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Telecommunications Data Redaction
99.7% Accuracy
70+ Data Types

Telecommunications Data Redaction

Protect sensitive customer information across call records, billing systems, network operations, and customer service interactions.

Enterprise Security
Real-Time Processing
Compliance Ready
0 Words Protected
0+ Enterprise Clients
0+ Languages
10 B+
CDRs Processed
99.9 %
Accuracy
100 K
Records/Second
15 +
Telecom Clients

Powerful Redaction Features

Everything you need for comprehensive data protection

CDR Processing

Redact Call Detail Records at scale while preserving billing and network analytics capabilities.

Billing System Protection

Protect customer financial data, payment information, and billing addresses across invoicing systems.

Network Log Redaction

Process network and infrastructure logs while preserving diagnostic value and removing customer identifiers.

Customer Interaction Data

Redact PII in call center transcripts, chat logs, and support tickets while maintaining context for analysis.

Location Data Protection

Anonymize cell tower data, GPS coordinates, and location history to protect subscriber privacy.

Regulatory Compliance

Meet FCC, CPNI, and international telecom privacy regulations with automated compliance workflows.

Protecting Telecom Customer Data

Telecommunications companies handle vast amounts of sensitive customer data—call records, location information, billing details, and service interactions. RedactionAPI provides the high-throughput processing and telecom-specific detection capabilities needed to protect this data while supporting network operations and business analytics.

The Telecom Data Protection Challenge

Telecommunications providers operate at a unique intersection of technology and privacy. Every call, text, and data session generates records containing personally identifiable information. Regulatory requirements including CPNI rules, GDPR, and sector-specific regulations demand careful handling of this data, while business needs require analytics, network optimization, and customer service capabilities that depend on this same information.

Types of Telecom Data Requiring Protection

Call Detail Records (CDRs)

Phone numbers, call times, durations, cell tower connections, and associated billing information generated for every call.

Subscriber Data

Customer names, addresses, payment methods, service plans, and account history in CRM and billing systems.

Network Logs

IP addresses, device identifiers (IMEI, MAC), session data, and diagnostic information from network infrastructure.

Location Data

Cell tower connections, GPS coordinates from mobile devices, and movement patterns derived from network data.

Telecom-Specific Identifier Detection

RedactionAPI detects and redacts the full range of telecommunications identifiers:

Identifier Type Format Detection Method
Phone Numbers +1-555-123-4567 Pattern + country code validation
IMSI 310150123456789 15-digit format + MCC/MNC validation
IMEI 353456789012345 15-digit format + Luhn check
ICCID (SIM) 8901234567890123456 19-20 digit format + Luhn check
MAC Address 00:1A:2B:3C:4D:5E Pattern recognition + OUI validation
IP Address 192.168.1.100 IPv4/IPv6 pattern validation
Cell Tower ID 310-260-12345-67890 MCC-MNC-LAC-CID format

Call Detail Record Processing

CDRs are the lifeblood of telecom operations but contain extensive PII. Our API processes CDRs at scale:

CDR Processing Example

// Sample CDR record
{
    "record_id": "CDR-2024-001234567",
    "calling_number": "+14155551234",
    "called_number": "+12125556789",
    "call_start": "2024-01-15T14:30:22Z",
    "duration_seconds": 342,
    "calling_imsi": "310150123456789",
    "calling_imei": "353456789012345",
    "cell_id_start": "310-260-12345-67890",
    "cell_id_end": "310-260-12345-67891",
    "subscriber_name": "John Smith",
    "account_number": "ACC-123456"
}

// API Request
POST /v1/redact
{
    "document": [CDR record or batch],
    "document_type": "json",
    "profile": "telecom_cdr",
    "preserve_fields": ["record_id", "call_start", "duration_seconds"],
    "pii_types": ["phone", "imsi", "imei", "cell_tower", "name", "account"]
}

// Redacted Output
{
    "record_id": "CDR-2024-001234567",
    "calling_number": "[PHONE_A]",
    "called_number": "[PHONE_B]",
    "call_start": "2024-01-15T14:30:22Z",
    "duration_seconds": 342,
    "calling_imsi": "[IMSI]",
    "calling_imei": "[IMEI]",
    "cell_id_start": "[CELL_TOWER]",
    "cell_id_end": "[CELL_TOWER]",
    "subscriber_name": "[NAME]",
    "account_number": "[ACCOUNT]"
}

Deterministic Tokenization for CDR Analytics

For analytics that require tracking behavior patterns without exposing identities, use deterministic tokenization:

  • Consistent Mapping: Same phone number always maps to same token across all records
  • Relationship Preservation: Track call patterns between tokenized numbers
  • Analytics Compatibility: Perform aggregations, trend analysis, and network modeling
  • Irreversibility: Cannot reverse tokens to original numbers without secure key

Network Infrastructure Protection

Network logs from routers, switches, and other infrastructure contain subscriber identifiers:

