Technology due diligence assessment for CTOs

Technology due diligence assessment: how CTOs find the real cost of a codebase

In 2025, 45% of deal teams said technology review was the most expensive and most painful part of M&A due diligence, and half said deals take at least six months end to end (SRS Acquiom M&A Due Diligence Study). CTOs feel that pain differently. A miss in tech diligence turns into a surprise rewrite, a security incident, or a talent exodus in the first 90 days.

A technology due diligence assessment isn’t a code review. The assessment is a structured process that turns product, platform, security, and team facts into a priced risk register and an executable post-close plan.

What is a technology due diligence assessment (and what it is not)

Most CTOs I talk to get stuck on the same thing. They can spot sketchy code in minutes, but they can’t turn that gut feel into deal math.

A technology due diligence assessment answers three questions:

Can the product keep selling? Look at reliability, performance, and roadmap credibility.
Can the platform scale? Look at architecture, data, and cloud posture.
Can the team keep shipping? Look at org health, key person risk, and the delivery system.

A good assessment produces three artifacts you can walk into a boardroom with:

Risk register with severity, likelihood, signals, and an owner.
Value creation backlog with cost, time, and expected impact.
Integration plan for systems, data, and operating model.

Umbrex calls out the same core artifacts in its practitioner playbook, and it frames the workstream as something you can run in weeks, not quarters (Umbrex playbook guide).

A technology due diligence assessment is not:

A one-time static audit.
A vendor tool inventory.
A security questionnaire only.

The assessment needs data. Deal teams often get missing or misleading data, and boutique banks called incomplete target information a top hurdle in 40% of buy-side deals (SRS Acquiom study). Plan for gaps.

Framing statement: technology diligence works when you treat it like underwriting, not like discovery.

How to run a technology due diligence assessment in 2 to 4 weeks

Speed matters. Deal timelines stretch, and diligence adds 1 to 3 months for 59% of teams that see delays (SRS Acquiom study). You still need enough depth to avoid buying hidden work.

I use a simple operating rhythm.

Week 0: set scope, access, and rules

Start with a one-page scope memo. Keep it tight.

Deal intent: tuck-in, platform bet, acquihire, or carve-out.
Systems in scope: customer-facing product, data platform, internal IT.
Depth: read-only access, or hands-on testing.
Red lines: data residency, regulated workloads, customer SLAs.

CBIZ makes a point many CTOs miss. The playbook has to drive immediate execution, and the buyer should line up resources before close (CBIZ IT due diligence playbook tenets). That rule changes what you ask for on day one.

Week 1: build the evidence pack

Interviews help, but systems of record win. TechMiners describes a data-driven diligence approach that pulls from code, tickets, monitoring, and docs, not just conversations (TechMiners guide).

Ask for an evidence pack that includes:

Architecture: current diagrams, deployment topology, data flows.
Code: repo list, dependency graph, test suite stats.
Delivery: last 90 days of PRs, lead time, deploy frequency.
Reliability: uptime, incident log, on-call rota, SLOs.
Security: vuln scan results, IAM model, audit logs.
Vendors: contracts, renewal dates, change of control clauses.

DealRoom highlights vendor contracts, IT staff, maintenance procedures, and security controls as core diligence areas (DealRoom IT due diligence checklist).

Week 2: validate product and platform claims

Marketing claims fall apart under basic tests.

Product demo: run the demo yourself, then ask for edge cases.
Performance: load test one critical workflow.
Data: trace one customer record across systems.
Roadmap: map roadmap items to actual backlog and staffing.

TKO Miller notes that AI claims now trigger a new set of investigations. Buyers want to know if AI is proprietary or licensed, how models train, and how sensitive inputs get protected (TKO Miller due diligence trends). Ask for model cards, training data lineage, and vendor terms.

Week 3: price the risks and draft the 100 day plan

A risk without a cost is noise.

Build a table that ties each finding to dollars, time, and deal terms.

