What Are the Most Common Risks Found in Tech Due Diligence?

The most common risks uncovered in tech due diligence include undocumented technical debt, scalability limitations, security and compliance gaps, dependence on key personnel, unrealistic product roadmaps, and exposure to third-party AI providers. A technology risk assessment for investors examines how these vulnerabilities could affect the investment thesis, what addressing them would cost, and which deserve attention before a deal closes.

Most acquisition targets carry some degree of technical risk. Software evolves, business priorities change, and companies often accumulate workarounds as they grow. The challenge for investors is determining whether those conditions are manageable within the business plan or could undermine the growth, profitability, and operational improvements supporting the acquisition.

The following six areas deserve particular attention:

What should a technology risk assessment for investors establish?

The central question is whether the target's technology can support the strategy the buyer intends to execute.

A technical finding becomes material when it changes the assumptions behind the deal. An outdated framework, for example, may be manageable in a stable business with limited product development needs. The same framework could become a significant constraint if the investment thesis depends on launching new products, integrating acquisitions, or entering additional markets.

A useful assessment connects three elements:

• Technical evidence: What weaknesses exist, and how reliably have they been verified?
• Business consequences: How could those weaknesses affect revenue growth, operating costs, customer experience, or execution timelines?
• Investment decisions: Which findings require adjustments to valuation assumptions, further investigation, or a funded remediation plan?

This approach helps investment teams distinguish ordinary technology maintenance from risks that could materially affect returns.

For a broader explanation of the assessment process, see The Complete Guide to Technical Due Diligence for Acquisitions.

Where these risks usually show up

1. Technical debt that has never been quantified

Technical debt refers to the future effort and cost created by earlier technology decisions, including development shortcuts, outdated frameworks, insufficient testing, and tightly coupled systems.

Some debt is a reasonable consequence of prioritizing speed. The concern arises when a company cannot identify its most significant liabilities, estimate remediation effort, or explain how those liabilities affect future development.

Common warning signs include:

• Minor changes that unexpectedly affect unrelated functionality.
• Releases that depend heavily on manual testing or deployment.
• Unsupported components with known security vulnerabilities.
• Feature estimates that increase because foundational work must happen first.

Reviewers examine code quality, dependency inventories, testing practices, and release history to identify where these problems are concentrated.

For investors, the key question is how much engineering capacity must be redirected toward remediation before the growth roadmap can proceed.

On a multinational, multi-product target in capital project planning, the review surfaced debt the seller had not flagged and the buyer had not priced. The growth model assumed development capacity the team would have had to spend on maintenance first. 

2. Scalability limitations that could undermine growth economics

Scalability risk arises when a company's systems cannot accommodate anticipated increases in customers, transactions, data, or operational complexity without disproportionate costs or performance degradation.

A platform that runs reliably at today’s volume is not evidence that it will hold at three times the load the model assumes. 

Take a software company planning to triple its customer base. The infrastructure holds. Onboarding does not: every new account needs a manual database change, a senior engineer for a day or two, and another seat on a third-party license. Revenue grows, and the cost of serving each customer grows faster. 

A technical assessment should investigate:

• Whether the architecture has been tested against projected demand.
• How infrastructure and licensing costs behave as volume increases.
• Which processes require manual intervention for each new customer.
• What investments are needed to maintain performance at the target scale.

These findings become particularly important when the investment thesis depends on operating leverage.

A platform that needs engineering hours for every new customer will not produce the margins in the model. Understanding that constraint before closing allows investors to evaluate remediation costs and revisit the expected timing of profitability improvements.

3. Security and compliance gaps between policy and practice

Security risk often emerges from the difference between documented controls and the way systems actually operate.

A target may have security policies, access-management procedures, and incident-response documentation. None of that tells you whether the controls are running, who maintains them, or when anyone last tested them.

Reviewers routinely find former employees still in the admin panel, audit logs with gaps, production data sitting in a staging environment, and dependencies with published vulnerabilities that no one has triaged. 

The implications depend on the business and its regulatory exposure. A vulnerability in an internal administrative application presents a different risk profile from one involving patient information, financial transactions, or government data.