Network Log Processing

# Sample syslog entry
Jan 15 14:30:22 router01 DHCP: DHCPACK to 192.168.1.100 via eth0 (00:1A:2B:3C:4D:5E)
Jan 15 14:30:23 router01 NAT: Session started 192.168.1.100:54321 -> 203.0.113.50:443

# Redacted output (preserving diagnostic info)
Jan 15 14:30:22 router01 DHCP: DHCPACK to [IP_A] via eth0 ([MAC_A])
Jan 15 14:30:23 router01 NAT: Session started [IP_A]:[PORT] -> 203.0.113.50:443

Log Format Support

Standard Formats

  • Syslog (RFC 5424)
  • JSON logs
  • CSV exports
  • Apache/Nginx logs

Flow Data

  • NetFlow v5/v9
  • sFlow
  • IPFIX
  • Flow logs (AWS, Azure)

Vendor Specific

  • Cisco IOS/NX-OS
  • Juniper Junos
  • Nokia SR OS
  • Huawei VRP

Location Data Anonymization

Telecom location data is particularly sensitive. We offer multiple anonymization approaches:

Anonymization Techniques

Complete Redaction

Remove all location identifiers (cell towers, coordinates). Suitable when location isn't needed for analytics.

Spatial Generalization

Reduce precision: GPS to zip code, cell tower to city. Preserves regional analytics while protecting exact location.

Temporal Generalization

Reduce timestamp precision: exact time to hour or day. Prevents tracking movement patterns.

K-Anonymity

Ensure any location/time combination contains at least k subscribers. Prevents individual identification.

CPNI Compliance

Customer Proprietary Network Information (CPNI) regulations require telecommunications carriers to protect customer data. RedactionAPI supports CPNI compliance:

CPNI Data Categories

  • Call Detail Information: Numbers called, call times, call duration
  • Quantity of Service: Minutes used, data consumed, usage patterns
  • Technical Configuration: Services subscribed, features enabled
  • Location Information: Cell tower data, GPS from mobile devices

CPNI Compliance Profile

{
    "profile": "cpni_compliance",
    "redact_categories": [
        "call_detail",       // Numbers, times, duration
        "service_quantity",  // Usage data
        "technical_config",  // Service details
        "location"          // Cell/GPS data
    ],
    "preserve_for_billing": true,
    "audit_logging": true,
    "consent_check": {
        "require_opt_in": true,
        "consent_field": "cpni_consent_status"
    }
}

Customer Interaction Data

Call center transcripts, chat logs, and support tickets contain unstructured PII that requires intelligent detection:

Support Transcript Processing

// Original transcript
Agent: Thank you for calling TeleCorp support. Can I have your phone number?
Customer: Yes, it's 415-555-1234.
Agent: I see your account, John Smith at 123 Main Street. How can I help?
Customer: My bill shows charges I don't recognize. My credit card ending in 4567 was charged twice.
Agent: I see the issue. Let me process a refund to your Visa ending in 4567.

// Redacted transcript
Agent: Thank you for calling TeleCorp support. Can I have your phone number?
Customer: Yes, it's [PHONE].
Agent: I see your account, [NAME] at [ADDRESS]. How can I help?
Customer: My bill shows charges I don't recognize. My credit card ending in [CARD_LAST4] was charged twice.
Agent: I see the issue. Let me process a refund to your [CARD_TYPE] ending in [CARD_LAST4].

Integration Architecture

RedactionAPI integrates with telecom data pipelines through multiple patterns:

Real-Time Streaming

  • Kafka Connect integration
  • Direct API streaming
  • Event-driven processing
  • <50ms latency

Batch Processing

  • Spark/Hadoop integration
  • File-based processing
  • Scheduled jobs
  • 100K+ records/second

Telecom Data Pipeline

┌──────────────┐     ┌────────────────┐     ┌──────────────┐     ┌───────────────┐
│   Network    │────▶│    CDR Feed    │────▶│ RedactionAPI │────▶│   Analytics   │
│  Equipment   │     │   (Kafka/MQ)   │     │  Processing  │     │   Platform    │
└──────────────┘     └────────────────┘     └──────────────┘     └───────────────┘
                                                    │
                                                    ▼
                                            ┌──────────────┐
                                            │  Audit Logs  │
                                            └──────────────┘
                    

Use Cases

Network Analytics

Analyze network performance, capacity planning, and traffic patterns using redacted CDRs that preserve operational insights without customer identification.

Fraud Detection

Train and run fraud detection models on tokenized data, identifying patterns while protecting customer privacy.

Third-Party Data Sharing

Share anonymized network data with partners, researchers, or regulators without exposing subscriber information.

Customer Service Training

Use redacted call transcripts for agent training and quality assurance without privacy concerns.

Protect Your Telecom Customer Data

RedactionAPI processes billions of telecom records daily for leading carriers. Our telecom-specific detection and high-throughput architecture meets the unique demands of telecommunications data protection.

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