By a DevSecOps Practitioner Who Has Seen Both the Breakdowns and the Breakthroughs
If you spend your days inside CI/CD pipelines, Kubernetes clusters, cloud consoles, and compliance audits—as I do—you eventually discover a brutal truth:
Most security incidents, compliance penalties, and failed digital transformations have nothing to do with bad code.
They happen because the company never built strong data architecture and governance.
In 2025, as more tech-first companies scale on cloud-native platforms, ship features aggressively, adopt AI, and integrate dozens of data sources, the real blind spot has become how data is structured, protected, moved, classified, and recovered.
This isn’t a “data engineering problem.”
This is a DevSecOps problem, because broken data governance breaks pipelines, breaks compliance, breaks predictability—and eventually breaks trust.
In this blog, I’ll break down the six pillars of Data Architecture & Governance that every DevSecOps leader must evaluate during Tech Due Diligence — and share real-world examples of what happens when companies don’t.
1. Why Data Governance Is Now a DevSecOps Responsibility
Five years ago, data governance sat squarely with data teams. Today, with infrastructure-as-code, GitOps, AI pipelines, and ephemeral cloud resources, data security and data reliability now sit inside the delivery pipeline itself.
Industry research reflects this shift:
- IBM reports that data breaches cost companies an average of $4.45M in 2023, with misconfigured cloud data stores among the top 3 incident drivers.
- Gartner predicts that by 2026, 70% of data governance failures will stem from DevOps pipelines integrating uncontrolled data flows.
Tech-first companies depend on connected datasets, API ecosystems, micro-services, and automated decisioning systems.
This means:
If you can’t trace your data,
you can’t secure your data,
and you definitely can’t scale your platform.
That is why modern Tech Due Diligence places disproportionate weight on one question:
“Is your data governed end-to-end, or are you operating on trust and luck?”
2. Data Lineage: The Single Most Valuable Artifact During Due Diligence
Data lineage is not documentation — it’s the x-ray of the entire company.
It should tell you:
- Where data originates
- Every system it touches
- Every transformation it undergoes
- Every pipeline, dashboard, API, or model that depends on it
In due diligence, missing lineage is a red alert. It means:
- Metrics cannot be trusted
- AI models may be trained on unverified or non-compliant data
- Feature flags, experiments, or analytics pipelines may break unexpectedly
- Regulatory reporting may be inaccurate
A Secoda analysis found that over 60% of data teams admit lineage only exists “in people’s heads.”
This is an unacceptable risk for a tech-first company preparing for scale, investment, or acquisition.
REAL CASE IMPACT
A fintech SaaS platform I evaluated in 2024 had grown quickly but lacked lineage across its payment analytics pipeline. A schema change upstream modified a column type; downstream fraud models silently degraded for 17 days before anyone noticed.
Loss: $2.4M in chargeback exposure, all avoidable.
3. Data Cataloging: The Foundation for Access Control & Compliance
If lineage is the x-ray, cataloging is the map.
A proper data catalog must include:
- Business glossary
- Technical metadata
- Sensitivity classification (PII, PHI, PCI, etc.)
- Ownership
- Update cycles
- Downstream dependencies
- Quality indicators
Without a catalog, DevSecOps pipelines can’t enforce:
- Least-privilege access
- Automated masking
- Policy-as-code
- Compliance guardrails
- Encryption coverage
- Security scanning for sensitive datasets
According to Alation, companies with a mature data catalog experience 40% fewer data incidents because “shadow data” becomes visible and governable.
4. Access Controls & Encryption: Where 80% of Breaches Still Happen
Misconfigured access controls are the #1 root cause of cloud breaches, per Verizon’s DBIR report.
During due diligence, I look for:
- Zero Trust enforcement
- Role-based access controls (RBAC)
- Attribute-based policies (ABAC)
- MFA enforcement
- Secrets management in pipelines (Vault, AWS Secrets Manager, etc.)
