Regulatory Tags

Regulatory tags are structured classifications that encode safety, legal, institutional, and quality requirements directly into Flask Track’s operational data model.

They allow Flask Track to understand regulatory context automatically by analyzing the contents of laboratory work, including:

• Species
• Samples
• Batches
• Workflows
• Protocols
• Protocol steps
• Ingredients
• Tools
• Plasmids
• Agrobacterium strains

Regulatory tags are not simple labels or documentation notes. They are machine-readable compliance signals used by the compliance engine to derive risk, determine applicability, map requirements, and enforce operational controls.

What Is a Regulatory Tag?

A regulatory tag is a structured classification attached to an entity.

Tags may indicate:

• Biosafety level
• GMO or recombinant DNA involvement
• Restricted or controlled material usage
• Pathogen classification
• Special handling requirements
• Institutional review requirements
• Operational containment requirements

Because tags are structured, Flask Track can use them to:

• Derive compliance surfaces
• Trigger applicable frameworks
• Map requirements to severity levels
• Apply authorization rules
• Require approvals
• Block unsafe or unauthorized actions
• Scope audits and reports automatically

Each tag may also include human-readable notes explaining why the classification applies.

Why Regulatory Tags Matter

In many laboratories, regulatory classification is handled manually through SOPs, spreadsheets, PDFs, or institutional knowledge.

This creates risk because regulatory context may be:

• Applied inconsistently
• Missed during execution
• Disconnected from protocols and samples
• Difficult to audit
• Hard to scale across teams

Flask Track embeds regulatory context directly into the operational model.

This allows compliance requirements to follow the work automatically as protocols, materials, tools, samples, and batches are connected.

Compliance Surface Derivation

Flask Track uses regulatory tags to automatically derive the compliance surface of an entity.

A compliance surface is the complete set of regulatory classifications that apply to a sample, batch, workflow, protocol, or other operational record.

For example, a sample’s compliance surface may be derived from:

• The sample’s species
• The batch it belongs to
• The workflow used
• The protocols in that workflow
• The protocol steps executed
• The ingredients used
• The tools required
• Any plasmids or strains involved
• Any direct regulatory tags applied to the sample

This means compliance status is not manually assigned.

It is calculated from the actual contents and relationships of the work.

Example Compliance Surface

A batch may inherit regulatory context from several sources:

The resulting batch compliance surface may include:

• GMO-related controls
• Restricted material requirements
• BSL-2 containment expectations
• Required approval workflows
• Audit evidence requirements

This allows Flask Track to detect compliance obligations from real operational configuration.

Mapping Tags to Framework Severity

Regulatory tags become operationally meaningful when mapped into compliance frameworks.

A tag such as gmo, restricted, or bsl2 may have different implications depending on the framework being evaluated.

For example:

This framework-specific mapping allows organizations to define how serious a tag is under each compliance program.

Framework Rules

Compliance frameworks may define rules that map regulatory tags to:

• Severity levels
• Required approvals
• Required checklists
• Blocking behavior
• Evidence requirements
• Certification requirements
• Audit scope

This allows the same operational entity to be evaluated differently under different compliance frameworks.

For example, a plasmid may trigger one set of requirements under a recombinant DNA framework and another set under an institutional biosafety framework.

Authorization & Enforcement

Regulatory tags may trigger authorization rules during execution.

Depending on framework configuration, Flask Track may:

• Allow execution
• Require approval before execution
• Require checklist completion
• Require evidence upload
• Require certified personnel
• Block the action entirely

This ensures regulatory classification is enforced during operational work, not reviewed only after completion.

Preloaded Regulatory Tags

Flask Track may include a preloaded set of common regulatory tags to help organizations start with sensible defaults.

Biosafety Levels

Examples:

• bsl1
• bsl2
• bsl3

These tags indicate containment or biosafety expectations associated with work.

Genetic Modification

Examples:

• gmo
• non_gmo
• recombinant_dna

These tags identify work involving genetic modification, recombinant DNA, or synthetic nucleic acid workflows.

Pathogen Classification

Examples:

• human_pathogen
• plant_pathogen
• animal_pathogen

These tags indicate organism-specific risk classifications.

