How do I audit what my AI is doing?
Logs, provenance, and a policy engine. bRRAIn writes every read, write, tool call, and decision to an immutable audit log keyed by user, agent, and session. The Security Engine replays any interaction for review. Compliance becomes a query.
Why most AI deployments can't be audited
Most AI deployments are unauditable by design. The model call happens over an opaque API, the prompt gets stitched together in memory, the response lands in a chat window and scrolls away. There is no persistent record of who asked what, what context was injected, which tools fired, or what the final answer was. When regulators, internal audit, or an incident response team asks "what did your AI do last Tuesday," the honest answer is "we don't know." That is untenable for any regulated industry and a ticking risk for every other. Auditability has to be architectural, not bolted on.
What full AI provenance actually logs
Complete AI auditability needs four things captured per interaction. The actor — user and agent identity, resolved through the Auth Gateway. The context — what slice of the POPE graph was assembled and shipped. The action — every MCP tool call, with arguments and result, routed through the MCP Gateway. The outcome — the model's response, stored with its prompt and metadata. bRRAIn writes all four into an immutable audit log keyed by user, agent, and session. Nothing is ephemeral. Every interaction is a row you can query.
How the Security Policy Engine replays interactions
An audit log is only useful if you can ask it questions. The Security Policy Engine indexes the log and provides a replay surface: show me every action agent X took on Tuesday, every context bundle that included document Y, every tool call with a failure code. You can replay the full sequence — context injected, prompts issued, tools called, responses returned — for any session. Compliance teams stop asking engineering for CSV exports and start running queries themselves. Incidents resolve in minutes because the trail exists in structured form, not in scrolled-past chat transcripts.
The Ontology Viewer for human-readable traces
Logs are for machines. Reviewers are humans. The Ontology Viewer renders the graph state and the decision chain behind any answer in a form a non-engineer can follow. You see the entities that were involved, the relationships traversed, the sources cited, and the actors who touched each node. That visibility turns "the AI decided X" into "here is exactly why, with every evidence link intact." For legal, risk, and regulatory conversations, a viewable trace is worth more than a technically correct but opaque log. It makes audit a dialogue, not an archaeology project.
Making compliance a query, not a fire drill
Audit becomes routine when the data is structured and the tools are in place. You can write a saved query — "every tool call in the last 30 days where the actor was an external contractor" — and get an answer in seconds. The Security Policy Engine supports scheduled audit reports so your compliance calendar runs itself. If you are evaluating whether bRRAIn can satisfy your specific regulatory regime, book a demo and bring a sample audit question. Or read the security overview for the list of controls we ship by default. Auditability is a design choice. Choose it early.
Relevant bRRAIn products and services
- Security Policy Engine — immutable audit logs, replayable sessions, queryable compliance surface.
- Auth Gateway — actor identity for every log entry, including agent attribution.
- MCP Gateway — tool-call logging with arguments and results preserved.
- Ontology Viewer — human-readable decision traces for legal and risk reviewers.
- Security overview — the full list of controls and compliance features bRRAIn ships.
- Book a demo — bring a sample audit question and see the replay surface in action.