- Field-level masking
- Encryption at rest + in transit
- Key rotation policies
- Access audits tied to SOC 2 workflows
Every tech-first company claims “we encrypt everything.”
Very few can prove:
- Which datasets
- With what keys
- Stored where
- Managed by whom
- Rotated how
- Audited when
Without this, “encryption” is a checkbox—not protection.
5. PII/PHI Protection & Compliance: The Make-or-Break Section of Any Due Diligence Report
If a company deals with:
- Health data
- Financial data
- User identities
- Behavioral analytics
- Telemetry
- Biometrics
- Customer communications
…then compliance readiness (HIPAA, GDPR, SOC 2, PCI, or regional laws) is not optional.
A 2023 Cisco report states that 92% of consumers will not engage with companies that fail to protect their data.
During an acquisition, compliance gaps translate directly into valuation hits.
A common example:
I once reviewed a healthcare SaaS startup claiming HIPAA readiness. Their application logs contained PHI because their debug mode logged request bodies.
Cost of remediation:
- 6 engineers × 12 weeks
- $650K in re-architecting
- $2.1M lost in valuation adjustment
Compliance failures are expensive, but compliance illusions are deadly.
6. Backup, Restore, and Disaster Recovery Drills: The Most Overlooked Risk in Tech-First Companies
Engineers obsess over shipping features.
Executives obsess over growth.
Investors obsess over valuation.
Almost nobody obsesses over restore-time reliability — until things go south.
Industry data is sobering:
- 48% of organizations cannot recover from a ransomware incident within acceptable RTO/RPO windows (Datto 2024)
- 75% of companies discover backup failures only after attempting a restore (Veeam 2023)
In DevSecOps audits, I simulate a simple test:
“Restore your primary customer database to last Thursday at 3 p.m.”
If the answer requires more than 5 steps—or more than 10 minutes of explanation—the company is not ready.
CASE STUDIES: A NIGHTMARE OF GOVERNANCE FAILURE
Case Study: Capita (UK outsourcing firm)
The 2023 Capita breach, which exposed data of 6.6M people, was traced back to a backup service account with domain-admin privileges and no monitoring.
The outcome:
- £14M in penalties fined in Oct 2025
- Millions spent on remediation
- Permanent reputational damage
One poorly governed backup process became a national-scale crisis.
Lessons:
- Backup infrastructure is part of your data architecture and governance. If it’s mis-configured, attackers exploit it.
- Failure to restrict and audit privileged accounts undermines entire control frameworks.
- Financial and reputational cost: tens of millions in penalties and remediation.
Case Study: SingHealth (Singapore healthcare)
In 2018, SingHealth suffered a data breach where 1.5 million patient records were stolen including that of the Prime Minister. The inquiry cited unpatched servers, lack of escalation procedures, insufficient monitoring—fundamentally governance failings in architecture.
Broader Data Governance Failures: Citigroup
Citigroup has been cited for compliance failures (risk management and data governance) that resulted in over US $1.5 billion in fines from U.S. regulators over time. ovaledge.com
These cases illustrate how neglecting the components listed above—even if everything else seems fine—can lead to deep troubles.
What Happens When Companies Ignore Data Architecture & Governance
Here’s what I’ve personally seen in technology due diligence when governance is weak:
1. Valuation Drops 10–30%
Acquirers discount for:
- Tech debt
- Compliance exposure
- Migration cost
- Security risk
2. Cloud Costs Spiral
Without cataloging, lineage, and ownership, infra costs rise 30–70%.
3. AI & Analytics Become Unreliable
Models drift or hallucinate because input datasets are untracked, inconsistent, or stale.
4. Compliance Becomes a Minefield
GDPR “right to be forgotten”?
HIPAA audit?
SOC 2 Type II?
If lineage and access control are missing, audits fail instantly.
5. Incident Response Fails
You cannot contain what you cannot trace.
