AI in M&A: The Shadow AI Hidden Risk Costing Deals Millions Right Now

Learn how shadow AI in M&A creates risks that traditional due diligence misses. These unmonitored tools expose companies to regulatory violations, IP theft, security breaches, and integration delays. Buyers need expanded discovery processes, stronger contract protections, and post-close remediation plans to protect deal value and prevent costly surprises.

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In brief:

• Shadow AI in M&A is widespread and invisible: 68 percent of cybersecurity leaders use unauthorized AI. Traditional due diligence misses personal AI accounts, SaaS add-ons, and features employees activate without IT approval.
• Hidden liabilities affect deal value. Undiscovered AI creates regulatory gaps, IP uncertainties, and security vulnerabilities that force price adjustments, stronger indemnification, or escrow provisions.
• Shadow AI discovered post-close causes breaches that cost $670K more on average and requires 30–90-plus days of remediation that disrupts synergy timelines.
• Buyers need employee interviews, network analysis, and vendor AI audits — not only formal IT reviews — plus direct questions about governance policies.
• Fifty-six percent of third-party vendors have embedded AI features, often activated by default, creating undisclosed data processors and compliance obligations.

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If you’re involved in mergers and acquisitions, you already know how much pressure there is to move fast, validate assumptions, and gain a clear picture of what you are buying. What many M&A deal teams overlook is the role of shadow artificial intelligence (AI).

Gartner predicts that by 2030, 40 percent of enterprises will experience a shadow AI incident, defined as unauthorized or unmonitored AI use that exposes data or creates new compliance and operational concerns. In an acquisition, those hidden tools and data flows can influence valuation, integration plans, and long-term liabilities.

Traditional due diligence often fails to capture the full picture. You may review vendor contracts, examine formal information technology (IT) systems, and evaluate security practices but still miss shadow AI usage patterns.

Centric Consulting’s National Strategy Alignment and Architecture Practice Lead Darren Rehrer notes this is not only an M&A issue. Shadow AI also appears during modernization efforts and operating model shifts. The difference is that it is far more expensive to uncover these patterns after a deal closes.

Shadow AI often shows up in places leaders do not expect, and the consequences surface when a transaction is already in motion. Understanding how and why shadow AI appears is one of the most effective ways to protect value before you sign.

The Shadow AI Hidden Risk in Traditional Due Diligence

When you enter an M&A process, you expect some inconsistencies in documentation or security posture. What often goes unnoticed is how much work happens in tools that leadership never approved. Shadow AI has accelerated that gap.

According to UpGuard’s “State of Shadow AI” report, 68 percent of cybersecurity leaders admit to using AI without authorization, which means your target may rely on far more unmonitored tools than you realize.

Traditional due diligence frameworks were never designed to uncover this layer. Most checklists concentrate on formal IT systems, vendor contracts, and documented applications. They don’t capture the personal AI accounts, software as a service (SaaS) add-ons, or embedded AI features employees adopt on their own.

As Centric Consulting’s AI Strategy Lead Joe Ours explains: “People are going out and using their personal ChatGPT or Claude accounts or turning on AI features in tools the organization doesn’t even realize are enabled.”

These behaviors introduce data exposure and compliance risks that will not appear in a system inventory.

Shadow AI also spreads faster than traditional oversight can keep up. Surveys show that more than half of employees use unauthorized SaaS apps, and many of these tools include AI activated by default. Restricting access rarely solves the problem. When companies ban AI completely, employees just find different ways to use it, often pushing sensitive information into systems the organization can’t monitor.

You’ve already seen the consequences play out publicly. Samsung banned employee use of generative AI after sensitive source code was exposed through ChatGPT, and other organizations have issued similar restrictions after discovering employees entered proprietary information into public AI models. These incidents illustrate the types of risks that may not appear in a target’s documentation but can surface quickly once a deal moves into integration.

During due diligence, these hidden risks become more expensive. You may not see the AI-enabled modules that employees turned on without IT approval or line-of-business purchases, which are often buried in expense reports.

