Key takeaways:
- Shadow AI is the unauthorized adoption of AI technology by employees within an organization
- The risks of shadow AI are multifaceted, and include cyber, data privacy, performance, transparency, and ethical challenges
- Detecting shadow AI requires a multi-pronged approach including upfront review, ongoing monitoring, and regular audits
- While no method is foolproof, organizations can reduce the risk of shadow AI by adopting education and compliance programs, implementing technical restrictions, and adopting AI governance programs
What is shadow AI?
Shadow AI is the unauthorized adoption and use of artificial intelligence technology by employees within an organization that is unknown to the organization’s IT department.
This phenomenon has become increasingly common in the workplace.
According to a recent Salesforce study, more than half (55%) of employees who use GenAI at work are using unapproved tools, despite recognizing that there are significant cyber, data, and other risks.
Shadow AI can occur when an employee turns on AI capabilities offered by a vendor, or when an employee uses foundation models like ChatGPT, Claude, or Gemini in ways that haven’t been sanctioned or approved by the organization.
What are some examples of shadow AI use?
- An employee creates a first draft report by entering company data into a foundation model website that hasn’t been approved by the company
- A recruiter – unbeknownst to the organization overall – begins using AI to transcribe notes and make suggestions about which candidates to proceed to the next interview
How did the term shadow AI come about?
Shadow AI is a relatively newer term derived from the older concept of shadow IT. Shadow IT is used to define hardware, software, or cloud services that are not tracked or approved of by an organization’s IT department.
Like shadow IT, unsanctioned use of AI can pose significant risks for organizations – even when employees are well-intentioned.
What are the risks of shadow AI?
While employee usage of AI is not inherently negative – and can come with significant benefits to productivity and quality of outputs – shadow AI can be dangerous for an organization. This is because when AI is not used responsibly, it can open up business, compliance, reputational, or security risks.
Examples of risks include:
- Lack of transparency
- Data privacy
- Biased outputs
- Poor performance
- Security challenges
- Regulatory or compliance issues
Read more about AI risks here.
How to detect shadow AI
Shadow AI can be detected by proactively evaluating terms of service and product specifications for third parties, as well as by conducting ongoing monitoring and regular audits:
- Upfront reviews: reviewing terms of service for vendor technology to review for embedded AI
- Ongoing monitoring: monitoring model usage logs for inappropriate and unsanctioned applications
- AI audits: conducting regular audits of AI use by employees within an organization
It is critical for organizations to take upfront and ongoing steps to reduce their risk exposure.
How to prevent shadow AI
Preventing shadow AI is done through a combination of personnel and technical solutions, and is supported by a clear and robust AI governance strategy.
Adopt Training and Compliance Measures:
- Implement employee training: HR departments should partner with legal/compliance to educate employees. This training should include details about AI, its usage, risks, and which technology capabilities have been sanctioned by the organization.
- Share an AI Acceptable Use Policy: The organization should clearly outline its view of acceptable AI use. This is typically documented in an Acceptable AI Use Policy that outlines which AI uses are allowed, sanctioned technologies, an AI usage review and approval process, and consequences for violations.
- Clarify personal device use: Organizations should make clear that exporting company data or conducting company business on personal devices is prohibited to reduce the risk of shadow AI being used to complete work on devices that cannot be monitored.
Invest in Technology Solutions:
- Restrict access to unsanctioned tools: IT departments should take care to block and limit access to AI solutions that aren’t allowed by the organization. This may include intentionally tuning off AI capabilities of vendors within their existing technology stack; or limiting employee access to certain websites or portals. Another example might be limiting access to certain AI tools only to specific employees who are sufficiently trained.
- Block transmission of sensitive data like PII: For sanctioned AI tools, IT departments can enlist software that proactively blocks the transmission of sensitive data before it is entered into the AI solution. While this isn’t guaranteed to protect sensitive information, it can dramatically reduce the risk that PII is shared with third parties.
Implement an AI Governance Program:
- Develop an AI governance plan: AI governance programs can help organizations track and manage their approved AI within internal and vendor technologies. They establish the groundwork for consistent review of AI applications, and ensure consistent and ongoing review as the AI landscape and capabilities rapidly change.
- Use technology streamline and track AI use: An AI governance platform like FairNow can help IT organizations increase visibility and stay on top of their companies’ AI. AI governance platforms increase transparency and reduce the risks of AI adoption.
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