Reconstruct every governed AI interaction.
Kynexa records structured policy, data access and activity events so security, risk, compliance and AI platform teams can see what data was accessed, which policy applied, which model and tool were used, who initiated the action, what output was generated and why the decision was allowed, denied, or redacted.
Reads at source · single-tenant · every policy decision logged.
A three-stage audit-service flow. Runtime signals such as identity, retrieval, policy, model and tool events feed into a structured AI activity record. The record captures who initiated the action, what data was accessed, which policy applied, which model ran, which tool was called, and why the decision was allowed, denied or redacted. The final decisions are searchable, exportable, investigable and reportable.
The problem you face
Application logs rarely provide a complete AI trace. Data access, retrieval, model routing, tool calls, memory operations, policy decisions and outputs are often recorded in separate systems or not recorded at all. Investigations and compliance reviews then depend on manual reconstruction.
How it works
Kynexa emits structured audit events as policy is evaluated and AI activity occurs. Enforcement does not wait for a downstream reporting process: the decision is made inline, while the event is published to the audit service for search, export, investigation and reporting.
One governed interaction, recorded end to end
- WhoAnalyst signs in via SSO (OIDC)
Identity and session resolved.
- RequestAsks for a customer risk summary
- DataRetrieval over governed sources
Finance, CRM and research — sensitivity-aware.
- PolicyPolicy evaluated by role, intent, sensitivityMasked
- ModelModel selected via the LLM gateway
- ToolRefund tool requestedDenied
Out of scope for this role.
- OutputGoverned answer generated with citations
- DecisionAllowed with redaction — reason recordedAllowed
An eight-step trace: who signed in; the request; governed data retrieval; a policy decision that masks sensitive data; model selection; a denied out-of-scope tool call; the governed output; and a final allowed-with-redaction decision with its reason.
What every interaction records
Structured events are emitted inline as enforcement happens.
| Control surface | Policy input | Enforcement point | Audit evidence |
|---|---|---|---|
| Data access | Sources · sensitivity | Retrieval | Access event |
| Policy decision | Role · intent | Policy engine | Decision log (allow/deny/redact) |
| Model & tool | Provider · arguments | LLM / MCP gateways | Model & tool trace |
| Memory & output | Purpose | Memory governance | Memory & output evidence |
What you get
Data and retrieval traces
Record sources, chunks, sensitivity, retrieval candidates and context included, denied or redacted.
Policy decision logs
Capture policy version, inputs, outcome, obligations and the reason for allow, deny, redact or approval.
Model and tool traces
Record model provider and version, usage, tool calls, arguments, results and fallback decisions.
Compliance and risk reporting
Search, export and report on structured AI activity without slowing runtime enforcement.
What changes for you
- What you can prove
- Who initiated an action, what was accessed, which policy applied and why.
- What changes operationally
- Investigations use one queryable record instead of manual reconstruction.
- Primary stakeholders
- Security, risk, compliance, AI platform.
- Evidence produced
- Structured, exportable AI activity traces.
Technical FAQ
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