Final Word: Cost of Inaction
Failing to invest in governance and architecture is not just a technical failure—it’s a business failure. The examples above show the financial and reputational cost: multi-million fines, breached customer trust, slow-downs, remediation cost. According to industry research: “Data governance helps organisations to ensure that data is accurate, consistent, and compliant… Without it, organisations run the risk of making wrong decisions, violating regulations, and losing customer trust.” Domo
For a tech-first company undergoing due diligence (for investment, acquisition, regulatory audit, scale-up), any weakness in this area signals elevated risk and can materially affect valuation, closing risk, or post-deal integration cost.
Addendum – Removed from blog – part of research – can use elsewhere or in another blog.
What Good Looks Like: The Gold Standard for 2025
When doing tech-due-diligence on a tech-first company, here are both the red-flags and good-practices to evaluate in the data architecture & governance domain:
Red Flags
- No documented data catalog, metadata inventory, data owner mapping.
- Unknown/un-measured data lineage: you cannot trace business-critical metrics back to their source.
- Weak access controls: broad admin rights, un-segmented network zones, few audit logs.
- Encryption absent or inconsistent (at rest / in transit).
- Backup/restore plans that are untested, not documented, or RTO/RPO targets missing.
- No evidence of regulatory alignment (e.g., for PII/PHI).
- Multiple systems added ad hoc, data silos, inconsistent definitions, little stewardship.
Good Practices
- A live data catalog with owners, sensitivity classification, last-updated metadata.
- An automated or semi-automated lineage system showing data flows (table/column level) and dependencies. Dataversity+1
- Robust access governance: role-based access, least privilege, audit logs, periodic reviews.
- Encryption and masking of PII/PHI fields; classification of data assets with controls aligned to that classification.
- Backup & DR documented, tested regularly, with clear RTO/RPO and scenario-based drills.
- Clean architecture: zones for ingestion/staging/curation/analytics, clearly defined data flows, governance oversight.
- Compliance evidence: SOC 2/ISO 27001/HIPAA/GDPR mapping, policy documents, periodic compliance audits.
Why This Is Especially Critical for Tech-First Companies
Tech-first companies (e.g., SaaS platforms, data-driven products, marketplaces) are often built on large flows of data, fast iterations, APIs, integrations, ML models, and third-party dependencies. That means:
- There are many ingress/egress points, many moving parts—so mis-governed data architecture quickly becomes uncontrolled.
- Investors and acquirers will place a premium on “data readiness” for scaling/trade-sale: can the company quickly integrate data, spin up analytics, merge systems? Poor architecture slows you down.
- Compliance/regulation risk is amplified because these companies often handle PII/PHI/fiscal data, operate cross-border, and must prove their controls.
- The cost of remediation is high: messy data architecture means rewrites, migration, cleanses; regulatory fines; loss of trust from enterprise customers.
A Suggested Governance Framework (High-Level)
For a tech-first company, data architecture & governance should be anchored around the following pillars:
- Vision & Strategy: Define what “trusted data” means for the organisation; align to business goals and compliance/regulatory domain.
- Catalog & Metadata: Build a central data catalog with business definitions, owners, sensitivity labels.
- Lineage & Dependency Mapping: Implement tools or processes to capture and visualise data flows (source → transform → sink).
- Access & Security Controls: Role-based access, least privilege, encryption, audit logs, classification schemes.
- Compliance & Regulatory Readiness: Maintain mapping to GDPR/HIPAA/SOC 2 etc, have documented policies, incident response plans.
- Resilience & Recovery: Backup/restore strategy, DR plans, periodic drills, monitoring, RTO/RPO targets.
- Stewardship & Ownership: Assign data owners/stewards in each domain, embed responsibilities for data quality, access, lifecycle.
- Continuous Improvement & Monitoring: Metrics around data quality, access requests, incident rates; periodic governance reviews.
