Official public technical overview and resource center for Siberson Veriket Data Discovery.
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This is the public documentation repository for Siberson Veriket Data Discovery.
It is intended to provide:
- A public product and DSPM overview
- High-level discovery architecture
- Sensitive-data detection and risk concepts
- Integration patterns
- Evaluation and proof-of-concept guidance
- Links to official Siberson resources
- Searchable technical content for customers, partners and security teams
This repository does not contain:
- Proprietary Veriket source code
- Private product-development repositories
- Internal detection rules or model internals
- Customer-specific configurations
- Production credentials, secrets or certificates
- Confidential architecture or sizing documents
- Unreleased roadmap information
- Internal support tickets or vulnerability details
Siberson maintains separate private repositories for proprietary development and internal engineering workflows. This public repository is intentionally limited to approved, non-confidential content.
Siberson Veriket Data Discovery is an enterprise sensitive-data discovery and Data Security Posture Management platform designed to help organizations find, inventory, map and prioritize regulated and business-critical data across on-premises, cloud and hybrid environments.
Veriket Data Discovery can support visibility across:
- Structured databases
- Unstructured files
- Endpoints
- File servers and network shares
- Email and collaboration environments
- Microsoft 365 repositories
- Cloud storage
- Hybrid enterprise data estates
The platform helps security, privacy, compliance and data-governance teams answer four operational questions:
- Where is sensitive data located?
- What type of sensitive data is present?
- How exposed or risky is that data?
- Which findings should be remediated first?
Organizations cannot reliably govern information they cannot locate.
Sensitive information frequently accumulates in:
- Legacy databases
- Shared folders
- User endpoints
- Cloud repositories
- Email systems
- Collaboration platforms
- Backups and archives
- Forgotten or duplicated data stores
A structured discovery program helps organizations:
- Build a defensible sensitive-data inventory
- Identify dark, stale and duplicated data
- Detect regulated and business-critical information
- Prioritize exposure according to sensitivity and context
- Prepare for privacy, security and compliance assessments
- Establish a reliable foundation for classification and DLP
- Reduce unknown data exposure
- Improve remediation planning
- Support data-security posture management initiatives
| Capability | Description |
|---|---|
| Structured and unstructured discovery | Scan supported databases, files, repositories, endpoints and cloud environments. |
| Sensitive-data detection | Identify personal, financial, payment, health, intellectual-property and custom sensitive-data patterns. |
| International and Turkish PII | Detect global and regional identifiers, including supported Turkish personal-data patterns. |
| Policy-based scanning | Configure scheduled, incremental and policy-driven discovery activities. |
| Central data inventory | Consolidate findings into a searchable view of the enterprise data estate. |
| Data mapping | Understand where sensitive data resides and how it is distributed. |
| Risk prioritization | Evaluate findings using sensitivity, location, access, exposure and policy context. |
| OCR and fingerprinting | Inspect image-based content and known sensitive patterns in supported editions. |
| Remediation workflows | Support masking, secure deletion, quarantine, encryption and governed remediation in applicable editions. |
| Reporting and audit evidence | Produce operational and compliance-oriented reports from discovery activity. |
| Security integrations | Provide discovery context to classification, DLP, SIEM and governance workflows. |
| Flexible deployment | Support SaaS, on-premises, hybrid and multi-tenant scenarios according to edition and architecture. |
Capabilities vary by edition, connector and deployment model. Refer to the official product page and documentation for current information.
flowchart LR
A[Enterprise data sources] --> B[Veriket discovery connectors and agents]
B --> C[Content inspection and detection]
C --> D[Sensitive-data inventory]
D --> E[Data map and risk context]
E --> F[Prioritized findings]
F --> G[Owner review and remediation]
E --> H[Classification and DLP]
E --> I[SIEM and reporting]
E --> J[Compliance and governance]
This diagram is conceptual and does not disclose Siberson's proprietary implementation architecture.
A Veriket Data Discovery deployment may include supported sources such as:
- Relational databases
- Business application databases
- Supported enterprise data platforms
- Custom structured repositories through approved integration methods
- File servers
- Network shares
- Endpoint file systems
- Documents and archives
- Image-based and scanned content in supported editions
- Cloud file repositories
- Collaboration and content-management environments
- Email systems
- Microsoft 365 content
- Collaboration repositories
- Supported cloud productivity environments
Connector availability and supported operations should be validated against the current product edition and official documentation.
