KVKK / GDPR personal data protection
Detect personal data in documents and calls, mask it, and keep an audit-ready data map.
KVKK / GDPR personal data protection is an Aginies solution package for the Enterprise Functions sector in the “data protection & compliance” area. What the agents do: The KVKK Personal Data Detection agent finds and classifies national IDs, IBANs, health and contact data in documents and conversations; the KVKK Document Masking agent masks or anonymises fields before data reaches any model. Expected outcomes: personal data masked before it reaches a model; an audit-ready, current data inventory; compliance risk in document and call archives made visible. The package uses the PII detection, PII masking, Entity extraction, On-prem deployment capabilities of the Aginies platform and can be watched live through the Underwriting document extraction, Mortgage document review, Call centre quality monitoring, Email classification and attachment check scenarios in the agent library.
Personal data is scattered across contracts, emails and call recordings; nobody knows exactly what sits where. Every document and conversation sent to a model carries KVKK and GDPR risk unless it is masked.
- 01The KVKK Personal Data Detection agent finds and classifies national IDs, IBANs, health and contact data in documents and conversations.
- 02The KVKK Document Masking agent masks or anonymises fields before data reaches any model.
- 03Findings feed a data map: which data, in which system, for what purpose, for how long.
- 04Out-of-policy sharing and erasure requests route to the compliance team; every decision is kept with an audit trail.
- Detection and classification of personal data in documents and conversations
- Masking, anonymisation and retention rules
- Recording and response process for data-subject requests (access, deletion)
- Data inventory and processing-record updates
- Read access to document stores and communication channels
- Data inventory and retention policy
- Approval rules from the data controller and the KVKK team
Derived from the linked scenarios: the supervisors, specialist agents, models and systems that run this package.
Summed from the simulation definitions of the linked scenarios; recalculated for your scale in the discovery session.
- Personal data masked before it reaches a model
- An audit-ready, current data inventory
- Compliance risk in document and call archives made visible
| Personal-data detection accuracy | 97% and above |
| Response time to data-subject requests | Half the statutory deadline |
| Manual masking effort | 80% lower |
Target ranges from comparable deployments; baseline and target are fixed together in the first week of the pilot.
Scenario-level approval rules plus the package’s general controls.
- Underwriting document extractionLoading ≥ 50% or sum assured > 5,000,000 → senior underwriter approval
- Mortgage document reviewLoan > 3,000,000 or DTI > 45% → underwriting officer approval
- Call centre quality monitoringScore < 60 or compliance breach → team leader review
- packageDeletion and external sharing always under the data controller’s approval
- packageThe detection model runs on-prem; data never leaves Türkiye
- 1 · Rule and control design · 2–3 weeksRegulatory articles translated into the control set, the decision matrix approved with compliance, trace requirements written.
- 2 · Shadow period · 4 weeksThe agent produces findings and recommendations, the expert decides; disagreement rate and reasoning quality measured.
- 3 · Audit-ready operationControl evidence collected automatically; audit reports and the exception register opened to internal audit.
This package’s scenarios in the agent library
Underwriting document extraction
Medical reports and labs read and assessed against the manual; underwriters only see files above threshold.
Mortgage document review
Payslips, social security records and appraisal read automatically; income, LTV and missing documents checked.
Call centre quality monitoring
Every call scored against the rubric, not a sample; compliance breaches and coaching flags ready by morning.
Email classification and attachment check
Every e-mail in the shared mailbox is classified, attachments checked against a list, gaps asked for at once.
Time to move from experimenting with AI to transforming with it.
In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.
- Your 2–3 priority problems
- Live demo of a similar scenario
- Roadmap starting with value analysis