The New Legal Risk Isn’t AI Adoption—It’s AI Without Governance

Artificial intelligence (AI), including generative and agentic AI, is no longer an emerging workplace tool. AI is already embedded in daily business operations—often faster and more extensively than leadership teams realize.

Employees use generative AI platforms to draft emails, summarize contracts, analyze data, prepare presentations, write code, conduct research and build AI-enabled workflows without technical expertise. Legal departments are adopting AI-assisted workflows, and vendors are embedding AI capabilities into enterprise software by default, including commonly used services such as video conferencing platforms.

The question is no longer whether organizations are using AI, but whether they are governing that use through clear policies and procedures, human oversight, accountability, training and risk controls. This client alert explains key legal, privacy and cybersecurity risks associated with unmanaged AI use and identifies practical steps organizations can take to manage them.

The “Shadow AI” Problem

For years, companies have struggled to prevent employees from using unapproved IT solutions, and the availability and ease of use of AI present a similar but more complex challenge.

Many organizations already have employees using AI tools without formal approval processes, usage guidelines or awareness that a tool includes AI functionality. This phenomenon—often referred to as “shadow AI”—creates significant legal, privacy, cybersecurity and operational risk. Employees may, for example, input confidential client information, proprietary company data, protected health information, personal information or sensitive financial information into third-party AI systems without proper vetting or approval by legal, compliance, privacy, cybersecurity or IT teams. Agentic AI can also execute multistep tasks with limited human involvement, increasing the potential for unauthorized data access, erroneous actions or operational disruption if the tool is not properly configured and supervised. Leadership may assume AI use is limited because the organization has not formally adopted an enterprise AI platform; in practice, consumer-grade AI tools are often used informally across the enterprise. Depending on the provider’s terms and settings, user inputs may be retained, used to improve models or exposed to other confidentiality, privacy or security risks. Without proper governance, organizations lose visibility into what data is being shared, where it is going and which obligations may apply. That loss of visibility can create legal, regulatory, contractual and security risk.

AI Governance Is a Legal and Compliance Necessity

AI governance has shifted from a best practice to a core legal and compliance function that overlaps with privacy, cybersecurity, vendor management, employment, intellectual property and records governance. Regulators, courts, insurers, clients and business partners increasingly expect organizations to demonstrate that AI use is subject to documented oversight and risk controls. The legal risks are broad and interconnected and can include the following:

• Cybersecurity and Privacy Risks. Generative and agentic AI tools can create significant privacy, cybersecurity and confidentiality risks when confidential information, personal information or sensitive information is entered into external systems without appropriate safeguards, contractual protections, access controls and data-retention limits. Organizations subject to privacy, cybersecurity, sector-specific or contractual requirements may face heightened exposure if employee AI use bypasses approved compliance protocols or vendor-review processes.
• Accuracy and Reliance Risks. AI-generated content can appear authoritative while containing factual inaccuracies, fabricated citations (hallucinations), flawed reasoning, outdated information or outputs affected by model drift. When employees rely on unchecked AI outputs in legal, financial, healthcare, operational or customer-facing decisions, the resulting errors can create liability, regulatory, contractual and reputational risk.

Bias and Discrimination Risks. AI systems can reflect and amplify bias in training data, design choices, assumptions and human decision-making processes used to train, deploy and rely on those systems. Without appropriate governance, testing, validation and human oversight, AI-enabled decisions can produce unfair or discriminatory outcomes, particularly in high-impact contexts such as employment, credit, housing, healthcare, insurance and access to services.

• Intellectual Property Concerns. AI-generated materials can raise unresolved questions involving IP ownership, copyrightability, inventorship, licensing obligations, training-data rights, infringement exposure and trade-secret protection. Employees who use AI notetakers or other tools with inappropriate data-use settings may disclose confidential information or trade secrets to third-party systems. Because current U.S. copyright and patent frameworks generally require human authorship or inventorship, organizations should evaluate whether AI-assisted work product is protectable, whether human contribution is adequately documented, and whether trade-secret protections are sufficient. Organizations using AI-generated content externally without human review may create legal complications tied to IP rights, disclosure obligations and third-party claims.
• Employment and Workplace Issues. Businesses are increasingly facing litigation and regulatory scrutiny related to AI tools used in hiring, promotion, performance management, workforce monitoring, scheduling and productivity measurement. Claims may include discrimination, disparate impact, bias, wage-and-hour, privacy, notice-and-consent, and employee-relations issues. State and local laws, including New York City Local Law 144, also impose compliance obligations for certain automated employment decision tools used in candidate or employee screening.
• Vendor and Contractual Exposure. Many organizations adopt AI-enabled platforms through third-party vendors without fully understanding model training practices, data-use rights, security controls, subcontractor access, output ownership, indemnification limits, use-case restrictions or allocation of regulatory risk. Legacy service agreements and data protection agreements often do not address AI-specific risks, including whether the vendor may use customer data for training, model improvement or human review; whether the model is open-source, proprietary or third-party; whether the vendor can document data provenance; and which use cases may void warranties or indemnities. AI vendor review should therefore evaluate both contractual protections and the maturity of the vendor’s AI governance, privacy and security controls.

A Written AI Policy Is No Longer Optional

One of the clearest indicators of organizational preparedness is whether a company has implemented a formal AI governance framework. At a minimum, organizations should have a well-documented policy that addresses the following:

• Data inventory and mapping of AI use cases
• Acceptable-use rules, including approved use cases by role and function
• Data-handling restrictions for confidential information, personal information, sensitive information and regulated data
• Human review, approval and escalation requirements
• Disclosure and transparency standards
• Approval procedures for AI tools and AI-enabled vendor products
• Record-retention and output-preservation requirements
• Employee training and awareness protocols
• Vendor due diligence and contracting procedures

Importantly, AI governance should be integrated into a company’s existing cybersecurity, privacy and compliance programs rather than treated as a standalone IT initiative. Privacy laws and related regulations increasingly require assessments, cybersecurity controls and governance for automated decision-making technologies, making coordination among AI, privacy and cybersecurity teams essential. Organizations that use the NIST Cybersecurity Framework or NIST Privacy Framework may consider aligning AI governance with the NIST AI Risk Management Framework; organizations with ISO 27001 or ISO 27701 programs may consider whether ISO/IEC 42001 provides a useful AI management-system framework. Effective governance typically requires enterprise-wide coordination among IT, legal, compliance, privacy, cybersecurity, HR, operations, procurement and executive leadership. Organizations that wait for litigation, a regulator inquiry, a security incident or client scrutiny before implementing controls may find themselves reacting under far more challenging circumstances.

The Competitive Advantage of Clear Governance

Well-structured AI governance is not merely defensive; it can be a strategic advantage. Organizations that establish clear guardrails are better positioned to adopt AI tools confidently, improve operational efficiency, satisfy client and regulator expectations, reduce organizational uncertainty and scale innovation responsibly. The companies most likely to benefit from AI will not be the ones that use the most tools, but the ones that pair innovation with disciplined risk management.

Final Thoughts

AI adoption is accelerating across nearly every industry, and that trend is unlikely to slow. The legal risk facing organizations today is not simply that they use AI; it is that AI use may expand without governance, accountability, privacy and security controls or legal oversight. Companies that address AI, cybersecurity and privacy governance proactively will be better positioned to manage risk, preserve trust and adapt to evolving legal and regulatory expectations.