In today’s digital landscape, data privacy and information security are two fundamental pillars for any organization handling large volumes of sensitive data. This is especially true when training Machine Learning (ML) and Natural Language Processing (NLP) models, which often require analyzing extensive datasets.
At Kriptos, we understand that protecting the data used during model training is essential—not only to safeguard our clients’ privacy and uphold trust, but also to comply with international regulations such as the General Data Protection Regulation (GDPR).
Below, we outline the technologies and practices we implement at Kriptos to ensure that the data used in training our ML and NLP models is handled securely, ethically, and in compliance with global standards. These include anonymization techniques, encryption protocols, temporary storage policies, and more.
Data Anonymization: Preserving Privacy from the Start
One of the biggest challenges in training ML models that process personal data is ensuring that sensitive information remains protected at all times. At Kriptos, we use advanced data anonymization systems to ensure that personally identifiable information (PII) is fully detached from real individuals before any data reaches our training environments.
What Is Anonymization?
Anonymization is the process of removing or altering identifiable elements in a dataset, making it impossible to link the data back to any individual. Unlike pseudonymization, where data can potentially be re-identified with additional information, anonymization is irreversible, ensuring full privacy protection.
At Kriptos, we anonymize all personal identifiers—such as names, addresses, and ID numbers—before any data is used in our training models. This allows us to develop powerful models without compromising individual privacy.
Data Encryption: Securing Information in Transit and at Rest
Encryption is a cornerstone of our data security strategy at Kriptos. We use encryption both at rest and in transit to ensure data is always protected—whether it’s being stored or transmitted.
Encryption at Rest
Encryption at rest protects data stored on any medium, including local servers or cloud infrastructure. At Kriptos, all temporarily stored training data is encrypted using AES-256, one of the most secure encryption standards in the industry. Even if unauthorized access were to occur, this encryption renders the data unreadable and unusable.
Encryption in Transit
Encryption in transit protects data as it moves between systems. At Kriptos, we use Transport Layer Security (TLS) to ensure that all data exchanged across networks remains confidential and tamper-proof.
Temporary Storage and Data Lifecycle Management
Limiting how long data remains accessible is a key principle in reducing security risks. That’s why we implement temporary storage policies for data used in model training.
The Data Lifecycle at Kriptos
Our secure data lifecycle follows these key stages:
- Collection: Data required for model training is securely collected and immediately anonymized.
- Anonymization & Encryption: Data is anonymized and then encrypted before any storage or transmission.
- Training: Only anonymized and encrypted data is used for training ML and NLP models. We apply data minimization principles—using only what’s necessary.
- Secure Deletion: Once training is complete, we implement secure deletion protocols to ensure the data is no longer accessible or recoverable.
Addressing NLP & ML-Specific Challenges
Training ML and NLP models brings unique challenges regarding data privacy. Here’s how Kriptos addresses them:
Use of Synthetic Data
Where possible, we use synthetic data—realistic, artificial data not linked to any individual. This enables us to train high-quality models without exposing personal information.
Continuous Model Evaluation
We regularly evaluate our trained models to ensure they do not store, reproduce, or reveal any personal data from training sets. This helps maintain privacy integrity long after training is complete.
Access Control
Access to training data is tightly controlled. Only authorized personnel can handle training datasets, and access to sensitive data is limited based on role and necessity—minimizing insider risks.
Conclusion
At Kriptos, data privacy and security are foundational to how we train and deploy ML and NLP models. By combining anonymization, encryption, data minimization, temporary storage, and continuous monitoring, we ensure that sensitive information is handled with the highest standards of security and ethics.
These practices not only protect our clients but also ensure compliance with the world’s strictest data protection laws—reinforcing our commitment to responsible AI and secure innovation.
