Blocklog runs alongside AI agents making critical decisions and actions, capturing the complete execution trail to establish what happened, why it happened, and how it can be proven.
From a single cryptographically verifiable source of truth, Blocklog enables:
- 🔍 Forensic Replay — Reconstruct and replay AI decisions and executions to investigate failures and incidents.
- 📊 Decision Provenance — Trace decisions back to the inputs, tools, policies, models, and workflow state that produced them.
- ⏱️ Input Freshness Tracking — Measure the freshness and validity of critical data at decision time.
- 📋 Compliance Evidence — Generate auditor-ready evidence directly from production AI executions.
- 🔐 Cryptographic Audit Trails — Make historical decisions tamper-evident and independently verifiable.
- 🛡️ Execution Governance — Control high-risk AI actions through authorization policies and human approval.
Today's AI observability tools help engineers understand what an AI system did.
Governance and compliance platforms help organizations demonstrate whether AI systems operated within policy.
Blocklog bridges these worlds.
The same forensic record engineers need to investigate an AI incident can serve as the evidence security, risk, and compliance teams need to verify how a decision was made.
Blocklog creates a single, verifiable timeline of AI decisions and actions, connecting observability, security, governance, and compliance.
| Project | Purpose |
|---|---|
| Blocklog | AI forensic replay, provenance, and compliance infrastructure |
| Sentinel | Human-in-the-loop approvals for high-risk AI actions |
| Traceflow | Cross-agent execution and causal tracing |
| BlackVault | Agent identity, authorization, and execution security |
We believe every consequential AI decision should be explainable, replayable, and auditable.
Blocklog is building the forensic and security infrastructure layer for autonomous AI systems.
Status: 🚧 Under active development