The assessment should establish both the severity of the exposure and the evidence supporting the company's security claims.

In one assessment, a control meant to keep each customer's data separate had been written and documented in full. Nobody had switched it on in the application, so it had never run once. The same holds for recovery: if management reports that backups are tested regularly, ask for the restoration test results rather than the procedure.

A lower mid-market firm pursuing a buy-and-build strategy asked us to review a young government administration software company. The deficiencies we documented fed directly into pricing and post-close planning, and the security review surfaced additional risks in the software the fund was acquiring. 

For deal teams, these findings may warrant additional technical verification, remediation estimates, contractual protections, or consultation with legal and compliance specialists.

4. Critical knowledge concentrated in a few people

One engineer deploys the core application. That engineer is also the only person who can fix the billing integration when it breaks, and the only one who remembers why it was built that way. Key-person dependency rarely registers as a risk until the person is gone.

This situation is especially common in founder-led businesses and companies that have grown quickly without formalizing documentation, onboarding, or shared engineering ownership.

The commit history usually shows it before anyone says it out loud: one name on every change to the module that matters most.

When that person leaves three months after close, incidents take longer to resolve, the roadmap slips, and the cost of keeping the system running goes up.

Reviewers should examine whether critical knowledge is distributed across the team and supported by documentation, automated tests, runbooks, and repeatable processes.

Useful questions include:

• Can multiple engineers maintain each business-critical component?
• Are deployment and recovery procedures documented and tested?
• How quickly can a new engineer become productive?
• Which systems depend on external vendors with limited contractual continuity?

Depending on the exposure, investors may incorporate retention arrangements, knowledge-transfer initiatives, or additional engineering capacity into their post-close plans.

The objective is to preserve operational continuity while reducing dependence on individuals whose departure could disrupt the investment strategy.

5. Product roadmaps that exceed the architecture's capabilities

Roadmap risk emerges when a company's planned products, features, integrations, or market expansions require more technology investment or delivery capacity than the business plan assumes.

Take a SaaS company planning to introduce a self-service subscription tier within twelve months. Its existing product was designed for a small number of enterprise customers, with pricing, permissions, and billing rules embedded throughout the application.

Launching the new tier may be commercially attractive, but doing so could require significant architectural changes before product development can progress.

The investor needs to understand that dependency when evaluating time-to-market and expected revenue.

The evidence here sits in the delivery record: how often the team ships, how long a change takes to reach production, how often a release breaks something, and how fast they recover when it does.

These indicators help establish whether the organization can execute the planned roadmap at the required pace.

Strong engineers and a clear product strategy do not compensate for a release process that takes a week and a test suite nobody trusts. 

Most of the cost here is time, and time is the part of the model that is hardest to make up later.

6. Dependence on third-party AI providers

On many AI products, the feature customers pay for runs on a model the company does not own, cannot price, and cannot change.

Pricing, rate limits, deprecation schedules and contract terms all sit with the provider.

The economic implications can become significant as usage increases. A feature that appears profitable at current volumes may deliver weaker margins if inference costs increase disproportionately with customer activity.

Investors should examine several dimensions:

• Provider concentration: How dependent is the product on a single AI vendor?
• Unit economics: What does each AI-powered interaction cost, and how will that change with scale?
• Switching flexibility: Could the business migrate to another model without substantial redevelopment?
• Data governance: What customer or proprietary information is shared externally, and under which agreements?
• Operational reliability: How are model outputs evaluated, monitored, and corrected?

Governance deserves attention alongside the economic analysis. The Stanford HAI 2026 AI Index reports that documented AI incidents increased from 233 in 2024 to 362 in 2025, highlighting the importance of understanding how AI-related risks are monitored and managed.

The practical question for investors is whether the target can demonstrate control over the AI capabilities on which its business depends.

For a deeper assessment framework, see How to Evaluate AI Capability During Technical Due Diligence.

How the investment thesis changes which risks matter most

The significance of a technical finding depends on the buyer's intended strategy.

