EU STANDARD ENFORCEMENT. ARTICLE BY ARTICLE.

REQUIREMENT

AI systems used in critical infrastructure, education, employment, law enforcement, and legal contexts must be classified as high-risk and subject to full conformity obligations.

RTFCT SOLUTION

Automatic classification tagging at deployment time. Every AI system is tagged with its risk classification based on intended use case and deployment context. Classification metadata is cryptographically sealed and immutable.

REQUIREMENT

High-risk AI systems must be trained on datasets with appropriate data governance, bias mitigation, and representativeness.

RTFCT SOLUTION

Training data provenance logging. Every training dataset is logged with source, bias audit results, and representativeness metrics. Data governance documentation is generated automatically and appended to the system technical file.

REQUIREMENT

High-risk AI systems must maintain comprehensive technical documentation including system architecture, performance metrics, and risk management.

RTFCT SOLUTION

The VLT automatically generates technical documentation from every inference log, system change, and performance metric. The documentation is cryptographically signed and updated in real time. Conformity assessment packages are generated with one click.

REQUIREMENT

High-risk AI systems must be designed for effective human oversight. Humans must be able to understand, monitor, and override AI outputs.

RTFCT SOLUTION

Human oversight is structurally enforced through mandatory approval gates. Every high-risk AI output is logged with the human reviewer identity, approval timestamp, and override record. The oversight chain is immutable and auditable.

REQUIREMENT

High-risk AI systems must achieve appropriate accuracy, robustness, and cybersecurity throughout their lifecycle.

RTFCT SOLUTION

Continuous performance monitoring and deviation detection. Accuracy and robustness metrics are logged for every inference. Cybersecurity events are captured in the VLT with forensic detail. Performance degradation triggers automatic alert and documentation.

REQUIREMENT

General-purpose AI models with systemic risk must maintain systemic risk documentation, security testing records, and incident reporting.

RTFCT SOLUTION

The VLT captures every general-purpose AI model invocation with systemic risk metadata, red-team testing results, and security incident logs. Systemic risk documentation is generated automatically and updated continuously.

REQUIREMENT

AI systems interacting with humans must disclose that they are AI. Users must be informed of AI-generated content.

RTFCT SOLUTION

Automated transparency disclosure logging. Every AI-generated interaction is logged with the disclosure timestamp, user acknowledgment, and content classification. The transparency record proves compliance with Article 52.

REQUIREMENT

Providers must establish post-market monitoring systems to collect and review performance data throughout the AI system lifecycle.

RTFCT SOLUTION

The VLT is the post-market monitoring system. Every inference, every user complaint, every performance deviation, and every system update is logged continuously. Post-market monitoring reports are generated automatically in regulator-ready format.

REQUIREMENT

Non-compliant AI systems are subject to market withdrawal, fines, and criminal liability. Evidence of compliance must be defensible.

RTFCT SOLUTION

The complete evidentiary chain in the VLT provides defensible proof of compliance. Every system change, every training update, every inference, and every human oversight record is cryptographically sealed. The compliance record is court- and regulator-ready.

REQUIREMENT

AI systems processing personal data must comply with data protection principles, transparency obligations, data subject rights, and DPIA requirements.

RTFCT SOLUTION

The VLT logs every personal data processing operation with purpose limitation, data minimization, and retention period metadata. Data subject access requests are fulfilled automatically from the VLT. DPIA documentation is generated from the system risk profile and inference logs.