What PE Buyers Scrutinise in Tech Due Diligence

By the time a private equity buyer or a strategic acquirer sits down to negotiate a term sheet, they've usually already formed a view on your technology. The negotiation that follows, on price, on structure, on what gets held back in escrow, is often decided by what their technical due diligence team found in the weeks before. Most founders and CEOs prepare for the financial and commercial due diligence. Fewer prepare properly for the technology, security and AI review, and that's usually where the surprises, and the price adjustments, come from.

Here is what that review actually examines.

1. Architecture and technical debt

Buyers want to know whether your platform can support the growth plan they're underwriting, not just the business as it exists today. A due diligence team will look at how the system is built, how tightly coupled its components are, how much of the codebase is genuinely current versus legacy, and how expensive it would be to scale the parts of the business the buyer actually cares about. Technical debt itself rarely kills a deal. Technical debt nobody can quantify is what causes a buyer to build in a large, defensive discount, because unquantified risk gets priced as if it's the worst-case risk.

2. Security posture and incident history

Security due diligence isn't a checklist exercise anymore, it's a genuine risk assessment. Buyers want evidence of how access is controlled, how data is segmented, how vulnerabilities are found and patched, and what has actually gone wrong in the past, not just what the policy documents say should happen. A business that can produce a clear, honest account of a past incident and what changed afterwards is in a stronger position than one that claims a spotless record it can't substantiate. Buyers have seen enough deals to be more worried by "nothing has ever happened" than by a documented near-miss with a fix behind it.

3. Key-person risk in the leadership team

This is where technology diligence overlaps directly with people diligence. If your entire architecture, your security posture and your AI roadmap live in the head of one person who founded the company, and that person's role or ownership changes after the deal, the buyer is underwriting a risk they can't easily price. Diligence teams actively probe for this: who actually understands the system end to end, what happens if they leave, and whether critical decisions depend on institutional memory that isn't written down anywhere. A leadership structure that can clearly survive a transition, whether that's documented processes, a capable second layer, or an external fractional leader who already knows the business, reduces this discount meaningfully.

4. Data ownership, IP and third-party dependency

Buyers need to know what you actually own. That means tracing which parts of your product depend on open-source components and under what licence, which parts depend on a single vendor or supplier that could walk away or change terms, and whether your data rights, especially anything involving customer data or anything feeding an AI system, are clearly documented and defensible. A business that has never mapped its own dependencies usually hasn't thought about what happens if one of them breaks, and that's exactly the scenario a buyer is trying to price.

5. AI use and governance

This is the fastest-moving part of any recent due diligence process. Buyers now ask specifically where AI tools are used in the business, what data those tools have been given access to, who approved that access, and whether there's any governance over what a model is allowed to do versus what it's actually doing. A business using AI tools productively but without any record of what's been approved looks, from a buyer's chair, indistinguishable from a business with no AI strategy at all, except that the former also carries an unassessed data exposure. Being able to show a clear, current picture of every AI tool in use, what it touches, and who owns that decision, is quickly becoming as standard a request as a list of active software licences.

6. What it costs to fix, and how that changes the deal

Everything above eventually gets translated into a number. A due diligence team doesn't just flag that your architecture has debt, or that your security controls have gaps, they estimate what it costs and how long it takes to close those gaps, and that estimate shows up directly in the deal: as a lower valuation, as money held back in escrow, as warranties and indemnities written into the sale agreement, or in the worst cases as a walked deal. The single biggest driver of a bad outcome here isn't usually the size of the gaps themselves. It's the business being unable to answer basic questions about its own technology, security and AI posture quickly and consistently, which reads to a buyer as a business that doesn't actually know what it's selling.

Getting ahead of it

None of this is a reason to panic before a process starts. It's a reason to know the answers before someone else asks the questions. Businesses that go through diligence with a clear, current picture of their architecture, their security posture, their key-person risk and their AI governance consistently get through the process faster and with fewer surprises in the final number, because the buyer's biggest discount is always reserved for the things nobody in the business could explain.