AI Boom, Security Bust: How Deal Teams Should Diligence AI-Heavy Targets
By Dritan Saliovski
Private equity and corporate deal teams are seeing a rising share of targets whose value story depends on AI capability. Enterprise software portfolios now include AI-native products. Industrial targets are pricing in automation. Service businesses are projecting margin expansion from AI-driven efficiency. In nearly every case, the diligence process was not designed for any of this.
Key Takeaways
- 53% of M&A deals encounter critical cybersecurity issues that jeopardize the transaction
- Only 29% of organizations feel ready to deploy agentic AI securely despite 83% planning to do so; the acquisition target, by default, is in the unready majority
- The Salesloft-Drift supply chain breach propagated through over 700 downstream environments via OAuth tokens; AI-heavy targets introduce similar concentration risk
- EU AI Act enforcement begins August 2026 with fines up to 3% of global turnover; target AI non-compliance transfers on close
Where AI Risk Hides in a Typical Data Room
AI risk rarely shows up in the data room tree as a folder labeled "AI risk." It is distributed across four locations that deal teams routinely review without connecting them.
| Data Room Section | What to Look For | AI Risk Signal |
|---|---|---|
| Product / Technology | Architecture diagrams, third-party service listings, API documentation | Every reference to a model provider, vector database, agent framework, or LLM orchestration tool is an AI risk surface |
| Vendor / Contracts | AI vendor contracts (OpenAI, Anthropic, Google, hosting providers) | Data processing terms, residual training-data rights, termination clauses |
| Legal | Regulatory filings, correspondence, IP litigation, privacy complaints | AI-related matters the target may have minimized in the management narrative |
| HR / Operations | Employee agreements, customer terms, vendor agreements | Language governing AI use on the platform; mismatches between stated and contractual AI permissions |
Red-Flag Patterns Deal Teams Should Surface
Ungoverned agent sprawl. The target has deployed AI agents across functions without an inventory, permission reviews, or a lifecycle process. Surfacing question: how many agents are in production, who owns each, and when was the last access review. If the answer is partial, the sprawl is the finding.
Data residency chaos. The target processes regulated data through AI providers whose infrastructure sits in jurisdictions that do not match data residency obligations in customer contracts or applicable regulation. Particularly acute when the target sells into EU markets and uses US-based AI providers without appropriate data transfer mechanisms.
Vendor lock-in with weak controls. The target depends on a single AI vendor for a critical capability with no fallback, no exit terms, and no internal capability to replicate the function. If the vendor changes pricing, terms, or availability, the target's economics change materially.
Training data provenance gaps. The target cannot produce, on reasonable notice, the source and licensing posture of the data used to train its proprietary models. The IP and regulatory exposure is enforcement-ready for EU AI Act compliance starting August 2026.
Agent-to-production access without human approval. The target has AI agents with direct write access to customer environments, financial systems, or production databases with no human approval layer. This is an incident vector, not a capability. For the security baseline shift that AI has created, uncontrolled agent access in a target is a material finding.
The Six-Question AI/Cyber Diligence Framework
Six questions, applied to every AI-relevant target:
| # | Question | Output |
|---|---|---|
| 1 | What AI capabilities are in production, and what do they depend on? | Inventory (not a summary) |
| 2 | What data do those capabilities process, and under what legal basis? | Regulatory map |
| 3 | Who has accountability for each AI capability, and what is the governance process? | Named owners (not functions) |
| 4 | What incidents have occurred in the last 24 months, disclosed or not? | Incident register (non-disclosure becomes a representation issue) |
| 5 | What contractual obligations does the target have regarding AI use, and are they being met? | Gap map |
| 6 | What integration risks exist when connecting acquirer and target environments post-close? | Agent access scope, vendor concentration, data flow exposure |
The output is a red/yellow/green assessment by category, supported by diligence evidence, usable in the investment committee conversation. For the complete cybersecurity due diligence methodology and how PE firms typically miss critical findings, AI diligence extends rather than replaces the existing framework.
How This Integrates With Existing Cyber Diligence
AI diligence is not a separate workstream. Every AI system is a cyber asset. The cyber asset inventory and the AI inventory are the same list. An AI incident is a cyber incident. Remediation of AI-specific issues uses the same framework as general cybersecurity. Deal teams that fold AI into a consolidated cyber diligence process produce an investment committee narrative that is clearer and more defensible.
The AI/Cyber Diligence Framework includes the red/yellow/green assessment matrix, the integration risk checklist, and the post-close remediation scoping template.
Strengthen Your AI/Cyber Due Diligence
Innovaiden works with leadership teams deploying AI agents across their organizations, from initial setup and training to security framework alignment and governance readiness. Reach out to discuss how we can help your team.
Get in TouchFrequently Asked Questions
Where does AI risk hide in a typical data room?
AI risk is distributed across four locations that deal teams routinely look at without connecting them: the product/technology section (architecture diagrams revealing AI dependencies), the vendor/contracts section (AI provider terms with residual training data rights), the legal section (AI-related regulatory filings minimized in the narrative), and the HR/operations section (employee and customer agreements governing AI use).
What are the top red-flag patterns in AI-heavy targets?
Five patterns: ungoverned agent sprawl (agents deployed without inventory or lifecycle management), data residency chaos (regulated data processed through AI providers in non-compliant jurisdictions), vendor lock-in with weak controls (single-vendor dependency with no exit terms), training data provenance gaps (inability to document data sources for proprietary models), and agent-to-production access without human approval gates.
What six questions form the AI/cyber diligence framework?
What AI capabilities are in production and what do they depend on (inventory, not summary). What data do those capabilities process and under what legal basis. Who has accountability for each AI capability. What incidents have occurred in the last 24 months. What contractual obligations exist regarding AI use. What integration risks exist when connecting acquirer and target environments post-close.
Why should AI diligence be integrated into cyber diligence rather than run separately?
Every AI system is a cyber asset. The cyber asset inventory and the AI inventory are the same list. An AI incident is a cyber incident. The incident logs should be the same log. Deal teams that treat AI as a separate diligence category end up doing the work twice. Teams that fold AI into consolidated cyber diligence produce an investment committee narrative that is clearer and more defensible.
What is the post-close integration risk specific to AI-heavy acquisitions?
Three specific risks: the target's AI agents gain access to acquirer systems (blast radius of a compromised agent expands), vendor relationships combine and create concentration risk (a single AI vendor now supports a material share of portfolio operations), and data flows integrate creating new regulated-data exposure (a US target whose AI capabilities now process EU customer data under the acquirer's ownership).
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