Network activity rarely reveals when employees use AI features inside existing platforms. The result is a gap between the environment you think you’re acquiring and the one employees rely on, which affects compliance readiness, cyber insurance exposure, valuation, and post-close integration plans.

Hidden Liabilities: The Shadow AI Risks You Don’t See

Shadow AI introduces risks that rarely appear in due diligence materials but can create significant exposure once a deal closes. Understanding where these liabilities hide helps you evaluate a target’s true risk profile.

Regulatory Compliance and Data Security Gaps

Shadow AI often bypasses the security controls you rely on to meet privacy and industry regulations while simultaneously sitting outside expected security protocols. Employees may upload sensitive information into public models, activate AI features inside SaaS tools without approval, or use automation platforms that route data through unfamiliar regions or vendors.

According to UpGuard’s “State of Shadow AI” report, over 50 percent of employees know about sensitive data being shared with AI tools. These tools often don’t follow encryption standards, retention policies, identity requirements, or logging practices. When employees use them to move faster, your visibility into where sensitive data goes becomes limited, and information may be stored or processed in environments the organization never vetted.

In regulated sectors like healthcare, finance, and insurance, even small missteps can become violations. If customer or patient data flows through an unapproved model without proper disclosures or consent, you inherit that liability. Regulators are increasing their focus on AI use, which makes early discovery essential during due diligence rather than after integration.

Intellectual Property Risks

Shadow AI raises questions about ownership and protection of intellectual property (IP). Employees may use public AI tools to generate content, code or models. They may also unknowingly train third-party systems with proprietary information. These patterns introduce uncertainty about who owns what, whether outputs can be commercialized, and whether confidential information is now part of another company’s training corpus.

If the target’s valuation is tied to its IP, the risk becomes even more significant. You may need to remediate work, review model provenance, or retire systems altogether if they were created with unvetted tools.

Contract and Vendor Risks

Shadow AI often appears inside SaaS products you already license, especially given that 56 percent of these companies have already tested or launched AI features in their products. Even more concerning, many platforms now include AI features activated by default.

If employees enabled these features without review, your due diligence may miss new data processors, undisclosed sub-vendors, or unexpected data flows. Team-level SaaS purchases create even more risk. These tools may renew automatically, introduce compliance obligations, or store data in ways that conflict with your security posture. None of that appears in a target’s formal vendor list.

Algorithmic Bias and Discrimination Exposure

If employees use AI models to support hiring, credit decisions, claims processing, or customer service, you may inherit exposure to algorithmic bias. These tools often operate informally, so their impact on decision-making is undocumented. Regulators across industries are already asking organizations to show how they made automated decisions. If you acquire a company that relied on unapproved AI for these processes, you may be responsible for defending decisions you did not make.

Shadow AI does not create one type of risk. It creates many small, often invisible liabilities that accumulate across systems, teams, and workflows. If you do not identify these types of risky AI business applications during due diligence, they will likely surface during integration, regulatory review, or future audits.

How Shadow AI Affects Your Valuation and Deal Value

Shadow AI affects how you value a company and structure a deal. Undocumented tools, unmonitored data flows, or AI-driven processes may necessitate revisiting purchase price assumptions and integration plans.

Part of the challenge is timing and focus. Deal teams often concentrate first on financials, strategic fit, and formal technology assets. That leaves gaps in understanding how work actually gets done inside the organization. When employees rely on unapproved AI features or team-level SaaS purchases, the environment you believe you are acquiring may not match the one you inherit. That difference can introduce unplanned licensing costs, additional security work, or replacing tools that cannot meet regulatory or contractual requirements.

Those discoveries influence how you allocate risk in the deal. If a target cannot clearly explain where they use AI or what data they passed through external services, you may need narrower representations and warranties, stronger indemnification language, or escrow provisions tied to remediation. Buyers are increasingly adjusting deal structures when they find AI use that sits outside the organization’s documented systems and governance.

The impact continues after close. Once shadow AI surfaces during integration, remediation takes time and resources. Remediation often requires phased timelines that extend well beyond initial closing.

Those timelines affect early performance targets, earn-out milestones, and the point at which you begin to realize the value you modeled for the deal. They can also pull funding and talent away from other planned initiatives if remediation turns out to be more involved than expected.

