Academic transcripts

Identifies documents containing references to academic transcripts in international contexts. This information type is classified as personally identifiable information under applicable data protection regulations.

Type

regex

Engine

boost_regex

Confidence

medium

Confidence justification

category-aware structural regex with anchor and context constraints replaces phrase-only detection. Added context gating and exclusion rules improve precision and reduce incidental matches.

Detection quality

Partial

Jurisdictions

global

Regulations

GDPR

Data categories

pii

Scope

wide

Platform compatibility

Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Degraded, Netskope: Unsupported

Pattern

(?is)\b(?:academic\s+transcript|official\s+transcript|student\s+transcript|academic\s+record|credit\s+hours|grade\s+point\s+average|degree\s+awarded|course\s+history|cumulative\s+GPA)\b

Corroborative evidence keywords

academic transcripts, academic, transcripts, education, research, administration, student, transcript, grade, GPA, enrollment, FERPA, FAFSA, financial aid, tuition, degree, field, column, row, entry (+25 more)

Proximity: 300 characters

Should match

• academic transcript — Primary topic phrase match
• official transcript — Case-insensitive topic phrase match
• student transcript — Alternative topic phrase match
• academic record — Additional topic phrase match

Should not match

• unrelated generic text without domain phrases — No relevant topic phrases present
• placeholder value 12345 — Random text should not match topic-specific regex
• student grant — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to academic transcripts appearing in policy documents, training materials, HR templates, or compliance guidelines without actual personal data. Mitigation: Require corroborative evidence keywords within the proximity window to confirm sensitive data context rather than general discussion.
• In English (as the primary international business language), similar terminology used in formal or administrative contexts (education, professional documentation) that does not constitute sensitive data collection. Mitigation: Layer with additional contextual signals such as structured identifiers, form fields, or database column headers to distinguish sensitive records from general references.
• High-frequency pattern matches in large document corpora due to broad regex anchors. Expected match rate is significantly higher than specific identifier patterns. Mitigation: Tune confidence thresholds for bulk scanning. Consider using this pattern primarily as a pre-filter with secondary validation.

References

• https://www.legislation.gov.au/F2015L01639/latest/text
• https://www.legislation.gov.au/C2004A03712/latest/text
• https://www.teqsa.gov.au/