TRAMS: AI SAFETY RAILS FOR AGENTS, MODELS AND DATA USE
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Data Anonymisation

Obfuscate both structured and unstructured data including images and video
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Benefits

Shrink the audit footprint: Obfuscate data to reduce the cost of compliance e.g. GDPR, HIPAA

Supports ethical data sharing and analysis: Use valuable datasets for research, innovation, and training while respecting individuals' rights

​Reduce insurance costs

Mitigate risk of identity theft or data misuse

Features

Tokenisation
Replace original data with an anonymous token
K-Anonymity
Dataset generalization to ensure individuals are indistinguishable in groups
Differential Privacy
Epsilon (ε)-noise injection to anonymize query result
Homomorphic Encryption
Fully homomorphic encryption (FHE) for computations on encrypted data
Root of Trust & Quantum Resilience
Use Hardware Security Modules to provide FIPS compliance where required. Also to generate quantum resilient encryption keys, quantum entropy and make use of NIST-approved crypto algorithms
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Unified AI Governance and Advanced Data Privacy

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  • Home
  • PLATFORM
    • AI Risk Evaluation
    • AI Privacy Auditing
    • AI Performance Evaluation
    • AI Threat Intelligence
    • AI Threat Modelling
    • Federated Learning
    • Homomorphic Encryption
    • Synthetic Data Generation
    • Data Anonymisation
    • Data Quality Assessment
    • Industry Use Cases
  • Contact
  • Demo
  • Partnership
    • Consulting Partners
  • Blogs