Shadow AI does not always change your decision to move forward with an acquisition. However, it often changes how you price the deal, how you allocate risk, and how you plan the first phase of integration. Identifying those issues early gives you more room to adjust structure and expectations before they turn into surprises.

Post-Merger Integration Nightmares: 6 Potential Shadow AI Challenges

Even when diligence appears thorough, shadow AI often reveals itself only after the deal closes. These discoveries slow progress, introduce new liabilities, and alter initial 90-day assumptions.

Below are the most common shadow AI challenges that surface during integration:

1. Hidden AI Dependencies Surface During Process Mapping

Integration teams often discover AI-enabled tools only after interviewing employees or walking through daily workflows. Many of these tools do not appear in system inventories, which means each one must be reviewed for functionality, compliance, and security before it is merged into your environment.

2. Undisclosed AI Features Inside SaaS Platforms Trigger Surprise Reviews

Many SaaS tools now include embedded AI capabilities that employees can activate without IT oversight. When these features appear during integration, they require additional assessment and often create delays in data migration or platform consolidation.

3. Unmanaged AI Increases Security and Compliance Exposure

Shadow AI carries real security implications. An IBM analysis found that organizations with unmonitored AI usage experienced data breaches costing an average of $670,000 more than those with stronger oversight. If your target relied on consumer-grade models or unvetted services, you may inherit exposure that was never disclosed during diligence.

4. Remediation Timelines Disrupt Integration Momentum

Once shadow AI is identified, teams must evaluate, replace, or re-engineer processes that rely on those tools. Short-term fixes can limit access or isolate risky workflows, but long-term remediation often requires rebuilding processes, migrating data, or implementing new solutions. This reduces capacity for planned integration work and pushes out early milestones.

5. Each Business Unit Brings Its Own Shadow AI Patterns

When multiple teams or acquired entities have different AI tools, usage patterns, or levels of oversight, integration becomes more complex. These variations slow down governance rollout, complicate platform consolidation, and increase the risk that you miss tools entirely.

6. Synergy Timelines Shift as Teams Absorb Unplanned Work

Hidden AI usage pulls teams away from value-focused initiatives. As teams address unexpected tooling, data, and security issues, you often have to adjust key performance targets, earn-out milestones, and synergy expectations.

Shadow AI rarely creates a single point of failure, but it consistently creates friction across integration activities. Planning for discovery and remediation up-front helps you avoid surprises and preserve the value you expect to gain in the first months after close.

Due Diligence for Shadow AI: A Discovery Checklist and Questions to Ask

Shadow AI requires expanding discovery beyond formal systems and documented workflows. You need clarity on how employees work, what data flows through unmonitored tools, and which AI capabilities are embedded in platforms — helping you identify risks early.

Below is an updated set of discovery steps, questions, and assessment areas to include in your diligence strategy.

Shadow AI Discovery Checklist

1. Take inventory of all AI and machine learning (ML) tools in use. Ask each team which tools they rely on for daily work, including:
   • Public AI models
   • AI-enabled features inside SaaS platforms
   • Team-level automation tools
   • Personal accounts used for work tasks
2. Map data flows for AI use. You need clarity on:
   • What information employees input
   • Where that data is processed or stored
   • Whether AI outputs influence customer or regulatory decisions
3. Review vendor and licensing documentation. Look for:
   • Undocumented tools purchased by departments
   • Auto-renewing subscriptions
   • AI add-ons that activate without administrator approval
4. Assess compliance and security posture. Request information on:
   • Data retention
   • Model training inputs
   • Use of customer or employee data
   • Cross-border data flows
5. Audit decision-making processes involving AI. Focus on whether AI supports or influences:
   • Hiring
   • Credit decisions
   • Claims handling
   • Customer service routing
6. Conduct employee surveys or interviews. These reveal what tools are actually used, not just what IT believes is used.
7. Analyze network traffic for unmonitored AI activity. Patterns often reveal AI services that were never formally approved.