Restricted & Controlled Materials

Examples:

• restricted
• controlled

These tags identify materials or procedures that may require:

• Approval
• Access control
• Special handling
• Storage restrictions
• Disposal tracking
• Procurement oversight

Where Tags Can Be Applied

Regulatory tags may be attached throughout the operational model.

Species

Species tags describe inherent biological or regulatory characteristics.

Examples:

• Pathogen classification
• Biosafety relevance
• Organism-specific restrictions

Protocols

Protocol tags describe the regulatory nature of the procedure.

Examples:

• GMO work
• Sterilization procedure
• Transformation action
• Disposal procedure

Ingredients

Ingredient tags describe material-level restrictions or hazards.

Examples:

• Antibiotics
• Controlled reagents
• Hazardous chemicals
• Restricted biological materials

Tools

Tool tags describe required equipment or containment context.

Examples:

• Biosafety cabinet requirement
• Fume hood requirement
• Certified containment equipment

Plasmids & Strains

Plasmids and Agrobacterium strains may carry recombinant DNA, transformation, or GMO-related regulatory context.

Examples:

• Recombinant construct
• Transformation strain
• Restricted vector
• Selectable marker context

Workflows

Workflow tags represent the aggregate regulatory nature of a full operational process.

Examples:

• Transformation workflow
• BSL-controlled process
• Regulated disposal process

Batches & Samples

Batches and samples inherit regulatory context from upstream configuration and execution history.

This allows Flask Track to determine the final compliance surface of real laboratory work.

Tag Inheritance

Regulatory tags propagate through entity relationships.

For example:

• A workflow contains a GMO-tagged protocol
• The protocol uses a restricted antibiotic
• The step requires a BSL-2-tagged tool
• The batch applies the workflow to a species with pathogen relevance

The resulting batch and samples inherit the combined regulatory surface.

Inheritance ensures compliance context cannot be accidentally lost as work moves from planning into execution.

Direct Tags vs Derived Tags

Flask Track supports both direct and derived regulatory context.

Direct Tags

Direct tags are explicitly assigned to an entity.

Examples:

• A plasmid directly tagged as recombinant_dna
• An ingredient directly tagged as restricted
• A tool directly tagged as bsl2

Derived Tags

Derived tags are inherited from related entities.

Examples:

• A sample inheriting gmo from a transformation protocol
• A batch inheriting restricted from an antibiotic used in a workflow
• A workflow inheriting bsl2 from a required containment tool

Both direct and derived tags contribute to compliance evaluation.

Tags vs Compliance Events

Regulatory tags describe what something is or what regulatory context applies.

Compliance events describe what happened.

Examples:

• gmo is a regulatory tag
• A failed transformation containment review is a compliance event
• restricted is a regulatory tag
• A missing approval is a compliance event

Together, tags and events provide complete regulatory traceability.

Auditing & Reporting

Regulatory tags enable automated audit scoping and compliance reporting.

Auditors can answer questions such as:

• Which workflows involve GMO-related work?
• Which batches used restricted materials?
• Which samples inherited BSL-2 requirements?
• Which protocols require approval under the biosafety framework?
• Which tools are part of regulated workflows?
• Which plasmids contributed to recombinant DNA classifications?

Because tags are structured and inherited, these answers can be derived from system relationships rather than manual review.

Best Practices

Recommended practices include:

• Apply tags to source entities whenever possible
• Use direct tags for stable regulatory classifications
• Let derived tags flow through workflows, batches, and samples
• Avoid using tags as freeform notes
• Map tags carefully to framework severity levels
• Review framework mappings during compliance updates
• Keep tag definitions stable for historical auditability
• Use compliance events for incidents, deviations, and corrective actions

Strong tag hygiene improves compliance accuracy and audit readiness.

Summary

Regulatory tags encode biosafety, legal, institutional, and quality classifications directly into Flask Track’s operational model.

By attaching tags to species, protocols, ingredients, tools, plasmids, workflows, batches, and samples, Flask Track can automatically derive compliance surfaces based on the real contents of laboratory work.

Those derived surfaces can then be mapped into compliance frameworks, severity levels, authorization rules, checklists, approvals, and audit reports.

Regulatory tags are the foundation of execution-aware compliance in Flask Track.