Finding	Evidence	90-day impact	12-month impact	Deal lever
Unpatched critical vulns	Scan report, patch cadence	Breach risk, customer churn	Security program rebuild	Escrow, indemnity, price chip
Single region deployment	Cloud topology	Outage blast radius	Multi-region build	Holdback tied to DR milestone
Key person risk	Bus factor 1 in payments	Delivery stall	Hiring and docs push	Retention package, consulting agreement
License conflicts	SBOM, legal review	Distribution risk	Rework dependencies	Reps and warranties, remediation budget

TransJovanCap gives a blunt data point that helps in negotiations. Transaction audits in 2025 found open source components in 100% of deals reviewed, with license conflicts in 94% and unpatched vulnerabilities in 97% (TransJovanCap checklist). Use that to justify why you need SBOMs and patch data, not promises.

What to assess: the CTO due diligence scorecard

Most checklists sprawl. I prefer a scorecard with eight categories, each with a small set of measurable signals.

PDF.ai lists “Technology and IT Infrastructure Review” as a key diligence area, and it calls out scalability and cybersecurity as core outcomes (PDF.ai due diligence checklist). Use that as the anchor, then go deeper.

Architecture and scalability

Look for constraints that force rewrites.

Deployment model: single-tenant, multi-tenant, hybrid.
State management: shared database, per-tenant schema, event log.
Coupling: number of services that must deploy together.
Scaling ceiling: max QPS seen in prod, and headroom.

Scenario: A SaaS target runs a single Postgres cluster at 70% CPU during peak. The team plans enterprise growth. The buyer inherits a forced migration to sharding or a new data store within 6 to 12 months.

Reliability and disaster recovery

Operational due diligence teams now treat cyber incidents as “when, not if,” and they extend diligence into disaster recovery plans and resilience (DiligenceVault ODD trends 2025). CTOs should do the same.

Ask for:

SLOs: defined targets for availability and latency.
Incident history: count, severity, and time to restore.
Backups: RPO and RTO, plus restore test dates.
Runbooks: on-call handoffs and escalation paths.

Internal link: Pair this with our guide to incident postmortems that change behavior (/tools/incident-postmortem).

Security, privacy, and compliance

Security diligence needs proof, not vibes.

Identity: MFA coverage, privileged access model.
Vulnerability management: patch SLA, scan cadence.
Data controls: encryption at rest, key management, audit logs.
Privacy: data retention, deletion workflows, DPA templates.

AI compliance now belongs in tech diligence. The EU AI Act has phased enforcement through 2027, with prohibitions effective February 2, 2025, and GPAI obligations effective August 2, 2025 (TransJovanCap checklist). Map the target’s AI systems to risk tiers before close.

Codebase health and technical debt

Code quality debates go nowhere without shared measures.

Change failure rate: percent of deploys that cause incidents.
Test coverage: unit and integration, plus flaky test rate.
Dependency freshness: percent of deps behind major versions.

TechMiners gives a concrete example. A dependency chart showing 40% of third-party libraries needing major updates signals rising security and maintenance risk (TechMiners guide).

Internal link: Use our Engineering Metrics Dashboard for DORA metrics (/tools/engineering-metrics-dashboard) to standardize the numbers across targets.

Data and analytics posture

Data drives valuation, and data drives risk.

Data lineage: where PII enters, moves, and exits.
Warehouse: schema ownership, access controls, cost trends.
Retention: deletion SLAs and legal holds.

SRS Acquiom notes that data proliferation expands diligence scope into how companies store, manage, and use data, plus privacy and security risks (SRS Acquiom study). Ask for a data map, not a slide.

Team, leadership, and delivery system

Tech doesn’t run itself. A weak team turns a decent platform into a stalled asset.

Assess:

Org chart: spans, layers, and decision rights.
Turnover: last 12 months, and regretted attrition.
Bus factor: systems owned by one person.
Delivery flow: lead time, WIP limits, release train.

CompanySights calls turnover rate a key metric in diligence, since high turnover signals dissatisfaction or overload (CompanySights metrics). Ask for attrition by team, not a company average.

Internal link: Connect findings to our playbook on scaling engineering org design past 150 engineers (The Art of CTO topic).

Vendor dependencies and IT integration

Vendor risk hides in contracts.

Change of control: consent requirements.
Renewals: auto-renew terms and price ramps.
Critical vendors: auth, payments, data, observability.

DealRoom puts vendor contracts near the top of the IT diligence list for a reason (DealRoom IT due diligence checklist). A single non-transferable contract can block integration.

