Traditional validation assumed you controlled the release schedule. Cloud and SaaS removed that assumption and left the obligation exactly where it was.
A platform that updates on the vendor’s calendar cannot be validated as an event. It has to be maintained as a state.
Open a validation package for a low-risk administrative system. The volume is frequently indistinguishable from one for a system that releases product.
That is not stupidity. Nobody is ever criticised for producing too much documentation, and scripted testing of every function is easier to defend than a reasoned argument about where the risk actually is.
The result: packages measured by weight, in which the tests that would catch a consequential failure are a small proportion of the total.
Volume correlates with anxiety, not with risk
Low-risk systems carrying packages indistinguishable from critical ones.
The vendor controls the release calendar now
And the validation model assumed you did.
Testing is manual, so it cannot be repeated cheaply
Which is exactly what a six-week release cycle requires.
Evidence is scattered across documents, tools and inboxes
So inspection readiness becomes an assembly project.
Requirements were vague, so sufficiency cannot be demonstrated
A test suite can only be adequate against a stated intended use.
AI systems are assessed with a deterministic playbook
Which assumes the same input gives the same output.
Count the vendor releases that hit your validated systems last year. For each GxP-relevant cloud and SaaS system, count the vendor releases in the last twelve months, how many were assessed for change impact, how many triggered testing, and how long each assessment took. Compare that to the capacity available. Most organizations find the arithmetic does not work and has not for a while, which is usually being absorbed by assessing less thoroughly rather than by anybody deciding to. A day of work, and the clearest argument for automation and better supplier terms that exists.
Almost every validation problem traces back to one of the first two steps being done loosely.
An imprecise intended use makes sufficiency undemonstrable. An unexamined risk assessment makes effort arbitrary. Everything after those two is execution.
Step 1
Assess the system and its context
Step 2
Classify against your framework
Step 3
Define intended use precisely
Step 4
Evaluate risk to patient, product and data
Step 5
Establish testable requirements
Step 6
Plan assurance proportionate to risk
Step 7
Execute testing, automated where it repeats
Step 8
Document evidence as it is produced
Step 9
Manage deviations transparently
Step 10
Approve release with a stated residual risk position
Step 11
Monitor in operation
Step 12
Maintain through change and periodic review
Write down what you are not testing, and why. The hardest and most valuable artefact in a risk-based approach is the record of decisions not to test, with the reasoning behind each. It is uncomfortable to write, a reviewer will read it most carefully, and it is the only document that demonstrates critical thinking rather than compliance by volume. Organizations that produce it find the discussion changes: the argument stops being about how much documentation exists and becomes about whether the reasoning is sound, which is a conversation a competent quality function can win.
Grouped by where in a system’s life the work sits. Most engagements start either with a system being implemented, or with an estate where maintenance has fallen behind the release cadence.
Decide the approach
GxP system assessments and classification, with intended use scoped to the functionality you actually rely on
Risk assessment across patient, product and data impact, with documented reasoning
Validation and assurance planning, including what will not be tested, and supplier assessment positioned at selection and contract
Do the work
Requirements and traceability from intended use through to evidence, with installation, operational and performance qualification where that structure applies
Automated testing for the paths that will be re-run on every release
Exploratory testing where judgement finds more than a script, with documentation produced as a by-product of the work
The specific hard cases
Cloud and SaaS validation designed around a release cadence you do not control
Data migration validation covering records, relationships, attachments and audit history, and interface validation tested as behaviour
Electronic records and signature controls, and AI assurance on the basis set out below
Keep it valid
Change impact assessment as a standing process with an owner and a service level, and release approval with a stated residual risk position
Periodic review that tests whether the system still matches its intended use
Audit trail monitoring, access review, and lifecycle management including legacy remediation and decommissioning
Across almost all of these the expensive mistake is the same: validating a platform instead of a use.
The third column is the scoping question that decides whether the work is proportionate or enormous.
| System or activity | What it does | The scoping question that decides the cost |
|---|---|---|
| eQMS validation | Quality processes, records, workflow, approvals | Which configured workflows and records are relied upon |
| LIMS validation | Results, methods, instruments, calculations | Which calculations and result paths affect disposition |
| ERP GxP functionality | A large system where only part is regulated | Exactly which transactions and master data are in scope |
| SaaS upgrades | Mandatory vendor releases on the vendor calendar | Which paths must be regression tested every release |
| AI-assisted regulated workflows | Model-based components in a controlled process | Where the boundary is and where the human decision sits |
| Data migration | Moving regulated records between systems | Records, relationships, attachments and audit history, not just rows |
| Legacy modernization | Systems that cannot meet current expectations | Whether the answer is remediate, compensate or replace |
Requirements tools, test management, pipelines and issue trackers are sometimes treated as incompatible with regulated work, on the grounds that they are not validated.