Veriket Data Discovery can use a combination of approved detection methods, depending on edition and configuration:
- Predefined sensitive-data patterns
- Regular expressions
- Custom rules
- Dictionaries and keyword context
- Proximity and validation logic
- OCR
- Fingerprinting
- Policy-based content inspection
- Regional and industry-specific identifiers
- Classification and metadata context where supported
Detection quality depends on policy design, source quality, validation criteria and organizational context. Discovery programs should include tuning and review rather than treating every initial match as a confirmed incident.
Discovery results become more actionable when sensitivity is evaluated together with context.
A conceptual risk model may consider:
Risk Priority =
Data Sensitivity
× Exposure Context
× Access Scope
× Location Criticality
× Data Volume
× Policy Relevance
This expression is illustrative and does not represent a production scoring formula.
See Discovery Scope and Risk Model for additional public guidance.
Discovery should lead to controlled risk reduction rather than an unmanaged list of findings.
Depending on edition and deployment, remediation workflows may support actions such as:
- Masking sensitive values
- OCR-based masking for supported image content
- Secure deletion
- Quarantine
- AES-256 encryption
- Data-owner approval
- Policy-based review
- Audit evidence
Remediation should be tested, authorized and governed to avoid disrupting business processes or regulatory retention obligations.
Create a consolidated view of where regulated and business-critical information resides.
Identify personal data, validate processing locations and support data-mapping activities.
Map sensitive data, evaluate exposure and prioritize the highest-risk repositories.
Use discovery findings to understand the data estate before applying enforcement policies.
Locate forgotten, duplicated or unnecessarily retained sensitive information.
Generate reports and evidence for security, privacy and compliance assessments.
Focus security and data-owner effort on findings with the highest sensitivity and exposure context.
Use discovery outcomes to identify where classification and labeling controls should be deployed first.
Veriket Data Discovery can support data-inventory, discovery, exposure-management and governance programs associated with:
- KVKK
- GDPR
- ISO/IEC 27001
- PCI DSS
- NIS2
- Sector-specific information-security and privacy requirements
Technology alone does not establish compliance. Organizations should map product controls to their legal obligations, retention rules, risk model and operating procedures.
A discovery platform processes highly sensitive context and should itself be governed as a security-critical system.
Veriket Data Discovery supports enterprise security considerations that may include:
- Encryption at rest and in transit
- Role-based access control
- Customer-managed key scenarios in supported deployments
- Content redaction
- Tamper-evident audit records
- On-premises and SaaS deployment options
- Hybrid architecture
- Data-residency planning
- Segregation of duties
- Governed remediation approval
Detailed security architecture should be validated through official Siberson channels.
Discovery insights can provide context to:
- Data classification platforms
- Data Loss Prevention systems
- SIEM and SOC workflows
- Data governance platforms
- Privacy operations
- Retention and deletion programs
- Access-governance controls
- Incident-response workflows
- Supported enterprise applications and APIs
See Public Integration Patterns for a non-confidential overview.
Veriket Data Discovery supports enterprise scenarios that may include:
- SaaS
- On-premises
- Hybrid
- Multi-tenant deployment in supported editions
- Agent-based discovery
- Agentless discovery
- Centralized administration
- Distributed data-source scanning
- Scheduled and incremental discovery
Detailed topology, sizing, connector requirements and security configuration are provided through official Siberson documentation and technical assessment channels.
A structured Veriket Data Discovery evaluation can include:
- Business and compliance objectives
- Initial source inventory
- Priority data types
- Connector and access validation
- Detection-policy configuration
- Baseline scan
- False-positive and false-negative review
- Risk-scoring validation
- Reporting and audit requirements
- Remediation workflow validation
- Performance and source-impact assessment
- Production rollout and governance plan
To arrange a technical assessment, demonstration or proof of concept, use the Siberson contact page.
- Veriket Data Discovery Product Page
- Official Veriket Data Discovery Documentation
- Getting Started Guide
- Microsoft Marketplace Listing
- Siberson Data Security Platform
- Contact and Demo Request
Before publishing a change, review PUBLIC-CONTENT-POLICY.md.
Do not commit private repository content, proprietary code, internal detection rules, customer information, credentials, detailed deployment diagrams or non-public product information.
Do not disclose suspected vulnerabilities through public GitHub issues.
Follow SECURITY.md and use Siberson's official support or contact channels.
Siberson, Veriket, Verikor and associated product names, logos and marks are proprietary assets of Siberson.
This repository is provided for public technical information and product evaluation. It does not grant access to, or licensing rights for, Siberson's proprietary software, private source-code repositories, detection models or internal engineering assets.
Siberson develops enterprise data security technologies for discovering, classifying, protecting and monitoring sensitive information across endpoints, business applications and data infrastructure.
Discover. Classify. Prevent. Prove.