AI Audit Trails
That Prove Themselves.
Traditional logs record that something happened. Cachee audit trails prove it. Every AI inference, every agent decision, every model interaction — captured with a cryptographic hash chain that is independently verifiable, tamper-evident, and exportable for compliance. Not logs. Proof.
Why AI needs real audit trails
Every major regulatory framework now requires demonstrable auditability of AI decisions. The question is no longer whether you need audit trails for AI. It is whether your current approach will survive examination.
EU AI Act
High-risk AI systems must maintain logs of operation sufficient to enable post-market monitoring and investigation. Article 12 requires automatic recording of events throughout the system's lifetime.
FFIEC
Federal Financial Institutions Examination Council guidance requires financial institutions to maintain complete audit trails for model risk management, including all inputs, outputs, and decision rationale.
HIPAA
AI systems processing protected health information must maintain audit controls that record and examine activity in information systems containing ePHI. 45 CFR 164.312(b).
SEC / SR 11-7
Model risk management guidance requires documentation of all model development, implementation, and use. AI models used in trading, risk, or compliance decisions require complete decision audit trails.
Why traditional logs fail for AI
Application logs were designed for debugging, not for proving what an AI system did. They fall short in every dimension that matters for AI auditability.
Context Windows Rotate
The context that shaped an AI decision is discarded when the window fills. Logs capture the output, not the full context that produced it. You cannot reconstruct why the AI decided what it did.
Prompts Are Ephemeral
System prompts, user prompts, and chain-of-thought reasoning exist only during inference. Standard application logging does not capture them. When the request completes, the reasoning disappears.
Model Weights Change
Fine-tuning, RLHF updates, and version rotations mean the model that made a decision last week may not exist anymore. Logs do not record which model version was active or what parameters were used.
Logs Can Be Modified
Application logs are plain text files or database rows. They can be edited, truncated, deleted, or overwritten. There is no cryptographic binding between entries. Tampering is undetectable.
No Replay Capability
Logs record that an inference happened. They do not store enough context to replay it. When a regulator asks you to demonstrate what the AI did, you cannot reproduce the decision from logs alone.
No Independent Verification
Verifying a log entry requires trusting the system that produced it. There is no way for a third party to independently verify that a log entry has not been altered after the fact.
From inference to verifiable proof
Every AI operation follows the same path: capture, hash, chain, store. The result is an audit trail where every entry proves its own integrity and its position in the sequence.
AI Inference
Model receives input and produces output. Cachee captures input hash, model ID, version, parameters, and output.
Cachee Store
Complete execution context stored in Cachee. Input + output + metadata bound to SHA3-256 content hash.
Hash Chain
Content hash linked to previous entry's hash, forming tamper-evident chain. Any modification breaks all subsequent hashes.
H33-74 Attestation
Optional post-quantum attestation via H33-74. Three PQ signature families in 74 bytes. Quantum-resistant proof.
Audit trails built for AI at scale
Cachee audit trails are not bolted-on logging. They are a native capability of the computation cache, designed for the unique requirements of AI systems operating at production scale.
Every Entry Proves Its History
Each audit entry includes the hash of the previous entry. This creates an append-only chain where any modification to any historical entry invalidates every subsequent entry. Tampering is mathematically detectable, not just policy-prohibited.
Query by Any Dimension
Search audit trails by operation type, time range, agent identity, model version, input pattern, or output classification. Find every inference a specific model made during a specific window. Find every decision that produced a specific output class.
Compliance-Ready JSON
Export complete audit trails as structured JSON for compliance teams, regulators, auditors, or legal discovery. Each export includes the hash chain so recipients can independently verify integrity without access to Cachee.
No Trust Required
Verification uses standard SHA3-256 hash functions. Any party with the exported trail can verify the entire chain using commodity tools. No Cachee account, no API key, no network connection. The math is the proof.
AI Audit Trails That Prove Themselves
Deploy cryptographic audit trails for your AI systems. Hash-chained, tamper-evident, independently verifiable. No infrastructure changes required.