Questions for Target Leadership

Below are questions that help you uncover hidden use and leadership awareness:

1. AI Governance and Visibility
   • Do you have documented AI governance policies?
   • Who is responsible for monitoring AI use today?
   • How do you evaluate new AI tools before adoption?
2. Training Data and IP Clarity
   • What data has been used to train internal models?
   • Did employees input proprietary information into external AI tools?
   • Who owns the outputs of AI-assisted work?
3. Third-Party Risk and Contracts
   • Which vendors process data through AI features?
   • Do your SaaS providers enable AI by default?
   • Have you reviewed your vendors’ terms for data use, retention, or model training?
4. AI-Related Incidents
   • Have you experienced any AI-related data exposure?
   • Have auditors or regulators raised questions about AI processes?

This perspective reinforces the need for clear and direct questioning rather than relying only on formal documentation.

Technical Assessment Recommendations

• Allocate additional time in diligence for AI discovery
• Prioritize high-risk areas such as customer data and regulated workflows
• Validate whether AI tools align with your cybersecurity standards
• Document all findings for AI use in deal structure and integration planning

The more visibility you build during diligence, the fewer surprises you face after close. A thorough AI discovery process helps you protect value, set realistic expectations, and enter integration with a clearer view of the environment you are inheriting.

Remediation Strategies and Deal Protection If You Find Shadow AI During Your M&A

Once you discover shadow AI during diligence or integration, shift your priority to understanding scope and determining solutions without disrupting the deal. A structured remediation approach helps you manage risk and maintain transaction progress.

Below are the primary actions buyers should take.

Pre-Close Remediation Options

• Rapid risk assessment and prioritization. Start by identifying where AI tools appear, what information they process, and whether they support regulated or customer-facing decisions. Not all tools pose the same level of risk, so it is important to separate high-impact workflows from those you can evaluate later. This early triage helps you understand whether integration plans or deal terms need to change.
• Sunset or restrict high-risk tools. If employees rely on tools that handle sensitive data or lack basic security controls, you may need temporary restrictions. These actions stabilize the environment and prevent additional exposure while you complete the assessment. Restricting tools early can also give your team time to plan replacements or build short-term workarounds.
• Address immediate compliance gaps. Identify where regulated or customer data may have flowed into unapproved services, and document those findings right away. Legal teams, cyber insurers, and regulators expect organizations to show prompt action when gaps are discovered. Early documentation also helps shape your negotiation strategy if the scope of the issue affects deal structure.

Deal Structure Protections

• Review AI-specific representations and warranties. Traditional representations and warranties typically focus on data privacy, cybersecurity, and IT assets, but they rarely address AI use. Adding targeted language tied to undisclosed tools, training data, or data-sharing practices helps you assign responsibility for issues that surface after sign-off. These additions are becoming more common as buyers recognize the complexity introduced by informal AI adoption.
• Understand indemnification tied to AI exposure. If a target cannot fully document how employees use AI tools, you may need indemnification for losses connected to undisclosed models or data flows. This becomes especially important when tools influence regulated decisions. Indemnification ensures the buyer is not absorbing risk that was never revealed during the transaction.
• Determine escrow arrangements linked to remediation. Escrow provisions give both parties a clear mechanism to address any required cleanup. You can allocate funds for technical remediation, workflow redesign, or security improvements without delaying closing. This approach provides certainty for buyers and sets transparent expectations for sellers.
• Consider price adjustments or earn-out conditions. When shadow AI introduces significant remediation work, deal teams may adjust valuation or timing to reflect the added effort. You can also structure earn-outs to account for delayed performance if workflows must be redesigned. These adjustments help both sides maintain alignment as new information emerges.

