AI workflow assurance audit

Find out if your AI workflow can survive contact with reality.

ARGUS tests whether AI-assisted workflows are traceable, replayable, auditable, and survivable under degraded, adversarial, or contested conditions. The outcome is not a generic AI policy memo. It is an evidence-grade assurance package executives can act on.

Run the initial screenView sample executive report
7core deliverables
100point readiness score
30dstarter audit target window
Sample scorecard

Workflow Assurance Readiness

ARGUS
68
01
Traceability: partial logs exist but do not preserve model/tool lineage.
02
Replayability: decisions cannot yet be reconstructed deterministically.
03
Adversarial resilience: prompt injection and degraded-data scenarios are untested.
04
Human authority: review gates exist but escalation thresholds are not explicit.

What you receive

The audit converts a vague AI-risk concern into a structured evidence package: what the workflow does, where it can fail, whether it can be reconstructed, and what must change before scaling.

Workflow map

Decision path, model/tool dependencies, evidence sources, human handoffs, and control gaps.

Replayability score

Can the organization recreate a decision from preserved inputs, outputs, versions, prompts, and context?

Traceability score

Can reviewers track each decision to user, model, data, approval, and timestamp lineage?

Failure-mode matrix

Ranked failure modes across hallucination, tool misuse, outage, data drift, prompt injection, and over-trust.

Human-in-the-loop risk map

Where human authority exists, where it is performative, and where escalation rules are missing.

Red-team scenario set

Scenario cards for degraded, adversarial, contested, and audit-stress conditions.

Executive PDF

Decision-ready report with scorecard, evidence table, red-team results, risk register, and action plan.

How ARGUS scores the workflow

The assessment focuses on operational assurance, not AI hype. The key question is whether the organization can defend, replay, explain, and safely interrupt an AI-assisted decision process when conditions degrade.

Core scoring dimensions

  • Traceability: input, model, tool, approval, and timestamp lineage.
  • Replayability: preserved context and deterministic reconstruction packets.
  • Auditability: evidence quality, exception handling, and reviewer access.
  • Degraded operations: fallback modes and manual takeover paths.
  • Adversarial resilience: prompt injection, poisoned content, tool misuse, and over-trust.
  • Human-in-the-loop risk: authority, escalation, timing, and evidence.

Failure-mode matrix preview

Trace breakLogs do not connect input, model, tool, output, and human signoff.High
Replay failureDecision cannot be reconstructed after incident, dispute, or audit.High
Prompt/tool injectionExternal content alters model behavior or causes unsafe tool use.High
Degraded data modeWorkflow continues with stale, missing, or corrupted data without warning.Medium
Automation biasHuman reviewers rubber-stamp outputs without sufficient evidence.High
Model/version driftOutputs change after model, retrieval, or policy updates with no control trail.Medium

Who this is built for

ARGUS is for executives, compliance leaders, program managers, engineering teams, and operators who are already using AI in consequential workflows or are about to deploy it into decision support.

Defense and public sector

Mission assurance, ISR triage, procurement support, operational planning, grant scoring, after-action reporting, and decision-support workflows.

Regulated enterprises

Financial, healthcare, legal, insurance, infrastructure, and supply-chain workflows where audit evidence, explainability, and continuity matter.

AI builders and integrators

Teams that need assurance artifacts before selling AI-assisted workflows into serious buyers, regulated customers, or government stakeholders.

Packaging and pricing

Choose the engagement level that matches the consequence, maturity, and urgency of the AI-assisted decision process you need to defend.

Starter Audit

$2,500-$7,500

Best for one bounded workflow, pilot, or executive risk screen.

  • Intake review
  • Workflow map
  • Initial trace/replay score
  • Top 10 failure modes
  • Executive PDF
Start qualification

Full Engagement

$15,000-$50,000

Best for production or high-consequence workflows where assurance has to be defensible.

  • Stakeholder interviews
  • Evidence collection
  • Red-team scenario run
  • Human authority map
  • Remediation roadmap
Start qualification

Continuous Assurance Retainer

$3,000-$10,000/mo

Best for teams deploying or modifying AI workflows monthly.

  • Recurring score refresh
  • Scenario library updates
  • Change-control review
  • Monthly executive memo
  • Procurement/compliance support
Start qualification
Initial qualification

Run the first assurance screen.

Submit only unclassified, non-sensitive scoping information. The intake produces an initial readiness band, buyer-routing recommendation, and next-step recommendation for RFDELTA follow-up.

What happens next

  • RFDELTA reviews the submitted workflow context.
  • You receive an initial readiness band and recommended engagement path.
  • Qualified teams can move into a starter audit, full engagement, or continuous assurance retainer.
  • The paid audit produces executive-ready evidence, risk, and action-plan materials.
  • Sensitive details are handled only after proper scope and confidentiality controls are in place.
Book a scoping call