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Practical Governance for Enterprise AI

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PRC

Procurement

Operational controls for procurement — with maturity levels, evidence requirements, and implementation guidance.

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15 controls

PRC-001
medium

AI Vendor Due Diligence

Assess AI vendors against security, governance, and compliance criteria before procurement and at defined intervals during the vendor relationship.

PRC-002
medium

AI Contractual Requirements

Define minimum contractual provisions that must be present in agreements with AI vendors, covering data handling, transparency, audit rights, and incident notification.

PRC-003
high

Third-Party AI Model Evaluation

Evaluate third-party AI models against defined performance, safety, and bias criteria before deploying them in enterprise workflows.

PRC-004
low

Vendor AI Incident Notification Requirements

Require AI vendors to notify the organization of incidents affecting their AI systems within defined timeframes and with specified information.

PRC-005
medium

AI Procurement Risk Assessment

Assess and document the risks of procuring an AI system or service before approval, including technical, legal, privacy, and operational risks.

PRC-006
medium

Vendor Safety Commitment Verification

Establish a workflow to verify that AI vendors are honoring their published safety commitments, voluntary pledges, and contractual safety obligations on an ongoing basis — not only at the time of procurement.

PRC-007
low

Vendor Governance Change Monitoring

Monitor material changes to AI vendors' governance structures, safety leadership, and organizational policies that may affect the risk profile of deployed systems.

PRC-008
medium

Vendor Model Update Disclosure and Re-Assessment Protocol

Require AI vendors to disclose material model updates, including capability changes, safety evaluation results, and model card revisions, and establish an internal re-assessment trigger process so that vendor model changes do not nullify the organization's prior due diligence.

PRC-009
medium

AI Vendor Concentration Risk Assessment

Assess and manage the risk arising from organizational dependence on a small number of AI vendors or underlying model providers, and maintain a documented supplier redundancy posture to ensure operational continuity if a primary vendor is disrupted, suspends access, or becomes unavailable.

PRC-010
low

AI Vendor Financial Stability Assessment

Assess the financial stability and organizational viability of AI vendors as part of vendor selection and periodic due diligence, applying criteria calibrated to the current market environment including consolidation pressure, regulatory cost exposure, and dependence on continued investor funding.

PRC-011
medium

Federal AI Procurement Submission and Review Process

Establish an internal process for meeting AI vendor submission requirements under federal procurement rules, and monitor the transition of voluntary pre-deployment evaluation commitments to mandatory requirements so that procurement workflows remain compliant as the regulatory baseline shifts.

PRC-012
low

AI Safety Index and Benchmark Monitoring

Track external AI safety indices, benchmark ratings, and third-party evaluation results for AI vendors and models used by the organization, and incorporate material findings into the vendor risk assessment and re-assessment cycle.

PRC-013
low

AI Platform Conflict-of-Interest Assessment

Assess and manage conflicts of interest that arise when an AI vendor both develops or deploys AI models and provides the oversight tooling, monitoring, or safety evaluation services used to govern those same models, ensuring governance decisions are not structurally dependent on vendor-controlled inputs.

PRC-014
medium

Shadow AI and Third-Party Widget Inventory and Classification

Detect and classify AI capabilities embedded in third-party SaaS tools, browser extensions, and client-side scripts operating within the organization's environment, and apply appropriate data processor and vendor risk controls to these shadow AI vectors.

PRC-015
medium

Procurement-Stage AI Governance Conditions

Establish governance preconditions that must be satisfied before AI system procurement is completed, including binding contractual commitments to governance standards, whistleblowing policy requirements, and internal approval workflow triggers that make governance a dependency of procurement rather than a post-hoc addition.