AI program teams receive dozens of new use case requests per quarter — but most lack a repeatable intake prioritization workflow to evaluate risk, assign reviewers, and track approvals. FileSurf gives AI governance committees a purpose-built intake system: upload proposals, auto-score by risk tier, and route to the right reviewers without spreadsheet chaos.
Use Cases
When business units submit AI use case proposals, FileSurf extracts key fields — intended use, data types, affected populations, and regulatory scope — then scores each request against your governance framework's risk rubric. High-risk or PII-adjacent proposals are automatically escalated to senior reviewers.
Evaluate incoming AI vendor questionnaires, model cards, and security assessments in bulk. FileSurf extracts compliance-relevant data — training data lineage, bias testing results, SOC 2 status — and flags gaps against your vendor requirements checklist before procurement continues.
For AI systems touching regulated domains (lending, healthcare, hiring), FileSurf assembles pre-approval documentation packages from intake forms, impact assessments, and prior review notes. Route completed packages directly to legal or the Chief Risk Officer's queue with one click.
Powerful Features
Everything you need to streamline your workflow with AI-powered automation.
Define your governance framework's risk rubric once — data sensitivity, regulatory exposure, automation level, affected user count — and FileSurf applies it automatically to every incoming intake document. Requests surface pre-scored as Low, Medium, High, or Critical so reviewers focus on what matters.
Route intake requests to the right stakeholders based on risk score, use case category, or department. Data science requests go to the AI Ethics team; financial automation proposals go to Compliance and Legal. Workload balancing ensures no single reviewer becomes a bottleneck.
Define required fields for each intake form type — business justification, data inventory, model explainability approach, fallback procedures — and FileSurf flags incomplete submissions before they enter the review queue. Requestors receive automatic guidance on what's missing.
Track every AI use case from first submission through final approval or rejection. See status across the full pipeline, identify aging requests, generate board-level reports on AI governance posture, and export full audit trails for regulatory inquiries.
FAQ
An AI governance intake prioritization workflow is the structured process an organization uses to receive, evaluate, and triage new AI use case proposals before deployment. It typically includes: collecting a standardized intake form from the requesting team, assessing risk factors (data sensitivity, regulatory scope, automation impact), scoring and prioritizing the request relative to the existing portfolio, routing to appropriate reviewers (AI ethics board, legal, data privacy, CRO), and tracking the request through to a formal approval, conditional approval, or rejection decision. FileSurf automates the document-heavy steps — extraction, scoring, routing, and audit logging — so governance teams can handle higher volumes without expanding headcount.
FileSurf extracts structured data from your intake documents — use case description, data categories, affected populations, regulatory frameworks, and automation level — and evaluates each against a configurable risk rubric. You define the scoring dimensions and weights that match your organization's governance policy (e.g., NIST AI RMF, ISO 42001, or your internal framework). The system assigns a risk tier (Low / Medium / High / Critical) and surfaces the specific factors driving the score, so reviewers understand the reasoning rather than just seeing a number.
Yes, fully. FileSurf's custom schema validation lets you define exactly which fields are required for each intake type, what format they should be in, and which conditions trigger specific routing paths. You can create separate intake workflows for internal AI projects, third-party AI vendors, and research pilots — each with different required fields, reviewers, and SLA timers. Changes to routing rules take effect immediately without any code changes.
FileSurf connects to GRC platforms, document management systems, and workflow tools via REST API and webhooks. Approved intake records can be automatically pushed to your risk register, ServiceNow, Jira, or governance tracking tools. For AI-specific platforms, we offer scheduled exports in structured JSON or CSV formats compatible with most AI governance registries. Most integration setups complete in under a week.
Yes. FileSurf works whether you have a mature governance framework or are establishing one. We provide starter intake form templates aligned to NIST AI RMF and EU AI Act risk categories that you can adapt to your needs. Teams typically go from zero structured intake process to a running workflow within a few days. As your governance program matures, you can add more sophisticated scoring rules, additional reviewer tiers, and deeper system integrations without rebuilding from scratch.
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