Advanced Data masking & Anonymization
A real-time, policy-driven data protection layer combining DLP, NLP/ML detection and transformation techniques (masking, tokenization, anonymization) applied both before and after AI processing.
Sensitive data detection
Before anything is masked, the system must detect sensitive data using:
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Regex rules (emails, IBANs, SSNs)
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Named Entity Recognition (NER)
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ML-based classifiers
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Context-aware detection (e.g., “my password is…”)
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This is where Nex Firewall go beyond traditional DLP - it understand context, not just patterns.
Key Features
To ensure the protection of sensitive data in a dynamic IT environment, it is necessary to deploy appropriate security technologies. These include AI-based techniques such as data discovery, dynamic masking, tokenization, and anonymization, which protect privacy while maintaining usability. The solution also includes differential privacy, synthetic data generation and real-time policy enforcement.
Dynamic data masking
Replaces sensitive values while keeping structure usable:
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John Smith → J*** S****
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john@email.com → j***@email.com
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Credit card → **** **** **** 1234
Used when:
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The AI still needs partial context
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Logs must remain readable
Tokenization
(reversible protection)
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Replace sensitive data with tokens
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Store mapping securely outside the AI system
Example:
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CustomerName: John Smith → CustomerName: TOKEN_123
Later:
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Re-identification happens only in a secure backend
Full anonymization (irreversible)
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Remove or generalize identifiers completely
Examples:
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John Smith from Prague → User from CZ
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Exact DOB → Age range
Used for:
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Analytics
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Model training
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Compliance-heavy environments (GDPR)
Redaction
(hard blocking)
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Completely remove sensitive content
Example:
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“My password is 12345” → “My password is [REDACTED]”
Used when:
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Data must never leave the boundary
Synthetic data substitution
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Replace real data with fake but realistic values
Example:
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Real name → randomly generated name
Useful for:
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Testing
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AI processing without exposing real users
Contact offices
London (UK)
Easthampstead Road
Wokingham
RG40 3AE Berkshire
+44 (7887) 505 116
Prague (CZ)
Freyova 1/12
Areal Pivovar offices
190 00 Prague
+420 (730) 561 700
Vienna (A)
Scheydgasse 41
A-1210 Vienna
+43 (660) 400 1409
Terms & Conditions
© 2026 Contigen Ltd. | All rights reserved
Market Situation
Data masking and anonymization are growing fast due to stricter privacy regulations and AI adoption. Demand is driven by cloud migration, data sharing and analytics needs. Key issues include re-identification risks, inconsistent standards, data utility loss and high implementation costs. Many organizations struggle to balance privacy with usability. Emerging trends include synthetic data and automated privacy tools, but trust, compliance complexity and evolving regulations remain major challenges.
Possible Solution
A Nex Firewall can address data masking and anonymization challenges by acting as a real-time control layer around data and AI systems provide following services:
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Automatically detect and classify sensitive data (PII, financial, health) using ML, then apply dynamic masking or tokenization before data is exposed
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Context-aware anonymization helps preserve data utility while reducing re-identification risk
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Enforce policy-based access, monitor data flows, and block unsafe queries or model outputs (e.g., prompt leakage)
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Continuous auditing, risk scoring and compliance reporting help meet regulations