Vendor Due Diligence: Questions to Ask Before Integrating an AI Proctor

Vendor Due Diligence: Questions to Ask Before Integrating an AI Proctor

Hook: Your institution needs secure, fair, and reliable remote proctoring — but integrating an AI proctor without a rigorous vendor checklist creates exam integrity, privacy, and financial risk. Procurement teams must ask hard questions about security posture, debt and financial health, and compliance before signing an SLA.

Why this matters in 2026

Regulators, courts, and campus stakeholders became far less patient with opaque AI proctoring practices after high-profile incidents in 2024–2025. By late 2025, adoption of cloud security frameworks like FedRAMP for educational and government contracts accelerated, and courts increasingly examined algorithmic fairness in student-facing systems. Meanwhile, vendor consolidation and capital-market shifts (for example, notable companies reshaping balance sheets or acquiring FedRAMP-authorized platforms) changed vendor risk profiles overnight.

Procurement teams today must evaluate not just whether an AI proctor flags cheating, but whether the vendor can: protect data across jurisdictions, survive financial shocks, meet contractual SLAs, and demonstrate compliance with modern AI governance expectations.

How to use this article

This is a practical vendor questionnaire and red-flag list you can use during RFI/RFP, technical evaluation, and contracting. Use the sections below to build your own vendor checklist, score responses, and escalate risks that require mitigation clauses, escrow, or alternate providers.

Core evaluation categories

1. Security posture and engineering practices
2. Privacy and data residency
3. Compliance, certifications, and auditability
4. Proctoring efficacy, fairness, and explainability
5. Integration, SLAs, and operational readiness
6. Financial health and business continuity
7. Legal, IP, and indemnification

Practical questionnaire: Ask these questions (and demand evidence)

1. Security posture

• Do you hold formal third-party security certifications? (SOC 2 Type II, ISO 27001, FedRAMP authorization level). Request audit reports and the most recent SOC 2 auditor letter.
• Where are your production systems hosted? List cloud providers, regions, and whether key services run in multi-tenant or dedicated VPCs.
• What is your vulnerability management cadence? Ask for patch timelines, recent CVEs, and proof of a formal incident response plan including tabletop exercise frequency.
• Do you offer encryption-at-rest and in-transit? Require TLS 1.2+ (strongly prefer TLS 1.3) and AES-256 or equivalent for stored data. Ask for KMS architecture and whether customer keys (BYOK) are supported.
• How do you secure AI model artifacts and training data? Require separation of training pipelines from production inference and application of least-privilege controls.
• Do you perform regular third-party penetration tests? Ask for summary findings and remediation timelines; link pentest cadence to your release pipeline (see release and pipeline best-practices).
• How do you manage privileged access? Require MFA and just-in-time admin access. Ask for recent access logs as part of a pilot.

2. Privacy & data residency

• Where is candidate data stored and processed? Get explicit data center locations for images, video, and metadata. For regulated institutions, require onshore residency.
• Can you guarantee data locality and prevent cross-border transfers? Ask for contractual controls and technical mechanisms (regional-only storage and processing).
• What is your data retention and deletion policy? Specify deletion triggers: end of contract, student request, or defined retention schedule. Insist on certified deletion and audit logs.
• How do you handle sensitive biometric data? If face recognition is used, require clear legal basis, opt-in workflows, and minimization. Prefer vendors who can operate without storing raw biometric images.
• What privacy-preserving techniques are used? Ask about anonymization, pseudonymization, or differential privacy for analytics and research use.

3. Compliance & certifications

• Do you have FedRAMP authorization (and at what level)? For U.S. public institutions and federal contracts, FedRAMP is a must. Public statements about working toward FedRAMP are not enough — ask for authorization letter.
• Do you maintain SOC 2 Type II and/or ISO 27001? Request the date of the latest report and any exceptions.
• How do you comply with privacy laws (GDPR, UK UK-GDPR, CCPA/CPRA, and local laws)? Ask for lead DPO contact and a list of Data Processing Agreements (DPAs) and Standard Contractual Clauses (SCCs) in place.
• Are your models and processes auditable? Require an explainability and audit trail: logs for decisions, thresholds used, model versions, and human-overrule records.

