The stakes
AI model outputs are only as trustworthy as their training data. Approximately 80% of enterprise data is unstructured files that lack version control, change tracking, and provenance records. When compromised training data enters AI pipelines, remediation can cost hundreds of thousands of pounds with no guarantee of success. Without file-level governance, organisations cannot demonstrate to regulators that training data came from verified sources or reproduce AI-generated analyses.
The challenge
Most governance frameworks were designed for structured databases, not the unstructured files that AI systems actually consume. Organisations cannot verify that source files haven't been tampered with, leaving models vulnerable to data poisoning attacks.
Regulatory frameworks including GDPR and the EU AI Act require proof that AI training data came from trusted sources. Without comprehensive lineage tracking, organisations cannot trace file origins, demonstrate how conclusions were reached, or defend against compliance exposure.
How Confidios helps
File-level provenance and lineage
Automated metadata extraction establishes verified baselines at ingestion. Every action—access, transformation, inclusion in training datasets, consumption by RAG pipelines—is recorded as immutable CHAR events. Complete, tamper-proof lineage from source file through to model output.
Integrity assurance and poisoning defence
Cryptographic anchoring detects any unauthorised alteration against verified baselines. Access policy engine binds permissions across identity, purpose, and time. Document fraud detection identifies manipulated files before they enter training pipelines.
Regulatory-ready audit trails
Policy definitions and access histories permanently anchored to blockchain storage. Produce cryptographically verifiable records of every file's origin, every transformation, every accessor, and the governance authority for each action. Satisfies EU AI Act and GDPR requirements.
Governed data sharing for AI collaboration
Zero-copy sharing ensures data never leaves your security boundary. Share training data with model developers and external providers through policy-governed links. Access governed dynamically, every interaction logged, control persists across organisational boundaries.
Typical workflow
01
Training data ingestion
Upload unstructured files with automated metadata extraction and baseline establishment.
02
Provenance tracking
Every file action recorded as tamper-proof events anchored to immutable storage.
03
Governed sharing
Share training data with model developers through zero-copy, policy-governed links.
04
Continuous integrity monitoring
Cryptographic anchoring detects any unauthorised alterations against verified baselines.
05
Regulatory compliance
Generate verifiable audit trails demonstrating data came from trusted sources.
Results
Prove which files trained your models with complete lineage from source data through to AI outputs
Prevent data poisoning through cryptographic integrity checks and fraud detection before files enter pipelines
Satisfy EU AI Act and GDPR with verifiable records of data sources and access history
Avoid costly model retraining by identifying exactly which source files are compromised
Share training data securely with external partners without losing control or creating copies
Defend AI decisions with tamper-proof records tracing outputs back to verified source files