Cipherion secures personally identifiable, financial, or regulated data that passes through LLMs or GenAI systems — without compromising model performance or usability.
Generative AI (GenAI) systems such as LLMs, image models, and chatbots pose unique risks:
LLMs can memorize and later reproduce sensitive data from training sets
Models may generate false information or accidentally expose private data
User inputs often include sensitive personal or business information
AI responses might contain sensitive information that should be protected
Many AI systems don't properly manage data usage permissions
Cipherion helps mitigate these risks by embedding privacy-preserving encryption, masking, and monitoring into your GenAI pipeline — all while keeping data within your infrastructure.
Attackers insert crafted inputs to extract private info
Cipherion sanitizes and tokenizes user prompts before passing them to GenAI
LLM fine-tuned on raw customer data may leak it in future responses
Cipherion enforces tokenized training datasets — replacing PII with pseudonyms
Users enter or receive Aadhaar, PAN, contact info in chatbot
Cipherion masks or encrypts sensitive fields before reaching the model
Data processed by GenAI systems without explicit consent
Cipherion appends consent flags and policy constraints to prompt context
LLM logs or memory store raw conversations containing personal data
Cipherion scrubs and anonymizes logs before storage, complying with DPDP/GDPR
Cipherion filters, tokenizes, or encrypts sensitive elements in the prompt — such as names, numbers, and medical or financial terms — before sending data to an LLM or image generator.
All GenAI responses are scanned for potential sensitive information before they reach the user, API, or are logged — ensuring no reverse leakage.
Add fine-grained control over what kinds of prompts or data can be processed — e.g., block confidential IDs, prevent model queries on sensitive case records, or apply purpose restrictions.
Train or retrieve on encrypted, anonymized datasets — allowing your organization to use internal data safely for LLM fine-tuning or Retrieval-Augmented Generation (RAG).
User Prompt
Contains PII/PHI
Cipherion Filter
+ Tokenizer
LLM / GenAI
API
Output Scrubber
Validation
User / Logs / App
Secure Output
Consent Manager
+ Prompt Logger
Cipherion gives you privacy controls that run inline with your GenAI stack — no need to modify your model or switch vendors.
Data masking refers to the obfuscation of real data — partially or fully — to prevent exposure during software development, support, analytics, and unauthorized access. Unlike encryption (which is reversible with keys), masked data is made permanently non-identifiable or partially viewable.
Show only last 4 digits of a PAN, Aadhaar, or phone number
Apply partial masking: ********1234 via inline masking filters
Test environments require realistic data without exposing it
Use reversible format-preserving tokens with masking rules for controlled environments
Analytics, CRM, or BI tools shouldn't see real personal data
Mask or anonymize PII fields via API responses using Cipherion middleware
Prevent sensitive data from leaking in logs or crash reports
Mask personal fields in logs before they're written or exported
Show masked values during compliance audits or exports
Generate dual-layer reports (masked + traceable with access rights) using Cipherion export modules
Apply masking rules directly at the database or API response level — e.g., emails, mobile numbers, IDs — without modifying your frontend or backend code.
Apply different masking levels based on user context — show full values to admin APIs but masked values to frontend dashboards or test users.
Ensure logs, debug outputs, and analytics queries never reveal actual PII/PHI/PAN values — masking is enforced at the SDK level.
Keep structure and format intact so masked data can still be used in reports and dashboards without risking privacy.
Client DB
Raw Data
Cipherion SDK
Mask + Tokenize
Client API
Masked Data
User, Analyst
App, Auditor
Masking Rules
+ Logging Engine
Cipherion masks data with minimal performance impact and maximal control — enabling compliance, analytics, and testing simultaneously.