The better question is whether the records they hold are controlled, attributable and traceable. On that test, a properly configured lifecycle tool outperforms a folder of documents comfortably.
| Tool | What it is for | What it must satisfy to be usable here |
|---|---|---|
| Requirements and test management | Requirements, cases, execution records, results and defects as linked objects | Attributable change history, controlled approval, evidence retrievable later |
| Automated test frameworks | Regression re-run on every vendor release | The automation assessed, and its results trustworthy as evidence |
| ALM platforms | Requirement to evidence in one traceable chain | Access control, audit trail, no silent edit of an executed result |
| CI and CD pipelines | Build, test and deployment with gates | Deployment controls, and evidence generated by the pipeline itself |
| Evidence repositories | The assembled validation record, retrievable on demand | Retention, integrity and retrieval without a project |
| Audit trail monitoring | Detecting exceptions in operation rather than at review time | Coverage of the trails that matter and a route to an owner |
Time how long it takes to assemble the evidence for one system. Pick a validated system and ask somebody to produce its complete current evidence: plan, requirements, traceability, test results, deviations, approvals, change history since release, periodic review. Time it. If the answer is days, and it frequently is, inspection readiness is a project every time rather than a state. The gap is almost always that evidence lives in documents and inboxes rather than in a controlled repository with the relationships intact. That measurement is also the business case for the tooling, in terms a quality director recognises.
Several controls in this section are widely implemented and rarely operated.
Which frameworks apply, how a system is classified and what assurance is required are determined by your quality and regulatory functions.
Frameworks where they apply
- GxP, and GMP, GLP or GCP according to the activity the system supports
- GAMP 5 risk-based lifecycle approaches where your quality function has adopted them
- FDA 21 CFR Part 11, EU GMP Annex 11 and FDA Computer Software Assurance guidance, where applicable and as adopted by your quality function
Data integrity
- ALCOA plus principles applied to the records the system creates and holds
- Audit trails reviewed, not only enabled, on a risk-based schedule
- Original records retained with controlled correction, and retention that survives system replacement and supplier change
Access and duties
- Role-based access with least privilege, and periodic review that is performed
- Segregation of duties, with compensating controls documented where it is not achievable
- Electronic signatures to your requirements, and administrative access controlled and monitored because that is where risk concentrates
Lifecycle control
- Change control covering configuration, integrations, automation and supplier releases
- Supplier assessment and ongoing supplier performance as an assurance input
- Periodic review against intended use, with documented risk-based reasoning including what was not tested
Validation metrics tend to measure production: tests written, tests executed, documents approved. Those describe activity.
The measures that describe assurance are about coverage of the stated intended use, the health of the traceability chain, and whether the estate is keeping pace with the change arriving at it.
| Failure | How it presents |
|---|---|
| Coverage is measured against features | High coverage of functionality nobody uses, gaps in what the process relies on |
| Traceability is broken in the middle | Requirements and tests both exist and nothing links them to evidence |
| Change assessments are accumulating | Releases arrive faster than they are assessed and the gap compounds |
| A deviation was closed by justification nobody read | The package is complete and the residual risk was never actually accepted |
| Periodic review became a documentation check | It confirms the paperwork exists rather than that intended use still holds |
| Audit trail review finds nothing, ever | Which usually means it is being signed rather than performed |
| The supplier stopped providing what you rely on | A lighter effort predicated on evidence that no longer arrives |
| Automated tests pass on a broken system | The automation was never negatively tested and now gives false assurance |
Validation programmes report documents produced and systems validated.
The outcomes that matter are whether you can absorb change at the rate it arrives, whether evidence can be produced on demand, and whether the effort is now where the risk is.
| Outcome | How to tell |
|---|---|
| Unnecessary documentation reduced | Effort is proportionate to risk and the reasoning is written down |
| Releases accelerated | Vendor releases are assessed within their window and the backlog is stable |
| Inspection readiness strengthened | Complete evidence for a system is retrievable in hours, not days |
| Electronic records reliable | Audit trails are monitored and exceptions reach an owner |
| Cloud and AI adoption supported | New systems are adopted with a defensible approach rather than deferred |
| Assurance is sustainable | Unassessed change, periodic review and supplier performance are all stable |
We support validation and compliant operation for pharmaceutical, biotechnology and manufacturing organizations: intended use and risk-based planning, requirements and traceability, automated regression for release cadences you do not control, cloud and AI assurance, migration and interface validation, and the lifecycle work afterwards. Your quality and regulatory functions own the strategy, the risk acceptance and the approvals throughout.
CaliberFocus supports validation and compliant operation for pharmaceutical, biotechnology and manufacturing organizations. We start from a precise intended use and a documented risk assessment, put assurance effort where the risk is, automate regression for release cadences you do not control, and keep systems validated through change and periodic review, while your quality and regulatory functions own the strategy and approvals.
- Intended use scoped to the functionality you actually rely on
- Risk-based effort with the reasoning, and what was not tested, written down
- Automated regression for cloud and SaaS release cycles
- Lifecycle work that keeps a system validated after the vendor ships
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