2026 , Volume 31, № 5, p.145-163

The growing spotlight on blockchain for digital forensics shows a glaring need for architecture-specific evaluation frameworks that address various forensic requirements. These requirements include immutability, traceability, timestamping, and privacy-preserving data exchange. Current research on blockchain in forensics is largely generic, lacks context-specific performance appraisal, and fails to meet the needs of dynamic evidence lifecycle management, chain-of-custody (CoC), enforcement and AI- integrated forensic intelligence sets. This prevents blockchain technologies from achieving concrete applicability the real-world forensic systems where accuracy, compliance, and auditability take precedence. To fill these gaps, this work presents a comprehensive, multi-method forensic-centric blockchain evaluation and deployment framework. As its backbone, it includes the forensic-adaptive blockchain simulation engine (FABSim), which simulates realistic forensic workloads to model BFT (Hyperledger), DAG (IOTA), and PoS (Ethereum 2.0) architectures. The output is then processed using the Evidence-Centric KPI Normalization and Scoring (ECKNS) module, which translates technical metrics into informed context-aware forensic readiness scores. The winning architecture is integrated into the dynamic chain of custody validator (DCoCV), a hybrid IPFS-blockchain model using smart contracts for role-based access and tamper-proof evidence validation. This, alongside the zero-knowledge access & identity proof engine (ZKAIP) ensures that privacy and identity are protected via zk- SNARKs and decentralized identifiers (DIDs) in anonymous but verifiable stakeholder interaction environments. FedChainTrace, a blockchain anchored Federated Learning pipeline, enables evidence analysis through AI with complete model traceability as part of forensic intelligence. Collectively, this framework redefines forensic blockchain benchmarking, enforcement, and intelligence integration. It enables architecture-aware deployment, real-time CoC enforcement, identity-protected data exchange, and AI- enhanced evidence interpretation, thereby advancing trust, compliance, and scalability in forensic investigations.