Internal link: Run vendor decisions through our Build vs Buy Matrix (/tools/build-vs-buy-matrix) once you see the dependency map.

Cost structure and unit economics of the platform

Cloud bills tell the truth.

Infra cost per customer: by cohort and by region.
Gross margin drivers: compute, storage, third-party APIs.
Commitments: reserved instances, minimum spend contracts.

Internal link: Use our Cloud Cost Estimator (/tools/cloud-cost-estimator) to model post-close scenarios like multi-region, new observability, or data retention changes.

Enterprise implications: why technology due diligence changes the deal

Technology diligence isn’t a side quest. The work changes price, terms, and the first-year plan.

You inherit security and compliance debt on day one. Unpatched vulnerabilities and license conflicts show up in most audits, and buyers pay for cleanup after close (TransJovanCap checklist). CTOs should push for escrow, indemnities, and a funded remediation plan.
Integration risk can erase the synergy model. A target with an undersized ERP or manual workarounds can look fine on paper, but it drags operations until you replace systems (CBIZ IT due diligence playbook tenets). CTOs should quantify integration cost and timing, then feed it into the synergy case.
Data quality issues slow the deal and poison the plan. Deal teams report missing and misleading data, and incomplete information drives delays (SRS Acquiom study). CTOs should set a minimum evidence bar and treat gaps as risk items.
AI claims create new inherited obligations. EU AI Act milestones in 2025 and 2026 change what “shipping AI” means in regulated markets (TransJovanCap checklist). CTOs should map systems to risk tiers and budget for conformity work.

CTO recommendations: a repeatable playbook you can run every time

I use a named model so the team can move fast and stay consistent across deals.

The RACE framework for technology due diligence

RACE is a four-step loop.

R, Readiness: Can the target operate safely on day one?
A, Architecture: Can the platform scale without a rewrite?
C, Capability: Can the team ship and support the roadmap?
E, Economics: What does it cost to run and change the system?

A CTO can score each dimension from 1 to 5, then attach evidence and cost.

Immediate actions (next 10 business days)

Access plan: Get read-only access to repos, CI, cloud, and monitoring. Tie access to the LOI.
Evidence pack: Request the evidence pack list, and set a 72-hour SLA.
Red flag review: Run a two-hour session on security, data, and key person risk.
Metrics snapshot: Pull DORA metrics, incident counts, and cloud spend trends.

Policy framework (what to standardize across deals)

Minimum evidence bar: Require SBOM, vuln scan output, and incident history. Treat missing data as a risk item.
Risk register format: Use severity, likelihood, detection signals, and an owner, as Umbrex recommends (Umbrex playbook guide).
Deal term mapping: Define which findings trigger price chips, escrows, or holdbacks.

Architecture principles (what you refuse to buy)

Single point of failure: No single-region production without a funded DR plan.
Opaque data flows: No unknown PII paths, no unclear retention rules.
Bus factor one: No critical system owned by one person without a transition plan.

A practical checklist for the final diligence readout

Use this checklist in the final meeting with the CEO, CFO, and legal lead.

Top 5 risks: Each risk has evidence, cost, and a mitigation owner.
Top 5 value bets: Each bet has staffing, timeline, and expected impact.
Day 1 controls: Access, logging, backups, and incident response.
100 day plan: Sequenced work, with dependencies and staffing.
Deal asks: Price adjustment, escrow, retention packages, and covenants.

Internal link: Track the 100 day plan in Command Center for tech debt, incidents, and capacity (/command-center).

Internal link: Capture the target architecture in ArchiMate Modeler for architecture documentation (/tools/archimate).

Bigger picture: diligence is now a leadership test

Due diligence keeps expanding in scope, and buyers use more third parties and more time, since valuations stay high and scrutiny rises (TKO Miller due diligence trends). CTOs can’t treat diligence like a technical errand that sits outside leadership.

I look for two things in the process.

Trust shows up fast. A target team that hides data or spins stories during diligence tends to behave the same way after close.

Focus matters just as much. A buyer that collects 200 findings and funds none of them will burn out both teams.

Before signing, I want a straight answer to one question: Which risks are we accepting, and which risks are we paying to remove in the first 100 days?

Technology Due Diligence Assessment: How CTOs Find the Real Cost of a Codebase