AI: The Next Frontier of PE Deal Risk

Cybersecurity diligence was once treated as a specialized issue in private equity transactions; today, it’s part of the standard deal process. Buyers evaluate security controls, incident history, insurance coverage, vendor exposure, and data practices as a matter of course because operational failures in any of those areas can materially impact enterprise value.

Artificial intelligence (AI) is quickly emerging as a distinct and compounding layer of deal risk. The issue is no longer whether companies are using AI tools; most are. The real questions are whether management teams understand how those tools are being used inside the business, what data is being used and potentially exposed, whether outputs are reliable and auditable, and whether any meaningful governance exists around deployment.

Many companies cannot answer those questions with confidence. That creates legal, operational, and valuation concerns that are beginning to surface in the deal process. In many transactions, AI diligence still resembles traditional software diligence. Buyers may ask whether the target uses AI, whether products contain AI functionality, and whether the company has adopted an internal policy. Those questions are becoming less useful.

The more difficult questions are practical. Are employees uploading confidential or customer information into public generative AI platforms? Has proprietary code been used in, or generated by, third-party systems? Are customer-facing decisions being influenced by unverified AI-generated outputs? Is management overstating the sophistication of the company’s AI capabilities? Is the business considering any IP risks of generative AI? Are customers, employees, competitors, or regulators already asserting claims based on AI-enabled decisions or disclosures? Are existing insurance programs designed to respond to AI-related failures? In some cases, management teams themselves do not fully know the answers.

In fact, many companies have no centralized record of what AI systems are in use, on what terms, or what data has passed through them. That absence of documentation is itself a material finding, because it shifts AI diligence from a records review into an exercise that depends heavily on management representations—and raises a harder question about what contractual protection is appropriate when those representations cannot be independently verified.

Regulatory Scrutiny

Regulators are already focused on the gap between AI-related marketing claims and operational reality. In March 2024, the SEC announced settled charges against two investment advisers accused of making misleading statements about their use of AI. SEC Chair Gary Gensler described the conduct as “AI washing,” comparing it to prior greenwashing enforcement efforts. Although those actions involved investment advisers, the broader point applies across industries. Regulators are increasingly examining whether companies are overstating how AI is being used, how reliable it is, and whether internal controls match external claims. This has implications for disclosure, diligence, and transaction-risk allocation.

The FTC has taken a similar position. The agency has repeatedly warned businesses that AI-related claims remain subject to existing consumer protection standards, particularly where statements regarding functionality, accuracy, or data practices are misleading, and launched Operation AI Comply in September 2024 to target companies that overpromise the capabilities of their AI products or falsely claim to use AI to enhance their services.

Shadow AI: The Hidden Adoption Problem

The practical challenge for sponsors is that AI adoption often occurred informally and without centralized oversight. Unlike major software implementations, generative AI tools can enter a business quietly. Employees may begin using public platforms for drafting, coding, research, customer communication, or data analysis without formal approval processes or documentation. This creates what may become one of the defining diligence issues of the next several years: shadow AI. A single employee can upload contracts, financial information, customer data, or proprietary code into an external platform within minutes. In many organizations, shadow AI introduces these risks of data leakage, intellectual property exposure, and unmonitored external processing of sensitive information that may not be captured by security logs, cybersecurity reports, IT inventories, or compliance reviews.

The resulting risks extend well beyond data privacy concerns. AI-related exposure can affect intellectual property ownership and enforceability, confidentiality obligations, regulatory compliance, employment matters, consumer protection issues, fiduciary oversight, litigation exposure, and insurance recovery, to say nothing of reputational concerns.

Litigation Exposure and Contractual Risk

Litigation risk deserves particular attention because AI failures may produce fact patterns that are familiar to plaintiffs’ lawyers. A flawed AI-model output can result in a contract dispute, a customer claim, an employment dispute, a consumer protection claim, or a securities-style disclosure lawsuit depending on how the output was obtained, deployed, and governed. For example, target companies that use AI in customer communications, pricing, underwriting, hiring, claims handling, content moderation, coding, or financial analysis may face allegations that AI-assisted decisions were inaccurate, insufficiently supervised, or inconsistent with public disclosures or contractual commitments. Even where liability is uncertain, the cost of investigating AI-model behavior, reconstructing prompt histories, preserving logs and outputs, and producing AI-related materials in discovery can be significant.

