Audit Trail Agent AI

Invoked when: any AI agent in the workforce produces a decision output — triggered automatically by the orchestration layer, not by individual agents

• –Captures a standardised log entry for every AI agent decision: agent identity, invocation timestamp, input data hash (not the data itself — the hash proves what data was used without storing sensitive data twice), decision output, confidence level, policy basis cited, escalation routing, and the session ID linking to the full decision context stored in the primary system of record.
• –For human overrides of AI decisions: logs the original AI verdict alongside the human decision, the authoriser identity, the authorisation level used, the stated rationale for the override, and the timestamp — creating a complete record of where human judgment departed from AI output and why. Human overrides are the most important entries in the log from a governance and accountability standpoint.
• –Applies a cryptographic hash to each log entry and chains it to the prior entry — so any modification of a prior record produces a detectable chain break. The hash chain does not prevent modification attempts; it makes modification detectable with mathematical certainty, which is sufficient for audit and legal purposes.
• –Does not store the full input data (application documents, bank statements, bureau reports) in the audit log — it stores the hash of that data alongside a pointer to its location in the primary data store. Storing the data itself would create redundant personal data storage that conflicts with DPDP data minimisation obligations.

Architecture-level control — not an invokable function but a structural property of the log store

• –The log store is configured as append-only — no update or delete operations are permitted on any logged entry, by any agent, any user, or any system process. An entry once written cannot be changed. If an entry was wrong, a correction entry is written alongside it — the correction does not replace the original.
• –The hash chain is verified on a scheduled basis — daily automated integrity check that recomputes the chain from the genesis entry to the latest entry and confirms no chain break exists. A chain break triggers an immediate alert to the human CCO and the internal audit function.
• –Access control is read-only for all standard users — the log can be queried and retrieved but not modified. Write access is restricted to the orchestration layer that captures decision events. No human user, including system administrators, has modify access to the log store.

Invoked when: RBI inspection, internal audit, borrower grievance, court production request, or regulatory investigation requires reconstruction of a specific decision or decision set

• –Accepts structured retrieval queries — by account ID, by date range, by agent type, by decision outcome, by authoriser, or by any combination — and returns the complete decision chain for the specified scope, with integrity verification confirming the returned records have not been tampered with.
• –Reconstructs the decision narrative for individual accounts: the full sequence of AI decisions and human overrides affecting that account, in chronological order, with the rationale for each decision as logged at the time — so an RBI inspector or court can see exactly what happened, in what order, and why, for any specific borrower's journey through the AI workforce.
• –Formats retrieval outputs for the specific use case: RBI inspection packs (structured data tables with all required fields), internal audit samples (decision population with statistical summary), borrower grievance responses (the specific decision and its explanation in plain language), and legal production (full chain of custody documentation for admissibility).