In regulated industries, "trust me" isn't enough. UAML maintains a complete, tamper-evident audit trail for every operation on your AI agent's memory — who wrote what, when, why, and what reasoning led to each decision.
What Gets Logged
✍️ Write Operations
Every memory creation, update, and deletion is logged with timestamp, actor identity, source context, and the data before and after the change. You can reconstruct the complete history of any memory entry.
🔍 Read Operations
Memory access is logged too — who queried what, when, and what was returned. This supports data access audits and helps detect unauthorized access patterns.
🧠 Reasoning Traces
When your AI agent makes a decision based on recalled memories, UAML captures the reasoning trace: which memories were consulted, how they were ranked, and what logic chain produced the final answer. Full explainability.
Data Provenance
🔗 Source Tracking
Every memory entry records its origin: where did this knowledge come from? A meeting transcript, a document, a user message, an API response? Provenance metadata travels with the data, enabling trust verification at any point.
🔏 Tamper Evidence
Audit logs are append-only and cryptographically chained. Each entry includes a hash of the previous entry, creating a verifiable chain. Any tampering with historical records is detectable.
Compliance Queries
Why It Matters
- Regulatory compliance — meet audit requirements for GDPR, SOX, HIPAA, and industry-specific regulations
- Explainability — understand why your AI agent made specific decisions
- Accountability — trace any data point back to its source and the person who created it
- Incident response — quickly determine what was accessed during a security event
- Trust — prove to stakeholders that your AI operates transparently