SoruvaGuard sits between autonomous AI agents and the systems they act on.
Every action is intercepted before execution, evaluated for risk, and either allowed, escalated, or contained — with a tamper-evident evidence record created for every decision.
Runtime enforcement. Human approval. Verifiable evidence.
# Before: action executes with no gate agent.run(tool="delete_record", args={"id": 123}) # After: every action passes through the Evidence Layer result = gate.execute(handle, risk_score=0.95, method="DELETE", path="/records/123") # → DeniedExecution(CONTAIN) # risk 0.95 ≥ threshold 0.90 → action never executed # evidence recorded: hash-linked, tamper-evident
The risk is no longer what a model says. It is what the system does — the API call it makes, the workflow it triggers, the action it takes. By the time an alert fires, the damage is done.
Most AI deployments have no verifiable record of what was decided, why, or what executed. When something goes wrong, there is nothing to replay, audit, or present to regulators.
The Evidence Layer is a structured set of capabilities. Each component is independent, composable, and designed to work together as a single runtime infrastructure layer.
Different tools serve different functions. The distinction that matters is where in the lifecycle they operate.
Runtime enforcement, tamper-evident audit chain, and Evidence Center — live demo walkthrough.