Record Completeness
The Taset Framework Completeness Score™
What does Taset Pro check before you finalize and anchor a record?
Before a record is finalized and locked into a Frint™, Taset Pro can review it against a documentation framework you select — SR 11-7 model risk, M&A diligence, Data & Trust Alliance provenance standards, EU AI Act Article 10 dataset documentation, or a custom schema you define.
The review happens entirely in your browser. Your underlying data is not sent anywhere to perform this check.
What It Does
What the Taset Framework Completeness Score does
Compares your record’s fields against the fields required by the selected framework
Identifies which required fields are present and which are missing
Generates a Taset Framework Completeness Score — the percentage of required fields present
Preserves the result inside the Frint, establishing an immutable baseline of completeness.
What It Does Not Do
What the Taset Framework Completeness Score does not do
- Does not verify the accuracy of the content in each field
- Does not determine legal or regulatory sufficiency
- Does not replace review by qualified counsel or compliance staff
- Only checks field presence against the selected template, not substantive adequacy
Why It Matters
Why the Taset Framework Completeness Score matters
A record can exist and still be incomplete. Anchoring a record with missing required fields does not make it complete — it preserves an accurate audit trail of what was missing at that moment.
Completing the record before you lock it into a Frint, where possible, produces stronger evidence. The score is preserved either way.
Frameworks
What is a Taset Pro framework?
Choose a standard or custom checklist for the record. Taset Pro shows which expected fields are present, which are missing, and the resulting field-coverage score.
Taset Pro includes out-of-the-box support for industry-standard frameworks, alongside fully customizable schemas:
Core Dataset Fields
- Dataset title
- Summary of content
- Date collected
- Source or origin
- Type (text, image, etc.)
- Legal license
- Cleaning method
- Contact info
- Purpose
- Biases or restrictions
Taset Pro M&A / Due Diligence
- Dataset name/identifier
- General description of the dataset
- Data sourcing (origin, licenses, consent)
- Provenance and lineage
- Privacy compliance (GDPR, CCPA, EU AI Act)
- Model documentation (versioning, bias testing)
- Third-party AI dependencies
- License to use
- Known limitations and risks
- Intended use
- Collection/generation date
- Owner/contact for inquiries
SR 11-7 Model Risk Governance
- Model Name
- Model ID / Inventory Reference
- Model Owner (Role)
- Model Developer (Team or Vendor)
- Intended Use
- Model Type
- Development / Last Validation Date
- Training / Input Data Sources
- Data Quality Assessment
- Key Assumptions and Limitations
- Independent Validation Approach
- Validation Findings Summary
- Model Approval Status
- Approved By (Role)
- Approval Date
- Ongoing Monitoring Plan
- Known Weaknesses or Compensating Controls
- Model Risk Tier
Data Provenance Standards (D&TA)
- Dataset title
- Description of the dataset
- Unique metadata identifier
- Standards version used
- Dataset issuer (legal entity)
- Dataset issue date
- Method used to collect/generate data
- Data format
- Data origin geography
- License to use
- Intended use and restrictions
EU AI Act – Dataset Documentation
- Dataset name/identifier
- General description of the dataset
- Data type and main characteristics
- Data quality documentation
- Provenance and origin
- Data curation methodology
- Methods to detect bias/unsuitability
- Known limitations and risks
- Intended use
- Collection/generation date
Vendor Compliance & SLA Fields
- Vendor ID / Name
- Agreement reference (MSA/SLA)
- Security certifications (SOC2, ISO 27001)
- Uptime SLA percentage
- Audit rights granted
- Data residency region
- Breach notification window (hours)
- Remediation window (days)
- Support tier & escalation paths
Custom Schemas
Define a custom schema when none of the built-in checklists fit the record type. Add the fields your process requires—custom fields are scored the same way: present, missing, and field-coverage percentage.