The contractual dimension of that exposure is underappreciated. Where employees have accepted platform terms on the company’s behalf—typically at the point of use, without legal review—those terms govern data rights, output ownership, and vendor liability in ways management has not evaluated. Whether AI-generated outputs constitute protectable intellectual property, and whether the company has effectively licensed its confidential information to the platform in the process, depends on vendor terms and applicable law that are rarely examined at the moment of adoption.

Insurance questions are becoming particularly important. Many existing cyber, E&O, D&O, and technology-liability policies were not drafted with generative AI risks in mind. As claims involving hallucinated outputs, automated decision-making, deepfake fraud, AI-facilitated data breaches, or undisclosed AI usage begin to emerge, coverage disputes are likely to follow.

Representations and Warranties

Representations and warranties provisions are also evolving. Cyber representations historically focused on breaches, security practices, and compliance frameworks. AI-related representations are beginning to address governance controls, employee usage restrictions, training data practices, disclosure accuracy, internal approval procedures, and integration of AI systems within the company’s broader information security architecture. Sponsors are recognizing the importance of ensuring that AI-specific and cyber-specific rep packages are consistent and mutually reinforcing, rather than siloed. The market, however, has not settled on a consistent approach.

Many AI-related representations remain broad, aspirational, or disconnected from actual operational practices. The dynamic resembles the early years of cybersecurity diligence, when contractual language often exceeded the maturity of the underlying systems and controls. More sophisticated rep packages are beginning to address specific subject matter that generic AI language misses—model accuracy and validation procedures, training data provenance and licensing status, known instances of biased or discriminatory outputs, and compliance with emerging regulatory frameworks including the EU AI Act’s tiered obligations and applicable US state requirements governing automated decision-making.

However, the more fundamental problem is that the value of any representation depends on the documentation behind it. A representation warranting that the company applies reasonable validation procedures for AI outputs does little analytical work if no such procedures exist, or if the indemnification structure is insufficient to absorb the risk of a breach. That gap between contractual language and operational reality is the defining challenge of AI-related risk allocation in current transactions, and it is not a problem that improved drafting alone can resolve.

Third-Party Dependency and Competitive Differentiation

At the same time, companies face growing pressure to present themselves as AI-enabled businesses; that pressure creates its own risks. Not every company integrating generative AI possesses proprietary infrastructure, differentiated models, or defensible technology. In some cases, AI functionality may consist primarily of third-party integrations layered onto existing products or workflows.

This reliance on third-party platforms introduces a contractual layer that diligence frequently underweighs. Enterprise AI agreements vary significantly in how they allocate data use rights, liability for erroneous outputs, and migration flexibility. Where a target’s core workflows depend on a platform that it lacks contractual leverage to renegotiate or exit, the constraints imposed by that dependency—rather than any feature of the technology itself—may be the more consequential risk.

The same logic applies at a competitive level. Companies whose AI functionality consists primarily of a thin integration layer built on a foundation model provider’s API face a question that conventional diligence framing tends to obscure: whether their differentiation is durable as those providers continue to expand their own offerings. The existence of AI integration is not the same as the existence of a defensible position.

Distinguishing between genuine technological differentiation and marketing inflation is becoming an increasingly important diligence issue for buyers and investors.

Challenges in Carve-Out Transactions

Carve-out transactions present a structurally distinct version of these challenges. AI systems rarely respect business-unit boundaries—models trained on enterprise-wide datasets, pipelines drawing from shared infrastructure, and workflows serving both the divested and retained businesses simultaneously create separation problems that conventional asset purchase mechanics are not designed to resolve.

A transition services agreement (TSA) is typically the instrument through which that gap is managed, but its capacity to do so has limits. Where the divested business relies on AI tools or models that will remain under the seller’s control post-close, the TSA must bridge model versioning, data governance, and continued access in a period during which both parties’ interests have already diverged. The harder problem arises where a shared model cannot be cleanly disaggregated—where the performance the buyer is acquiring depends on training data it will not receive and cannot replicate, while the seller retains a model still reflecting the contributed data of a business it no longer owns. Those dynamics require analytical attention earlier in the process than TSA drafting typically permits.

The Bottom Line

None of these points suggest that sponsors should avoid AI-focused businesses or AI-enabled operational strategies. The technology will continue reshaping industries and investment models across the market. But the legal and diligence framework surrounding AI remains underdeveloped relative to the pace of adoption. The firms that adapt fastest will likely approach AI diligence as a broader operational and governance exercise rather than a narrow technology review.

Cybersecurity diligence became standard because investors eventually recognized that operational failures in that area could directly affect value. AI diligence appears to be headed in the same direction.