This article was originally published in the February 6, 2026, issue of the Portland Business Journal.
In an era where employees are increasingly expected to do less with more, “shadow AI” is a growing problem for businesses of all sizes—whether they know it or not.
Shadow AI refers to the use of AI by employees outside of officially sanctioned IT, security, or compliance frameworks. It is similar to the “shadow IT” problem organizations faced a decade ago, where employees downloaded unapproved apps or stored files on personal devices for “easy access”, but with far higher stakes, as AI tools may store, process, transform, and retain data in ways that are opaque to the organization—and fundamentally disruptive.
Shadow AI is growing rapidly for a number of reasons, including:
- Low barriers to entry: Many AI tools are free or inexpensive and easily accessible to employees with no IT involvement.
- Productivity pressure: Employees are under constant pressure to increase output. With the ability to quickly perform tasks such as drafting emails, analyzing data, summarizing meetings, or generating marketing materials, AI offers tangible efficiency gains.
- Lagging governance: For many organizations, policies have not kept up with AI adoption, leaving employees to make their own (sometimes questionable) judgment calls.
While the productivity gains of AI use are enticing, there are a number of material risks created by shadow AI that organizations should not ignore:
- Intellectual property damage: A company’s IP portfolio can be one of its most valuable assets. Unauthorized disclosure of company IP via input into public AI tools risks invalidating any potential patent rights and obliterating trade secrets. And copyright in purely AI-generated works isn’t owned by anyone, risking generating core materials that cannot be protected or monetized.
- Data leakage: Employees may unwittingly input sensitive customer data, financial information, or other proprietary or confidential information into public AI tools. Once shared, that data may be stored, reused for training, inadvertently disclosed in future AI responses, or exposed through security incidents.
- Legal exposure: Privacy regulations, contractual obligations, and emerging AI laws increasingly require organizations to know how data is used and processed. Unapproved AI usage can unknowingly put the company out of compliance, leading to monumental legal fees, fines, and damages.
- Reputational harm: Unvetted AI tools with unknown training data and biases can generate output that is inaccurate, discriminatory, or inconsistent. When that output is passed along as the word of the company via communications with clients or marketing materials, the reputational fallout can be severe, even where the intent was pure.
Outright bans on AI usage are usually not effective—responsible enablement is. The reality is that every organization already has employees using AI, whether leadership knows it or not. Attempting to ban AI use typically worsens the shadow AI problem, as many employees either ignore the ban or hide their usage. When establishing workplace guidance on AI use, companies should instead:
- Understand what tools are being used: The shadow AI problem cannot be fully addressed without understanding its scope. Companies should survey their employees in a friendly, non-adversarial manner to understand what tools are already used, expressing an interest in institutionalizing the AI efficiencies, not a goal of punishing its use.
- Offer company-appropriate platforms: There are now many enterprise-grade, industry-specific platforms available that segregate data, do not train on input, and otherwise have appropriate guardrails. Providing a company-acceptable alternative to problem AI demonstrates the company’s commitment to supporting its employees’ professional growth and encourages employee adoption of appropriate platforms.
- Establish AI governance: As with any governance model, AI governance is an iterative process. Start by engaging stakeholders to determine where AI tools can provide increased efficiencies in workflow and outcomes. Based on that knowledge, leadership can conduct assessments of AI tools, draft use policies, implement training programs, and align risk tolerance with oversight and accountability.
- Set and communicate clear policies and expectations: Empowered by the knowledge of why employees use AI and what company-appropriate tools exist to support employee needs, companies should adopt clear, concise AI policies and expectations and regularly train employees on proper AI use.
Addressing shadow AI requires intentional leadership, but an ounce of prevention is worth a pound of cure. Companies that act now can reduce risk, build trust, and position their organizations to benefit from AI responsibly. Ignoring AI is not an option; it’s already here and acknowledging that fact and acting appropriately is simply good business.
This article is provided for informational purposes only—it does not constitute legal advice and does not create an attorney-client relationship between the firm and the reader. Readers should consult legal counsel before taking action relating to the subject matter of this article.
