security · 1 min read
Audit Logging for LLM Apps: What Auditors Actually Want
Auditors want completeness, integrity and queryability - not just "we have logs". Here is the audit model Bhogar AI ships out of the box.
BABhogar AI TeamProduct & Engineering
Showing an auditor a heap of log files is not an audit log. They want completeness (every relevant action recorded), integrity (logs cannot be silently edited) and queryability (you can answer their question in minutes).
Why it matters
Most home-grown AI deployments fail two of those three. Production-grade audit logging needs append-only storage, schema discipline and a query layer that auditors can use directly.
How Bhogar AI approaches it
Bhogar AI emits structured audit records for every authorisation, configuration, prompt change, KB sync and sensitive action. Records are append-only, hash-chained for integrity and exposed via a dedicated audit-query API.
- Structured records for every auditable event
- Append-only storage with hash chaining
- Dedicated audit-query API and console
- Export to your SIEM (Splunk, Sentinel, Sumo)
- Retention policies per compliance regime
What you get
SOC2, HIPAA and ISO audits move from weeks-of-evidence-gathering to point-and-query.