Interactive CIAF + LCM Demonstrations

Explore the cryptographic mechanics of the Cognitive Insight Audit Framework through hands-on interactive examples and verification workflows.

Educational Research Demonstrations

All demonstrations use simulated data to illustrate cryptographic verification mechanics. These interactive modules are designed for research and educational purposes to demonstrate the CIAF + LCM framework capabilities. No live AI models are executed, and all receipts, hashes, and proofs are generated for illustrative purposes only.

Capsule Verification Explorer
CIAF Capsule Verification Explorer

Interactive demonstration of how AI inferences are cryptographically anchored through the CIAF LCM pipeline.

Research Demonstration Disclaimer

This interactive module illustrates the CIAF + LCM verification process using simulated inference data. It demonstrates the cryptographic mechanics of hashing, Merkle batching, and capsule verification, but does not execute or evaluate a live AI model. All receipts, hashes, and proofs are generated for research and educational purposes only and should not be interpreted as production-grade audit data or real model output.

Merkle Tree Visualization
Merkle Batch & Inclusion Proof

Visual representation of how AI inference receipts are organized in a cryptographic Merkle tree structure for efficient verification.

GDPR Cryptographic Erasure Demo
GDPR Erasure Demo

Demonstrate "cryptographic erasure" - how destroying encryption keys makes historical records unverifiable while preserving audit trail integrity per GDPR Article 17.

LCM Policy Inspector
Policy Inspector

Interactive viewer for LCM policy parameters, domain types, and commitment algorithms as defined in the CIAF technical specification.

CIAF Lifecycle Flow Diagram
Lifecycle Flow Diagram

Interactive visualization of the eight-stage CIAF capsule lifecycle process (A-H) as defined in the CapsuleHeader specification.

CIAF Capsule Lifecycle Overview
A
Dataset Anchor

Dataset identification and anchoring for traceability

B
Model Anchor

Model version identification and configuration capture

C
Training Session

Training process documentation and metrics capture

D
Pre-deployment Validation

Model validation and testing before deployment

E
Deployment Anchor

Production deployment configuration and environment setup

F
Test Evaluation

Production testing and evaluation metrics collection

G
Inference Receipt

Live inference execution and receipt generation

H
Roots (Merkle/Signature)

Cryptographic commitment and batch finalization

Technical Implementation Notes
Cryptographic Features
  • • SHA-256 hash functions for integrity
  • • Digital signatures for authentication
  • • Merkle trees for batch verification
  • • Tamper-evident data structures
Performance Characteristics
  • • O(log n) verification complexity
  • • 100:1 storage compression ratio
  • • Sub-second receipt generation
  • • Distributed storage capability
Compliance Integration
  • • GDPR Article 17 erasure support
  • • NIST AI RMF framework mapping
  • • ISO 27001 audit trail compliance
  • • Regulatory reporting automation
System Architecture
  • • Event-driven processing pipeline
  • • Immutable data structures
  • • Distributed consensus mechanisms
  • • Real-time monitoring capabilities
Storage Efficiency Simulator
Storage Efficiency Simulator

Compare traditional AI logging vs. LCM receipts to demonstrate the 100:1 compression ratio and significant storage cost savings.

512 B (256B - 1KB range)
Selected: Complete audit trail with full input/output data, model parameters, decision reasoning, and compliance metadata • Average size: 25 KB

Technical Implementation Notes

Cryptographic Standards

  • • SHA-256 cryptographic hashing for integrity verification
  • • Merkle tree structures for efficient batch verification
  • • RFC-3339 timestamp formatting for temporal ordering
  • • Canonical JSON serialization for consistent hashing

Compliance Framework

  • • EU AI Act Article 12 record-keeping requirements
  • • NIST AI RMF governance and verification protocols
  • • GDPR Article 17 cryptographic erasure support
  • • ISO/IEC 42001 audit trail specifications

Performance Characteristics

  • • O(log n) verification complexity with Merkle proofs
  • • 100:1 storage compression ratio vs traditional logs
  • • Sub-second cryptographic verification times
  • • Constant 256-byte receipt size regardless of input

Research Applications

  • • AI governance and ethics compliance verification
  • • Regulatory audit trail generation and validation
  • • Privacy-preserving verification protocols
  • • Immutable AI decision record management