Audit Logging & Evidence Management
Immutable, hash-chained logs record every action and export signed evidence bundles for audits and forensics.
What it is
Audit logging inside Jeeva AI is not a loose assortment of server logs; it is a governed evidence fabric that spans every layer of the stack—from LDAP authentication to canary deployments—capturing a cryptographically verifiable chronology of who did what, where and when. The architecture treats each event as a record of legal significance: it must be complete, tamper-evident, discoverable on demand and disposable only after its statutory life.
Why it matters
Enterprises cannot prove compliance, investigate incidents or reconstruct root causes without a deterministic history. Regulators expect verifiable artefacts for GDPR access requests and for SOC 2 or ISO 27001 attestations. Security teams must see a privilege escalation in the same window that developers diagnose a regression. Finance wants immutable traces that demonstrate licence-quota enforcement and segregation of duties. A unified, standards-aligned evidence layer answers them all without spawning brittle, ad-hoc logging scripts.
How evidence is captured
Every production service publishes structured JSON events to a central message bus resident in an encrypted, private subnet. Schemas are versioned; producers cannot emit fields that break validation. The message bus replicates across three availability zones to honour the business-continuity requirement that no single failure erase security telemetry.
From the bus, events flow into three destinations: an analytics warehouse for near-real-time dashboards, a hot storage tier retained ninety days for operational forensics, and a cold archive written to object storage with immutability locks for at least twelve months. Digest hashes of each hourly partition are anchored through an external time-stamping service. Any tamper attempt would require breaking both the storage lock and the cryptographic chain, a design directly derived from the incident-response and encryption guidelines.
Approval and change control
Code changes that affect logging cannot be merged without a peer review confirming they still emit all required audit fields: actor, action, resource, timestamp, and tenant key. Our CI pipeline automatically compares changes to the OpenAPI or GraphQL schema against the audit event catalog and blocks the build if a new endpoint is missing audit coverage. After merging, deployments follow the standard release flow: feature branch, automated testing, canary release, and staged rollout. During each phase, synthetic users simulate permission-based scenarios to ensure that audit events are properly generated and delivered.
Internal responsibilities and checkpoints
Dedicated observability engineers maintain the schema registry, retention schedules and alert rules. Quarterly, an automated task assembles a manifest of all event types and forwards it to the compliance function for gap analysis; any missing control-relevant field opens a JIRA under the “Evidence-Integrity” epic and is tracked until resolved. A separate quarterly control reviews the adequacy of storage immutability: object-lock status, key-rotation logs and restore tests must pass or a remediation plan is filed in the risk management programme.
Incident response integration
When the security hotline receives an alert, responders pull the raw event slice for the affected tenant and timeframe. The workflow forbids direct edits on production evidence; analysts spin up an isolated investigation cluster, hydrate it with the archived partition and mark the fork with a case ID. If root-cause analysis shows unauthorised access, the data-protection workflow automatically queries audit logs for any records containing personal data and prepares GDPR notifications within the seventy-two-hour window required by law.
Customer access and audit readiness
Enterprise tenants can stream their own partition of the audit feed into Splunk or Azure Sentinel over an encrypted channel, satisfying “bring-your-own-SIEM” requirements. For annual assessments the governance team exports a sealed snapshot—signed by the same hashing mechanism—covering the previous twelve months. External auditors log into a read-only workspace where saved searches map directly to control objectives: unauthorised-login attempts, data-export events, role-assignment history. Because the export derives from the same primary log source that operates the platform, there is no risk of divergence between “system of record” and “evidence of record.”
Retention and disposal
After twelve months, audit partitions transition to a glacier tier that keeps encryption keys alive but reduces cost. At the end of the contractual retention period—set by customer master agreements or regulatory calendars—the disposal job writes a deletion ticket to the change-management queue. Only after an automated dependency scan vetoes conflicts (open litigation holds, unresolved incidents) does the system purge the shard, record the purge event and update the compliance dashboard. In this way, evidence stays just long enough to meet duty-of-care requirements and no longer than data-minimisation principles allow.
Result
The outcome is a single, trustworthy narrative that can collapse months of operational detail into minutes of provable facts. Security teams hunt intrusions on the same backbone that finance uses to confirm entitlement drift, while auditors receive immutable proof that every transaction in Jeeva AI unfolded under governed, reviewable processes.
