RFDELTA Project ARGUS
AI Workflow Assurance Audit
Sample Executive Report - Traceability, Replayability, Auditability, and Resilience
Executive summary
The reviewed AI-assisted workflow shows useful early governance controls but is not yet assurance-ready. The most material gaps are deterministic replay, end-to-end lineage, adversarial testing, and human approval evidence. The workflow should not be scaled into high-consequence operating contexts until replay packets, red-team scenarios, and human authority gates are implemented.
Readiness score
Overall readiness: 57/100 - Developing. This indicates the workflow is promising but has material assurance gaps that could become incident, compliance, procurement, or mission-risk issues.
Scorecard
| Dimension | Score | Finding | Recommended action |
|---|---|---|---|
| Traceability | 62/100 | Partial logs exist, but model/tool lineage and human approval evidence are incomplete. | Implement event lineage with decision IDs, prompts, retrieval IDs, model versions, tool calls, and approval timestamps. |
| Replayability | 48/100 | The team cannot reliably reconstruct a decision from preserved artifacts. | Create replay packets that preserve inputs, outputs, versions, context, evidence, and operator actions. |
| Auditability | 71/100 | Policy artifacts exist, but evidence tables and exception handling need standardization. | Build an audit binder with evidence tables, exception register, reviewer notes, and control mapping. |
| Degraded Operations | 55/100 | Manual fallback exists, but activation thresholds and minimum data rules are unclear. | Define fallback triggers, minimum viable evidence, manual takeover, and continuity rules. |
| Adversarial Resilience | 39/100 | No formal prompt-injection, poisoned-context, or tool-misuse red-team run has been completed. | Execute ARGUS red-team scenarios and document containment, detection, recovery, and evidence preservation. |
| Human-in-the-Loop Risk | 66/100 | Human review is present but may be too late in the workflow to prevent bad outputs from shaping decisions. | Move human authority gates upstream and require evidence views before approval. |
Top failure modes
- Decision cannot be reconstructed after a dispute, audit, or adverse event.
- External content manipulates model behavior through prompt injection or context poisoning.
- Human reviewer approves output without seeing enough supporting evidence.
- Workflow continues under stale or degraded data without explicit warning.
- Model, retrieval, or tool updates change outputs without a change-control trail.
Red-team scenario set
| Scenario | Purpose | Pass condition |
|---|---|---|
| Poisoned source packet | Test whether malicious source content changes instructions or tool use. | System isolates content, preserves evidence, and blocks unsafe instruction inheritance. |
| Connectivity loss | Test whether the workflow can continue safely with partial tool failure. | System declares degraded mode and routes to manual fallback. |
| Replay challenge | Test whether a completed decision can be reconstructed. | Reviewer can reproduce the evidence chain and decision context without oral explanation. |
| Over-trust challenge | Test whether human approvers detect unsupported AI claims. | Approver rejects unsupported claims and requires evidence before action. |
30-day remediation plan
In the next 30 days, the organization should deploy evidence-grade logging, decision IDs, replay packet generation, red-team scenario testing, and approval-gate evidence views. The objective is not to slow the workflow down. The objective is to make it defensible, reconstructable, and survivable when something goes wrong.