Post-Close Integration Planning

• Conduct a 90-day shadow AI review. A focused 90-day assessment helps you understand how deeply shadow AI is woven into daily operations. This review should include an inventory of tools, documentation of data flows, and a map of dependencies that may affect integration. These findings guide your early integration planning and highlight areas needing immediate attention.
• Build a time-based remediation road map. Remediation varies in complexity, which affects the order in which you address issues. Centric CIO Services Coordinator Paul Gelter notes: “There might be some things you can do quickly, but other things are going to take time — some 30 days, some 60, and some 90 or longer.” A phased road map helps your integration teams maintain focus and avoid getting overwhelmed by competing priorities.
• Manage change carefully. Replacing or disabling tools can disrupt day-to-day work if not handled thoughtfully. Provide teams with clear communication, training, and temporary alternatives to maintain productivity. Strong change management reduces frustration and prevents employees from seeking new shadow tools to fill the gap.
• Implement clear AI governance. Governance is essential to prevent the same issues from re-emerging in the combined organization. Clear policies, review mechanisms, vendor requirements, and oversight responsibilities create visibility into how to use AI. With a governance model in place, future acquisitions become easier to evaluate and integrate.

Shadow AI becomes manageable once you understand where it appears and how it affects the environment you are acquiring. With structured diligence, appropriate deal protections, and a thoughtful integration plan, you can reduce surprises and preserve the value you expect to capture.

Vendor and Partnership Due Diligence: 4 Steps to Mitigate Shadow AI Use

Shadow AI exists beyond the organization you’re acquiring. Vendors, partners, and service providers may use AI in ways that influence your security posture and operational risk, requiring visibility into their data use, model operations, and governance alignment.

Below are steps to strengthen vendor and partnership due diligence in the AI era.

1. Assess How Vendors Use AI Inside Their Products

Many products now include built-in AI features that activate automatically. Review each vendor’s documentation to understand how these capabilities function, what data they process, and whether you can control or disable them. Standards such as the NIST AI Risk Management Framework provide helpful reference points for evaluating these tools.

2. Evaluate Data Protection Practices

Ask vendors to explain how data flows through the AI components of their services. This includes retention periods, model-training practices, and cross-border data movement. The European Data Protection Board provides guidance on AI-related data use, which can be helpful when evaluating global vendors.

This review helps ensure your partners do not introduce compliance gaps or handle information in ways that conflict with your policies.

3. Strengthen Contract Requirements

Update vendor contracts to include clear language around:

• Use of customer or regulated data in AI systems
• Rights to audit AI-related processes
• Restrictions on training third-party models
• Requirements for transparency when new AI features are introduced

These provisions help you manage risk and reduce surprises during integration or operations.

4. Expand Third-Party Risk Monitoring

Many AI-related risks emerge in the lower tiers of your supply chain. Tools from your vendors’ vendors may include AI features you have never reviewed, and these components often process sensitive data or interact with your systems in ways that are not visible during initial evaluations.

Expanding your third-party monitoring program helps you identify changes in vendor tooling, security posture, or AI use that could affect your environment. Regular reviews and clear reporting expectations help ensure that AI adoption across the supply chain aligns with your policies and governance requirements.

Vendor due diligence has always been a core part of M&A, but the rise of embedded AI increases its importance. By asking deeper questions, strengthening contracts, and expanding third-party oversight, you gain a clearer understanding of the risks that accompany the tools and partners your target relies on each day.

With a clearer view of internal and external AI use, you can move into closing and integration with fewer unknowns and a stronger foundation for long-term governance.

Bringing AI Visibility Into the Deal Room

Shadow AI is now part of nearly every organization’s operating reality, which means it inevitably becomes part of the M&A process. The challenge is not the presence of AI itself but the lack of visibility into where it is used, how it handles data, and how it influences decisions. Without that understanding, even well-planned transactions can encounter surprises that slow integration, increase remediation work, or shift the value you expected to capture.

A stronger diligence approach helps you reduce these unknowns. When you expand discovery to include employee tool use, embedded AI features inside SaaS platforms, and the practices of key vendors, you gain a clearer view of the environment you are acquiring. That clarity supports better deal structures, smoother integration, and more accurate planning for the first 90 days.

Most importantly, it gives you the confidence to move through a transaction without hidden risks undermining your momentum. As AI continues to evolve, bringing transparency and governance into the deal room becomes essential for protecting value and preparing the combined organization for long-term success.

Wondering how to protect your organization from cybersecurity threats during your M&A? Our IT security consultants can help address critical components of a M&A cybersecurity strategy, like shadow AI. Talk to an expert.