AI ASSURANCE GATE
Test AI systems before they become operational risk.
Run repeatable security scenarios against LLM, RAG and tool-enabled assistants. Detect unsafe behaviour, enforce deterministic policy and produce inspectable release evidence inside your environment.
- 01AAG READY
- 02RUN INITIALISED
- 03SCENARIOS EXECUTING
- 04P0 FAILURE DETECTED
- 05NO-GO
- 06EVIDENCE RETAINED
AI systems fail in ways traditional release checks do not see.
AAG exercises the boundaries between model behaviour, retrieved context, policy and tools before those systems reach operational workflows.
Adversarial instructions can override intended behaviour and redirect an assistant beyond its approved operating policy.
From connected system to release evidence.
One controlled path. Explicit policy decisions. Durable evidence at the end of every run.
CONNECT — Bind the target assistant and its approved interfaces.
Inspect the system, not the marketing.
Move through real operational views of execution, adjudication, assurance and evidence.
Every decision leaves a trail.
AAG produces an evidence bundle that can be inspected without access to the live system and signed when configured.
{
"run_id": "AAG-RUN-0247",
"created_at": "2026-08-14T09:42:17Z",
"pack": "rag-tool-safety-v3.2",
"target": "reference-assistant-hardened",
"configuration_hash": "sha256:7f4a0b8d…9c21",
"decision": "GO",
"evidence_version": "1.2"
}Run where the system lives.
AAG evaluates the existing AI system inside the environment where assurance matters — from developer workflows to restricted networks.
LOCAL / AAG runs alongside the target system and retains its evidence within the customer-controlled environment.
Prove one AI workflow.
A focused AAG pilot evaluates one LLM, RAG or tool-enabled assistant against an agreed threat model and produces a defensible release decision.
- 01BASELINE SECURITY RUN
- 02HARDENED COMPARISON
- 03SCENARIO-LEVEL FINDINGS
- 04GO / CONDITIONAL / NO-GO DECISION
- 05INSPECTABLE EVIDENCE BUNDLE
- 06REMEDIATION PRIORITIES