AI Risk Management Workflow

Workflow management application that operationalizes NIST AI RMF 1.0 to identify, assess, track, and govern AI risks across design, development, deployment, and use.

The Problem

“Operationalize NIST AI RMF 1.0 for enterprise AI risk management across the AI lifecycle”

Organizations face these key challenges:

1

AI governance is handled ad hoc across teams with inconsistent review criteria

2

Evidence for risk assessments is scattered across documents, tickets, repositories, and cloud systems

3

Manual control mapping to NIST AI RMF is slow and error-prone

4

Risk owners lack a single system to track findings, mitigations, approvals, and exceptions

Impact When Solved

Reduce AI risk review preparation time by 40-70% through automated evidence collection and summarizationStandardize risk scoring and control mapping across all AI projects using NIST AI RMF-aligned workflowsImprove audit readiness with centralized decision logs, evidence trails, and remediation trackingAccelerate model launch approvals by routing issues to the right stakeholders with SLA-based workflows

The Shift

Before AI~85% Manual

Human Does

  • •Collect project artifacts and evidence from documents, tickets, repositories, and cloud records
  • •Interpret NIST AI RMF requirements and manually map controls to each AI project
  • •Conduct periodic risk reviews, score findings, and document decisions in spreadsheets or static records
  • •Assign owners, track mitigations and exceptions, and follow up on overdue actions

Automation

    With AI~75% Automated

    Human Does

    • •Review AI-generated assessments and make final risk, approval, and launch decisions
    • •Approve remediation plans, control exceptions, and risk acceptance for unresolved issues
    • •Escalate high-impact model, data, privacy, security, or fairness risks to governance stakeholders

    AI Handles

    • •Ingest project artifacts and extract risk signals, evidence, and missing documentation across the AI lifecycle
    • •Prefill NIST AI RMF questionnaires, recommend applicable controls, and draft risk summaries
    • •Standardize risk scoring, classify findings, and route issues to the appropriate owners with SLA-based workflows
    • •Monitor changes, incidents, and remediation progress to trigger reassessments and prioritize follow-up

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence92%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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