An integration limitation, for example, may have relatively little impact on a company pursuing organic growth. The same limitation could create substantial cost and delay for a buy-and-build platform expected to integrate multiple acquisitions.

Different investment strategies therefore call for different priorities:

These priorities guide the depth of the review rather than limiting its scope.

For instance, a growth equity investor may emphasize scalability while still treating a serious security exposure as a critical finding. Effective diligence considers the complete risk profile, then concentrates resources on the issues most likely to affect the investment outcome.

How do diligence findings influence deal decisions?

A technology risk assessment for investors should produce findings that can inform valuation, transaction planning, and post-close execution.

Three decision areas are particularly important.

Risks that could change the investment thesis

Some findings challenge a fundamental assumption behind the acquisition.

A platform might require a major rebuild before it can support the growth being underwritten, or a security exposure could create liabilities beyond the buyer's acceptable risk tolerance.

What follows is usually one of a short list: deeper technical work before signing, a lower price, an indemnity or a specific escrow against the exposure, a condition precedent requiring the seller to remediate first, or a decision not to proceed. Which one applies depends on whether the issue can be priced, insured, or only avoided.

Costs that need to enter the financial model

Other findings are manageable but require investment.

Infrastructure modernization, security remediation, vendor replacement, and engineering retention initiatives can all create costs that should be considered before closing.

What the deal team needs is the sizing: how much work, over how many months, with how many engineers, and what that does to EBITDA in the first two years of the hold. A remediation item with no number attached does not make it into the model, which is how it ends up funded out of the growth budget instead.

Improvements that should shape the post-close roadmap

Many findings become actionable priorities for the first year of ownership.

A useful remediation plan assigns each item an owner, a target timeline, estimated cost, and measurable outcome.

For example, an integration modernization initiative could be measured by reduced onboarding time, fewer manual interventions, or lower cost per customer.

These decisions may overlap. A significant architecture issue can influence valuation while simultaneously becoming a critical post-close initiative.

The assessment should also distinguish verified findings from management claims and unresolved questions. Where evidence is unavailable, investors need to understand that uncertainty before assigning confidence to the conclusions.

For more on connecting assessments with portfolio performance, read Tech Due Diligence as a Private Equity Value Creation Tool.

Frequently asked questions

Is technical debt always a red flag in due diligence?

Technical debt is common and can reflect reasonable decisions made during product development. It becomes a significant concern when its scope, remediation cost, or effect on future delivery is unknown. Documented and quantified debt can generally be incorporated into a realistic technology roadmap.

Which technology risks are most likely to affect valuation?

Risks that materially change growth costs, profitability, operational continuity, or execution timelines are most likely to influence valuation assumptions. Examples include major scalability constraints, expensive security remediation, and architectural limitations that delay strategic product initiatives.

Can technology risks identified during due diligence be addressed after closing?

Many can be addressed after closing, provided their cost, urgency, and business impact are understood. Critical security exposures or issues that threaten operational continuity may require action before the transaction or specific protections in the deal structure. Other findings can become part of a prioritized post-acquisition improvement plan.

Do these risks apply to companies that primarily use purchased software?

Yes. Companies relying on commercial software face risks involving vendor concentration, licensing costs, unsupported products, complex customizations, and fragile integrations. The assessment should examine how these dependencies affect flexibility, scalability, and the buyer's ability to execute its strategy.

From technology findings to investment decisions

Technical due diligence creates value when its findings lead to better investment decisions. Understanding the severity, cost, and business implications of technology risks helps investors evaluate acquisitions more accurately and establish realistic priorities for ownership.

At Making Sense, we align each technical assessment with the investment thesis, connecting architecture, security, delivery, and AI readiness to the financial and operational objectives behind the deal. Across the portfolio companies we have worked with, that work has accompanied an average 25% increase in enterprise valuation. Our experience in technology advisory, software development, and modernization also allows us to support investors as identified risks become post-acquisition priorities.

Whether you're assessing a potential acquisition or planning improvements across your portfolio, explore how our technical due diligence services can help you evaluate risks and make informed technology decisions.