implement Clear Policies on AI Usage:

Establish and communicate a No-AI or AI-Free policy that outlines acceptable and prohibited uses of public GenAI tools. This helps mitigate compliance implications of GenAI tools that fail to comply with privacy laws and security best practices.

2. Decide How You Will Enforce the Policies:

If you are a very small organization, using a traditional change management committee or review process for incoming requests may be manageable. As your organization grows, though, you’re going to need application control capabilities that automate this process, as the average number of installed applications is 1.4 for every 2 users. Meaning, if you have 1000 users, you’re going to have around 720 applications in your environment.

3. Use Enterprise-Grade AI Solutions:

• Deploy private, on-premise, or cloud-based AI tools that are designed for enterprise use, offer advanced security measures, prevent potential security breaches, and comply with data protection regulations (e.g., Microsoft Azure OpenAI, Google Vertex AI).
• Implement AI detection tools like Spin.AI that provide Risk Assessments for Apps and Browser Extensions with pre-installed AI Free Policy.
• Example: SpinSPM.

4. Implement Data Loss Prevention (DLP) Tools:

• Use DLP software to monitor and block the transfer of sensitive data to unauthorized platforms, including public AI tools. This helps support a culture of trust, enabling you to communicate policies to employees, but have security controls in-place to protect your organization in the event they make a sharing error.
• Example: SpinDLP.

5. Conduct Employee Training and Awareness:

• Educate employees about the risks of using public GenAI tools and provide guidelines for safe usage.
• Example: KnowBe4

6. Implement Access Controls:

• Restrict access to public AI tools on corporate networks and devices using smart systems for compliance.
• Example: Spin.AI compliance enforcement

7. Encryption and Anonymization:

• Encrypt sensitive data and anonymize it before using it in any AI-related processes. You can also employ pseudonymization techniques like tokenization to ensure that sensitive data in your systems is not stored in a usable format, but it can still be used for analytics.

8. Regular Audits and Monitoring:

• Conduct regular audits to ensure compliance with AI usage policies and monitor for potential data leaks.
• You can conduct manual searches with free tools like Spin’s Risk Assessment solution, or  automate browser extension / application audits and remediation with a continuous monitoring approach, as a subscribed Spin user.

9. Develop Internal AI Tools:

• Build or customize AI tools that operate within the organization’s secure environment, ensuring data never leaves the organization’s control.

AI Risk Management and Compliance Risk Summary

The scope of this challenge and regulatory landscape varies by industry, but impacts user trust, trust with customers, and even employment decisions. It’s a significant and growing issue that requires a collaborative approach to mitigate cyber security risk that has emerged in the age of AI. The No-AI or AI-Free policy trend reflects a growing awareness of the potential risks associated with public GenAI tools. While these tools offer significant productivity benefits, organizations must balance innovation with data security and compliance. By implementing robust policies, using secure AI solutions that leverage effective techniques to identify risk and enforce compliance with those policies, and educating employees, organizations can protect their sensitive data while still tapping into the power of AI.

Don’t wait until a major data leak incident happens. Secure your organization from AI application risks today with SpinOne’s new GenAI policy enforcement solution.

Would you like to learn more about how SpinOne can protect your data from sneaky exfiltration processes? Click here to request a free demo!