4. Proctoring efficacy, fairness & explainability

• What are your false positive and false negative rates? Ask to see validation studies on representative populations, including demographic breakdowns — and compare them with third-party detection reviews such as deepfake/voice-moderation benchmarks.
• How do you detect bias? Request bias audits and mitigation strategies. Prefer vendors who offer configurable thresholds and human review workflows.
• Is there a human-in-the-loop? Understand the escalation path from automated flag to human reviewer to final adjudication.
• Can you operate in “shadow mode” for pilots? Require a pilot where the proctor system observes but does not affect grading, producing a side-by-side analysis.
• Do you provide candidate appeal logs and explanation artifacts? Students need actionable reasons for flags and a transparent incident record.

5. Integration & operational readiness

• What LMS and assessment platforms do you support? Ask for certified integrations and sample APIs (LTI Advantage, RESTful).
• What are the latency and bandwidth requirements? Include minimum device specs and worst-case offline behavior.
• What is your availability SLA? Request measurable SLAs (99.9%+ for availability), maintenance windows, and historical uptime reports; align these with release and deployment practices to reduce risk.
• What support coverage and escalation matrix do you provide? Define hours (24/7 vs business hours), response times (P1, P2), and on-call contacts.
• Do you provide training for faculty and technical admins? Require documentation, onboarding, and shadow-mode training sessions.

6. SLA, metrics & contract controls

• Availability and performance: Require at least 99.9% uptime for core services, 99.5% for real-time audio/video. Define remedies: service credits, termination rights.
• Accuracy & adjudication metrics: Define acceptable thresholds for false positives and the remediation process if thresholds are missed.
• Data breach commitments: Require notification within 72 hours (preferably 24) and a contractual incident response plan with breach costs defined.
• Termination & data return: Require data export in machine-readable format, secure deletion of backups within defined timelines, and escrow of critical code or models when appropriate.
• Audit rights: Insert audit and on-site inspection rights, including subprocessor lists and change-notification clauses.

7. Financial health & business continuity

Financial stability is a procurement risk as real as a security gap. In 2025–2026, several AI-platform stories showed how debt restructuring, acquisitions, or falling revenues can affect service continuity.

• Request audited financials: Ask for last three years of audited statements (or access to summary if vendor is private). Look for declining revenue, rising burn rate, or heavy short-term liabilities.
• What is your capital runway? Especially for startups, ask how many months of operating runway remain at current burn, and whether credit lines exist.
• Do you have insurance? Confirm cyber, professional liability, and errors & omissions policies with coverage limits and carriers.
• Have you recently raised or eliminated debt? Debt restructurings and acquisitions (for example, vendors acquiring FedRAMP platforms or eliminating debt) can materially change risk. Ask about pending transactions or covenant defaults.
• Do you maintain escrow for source code, models, or data? Require escrow arrangements that enable continuity if vendor insolvency occurs.
• What are your customer concentration risks? If a vendor depends on a small number of large customers for >30% revenue, that is a concentration risk.

8. Legal, IP & indemnity

• Who owns model outputs and candidate data? Clarify IP ownership and rights to use anonymized datasets for product improvement.
• Indemnification: Require vendor indemnity for data breaches and IP infringement, and clarity on who bears legal costs in candidate disputes.
• Limitations of liability: Negotiate caps that match institutional risk tolerance; flat low caps are red flags.
• Compliance with local laws: If operating internationally, require the vendor to comply with local proctoring restrictions and workplace surveillance laws.

Red flags procurement teams must escalate

• No proof of third-party security audits: Absence of SOC 2/ISO/FedRAMP or refusal to share reports is a major red flag.
• Opaque data residency commitments: Vague answers: “we may store data in multiple regions” — require concrete regions and contractual guarantees.
• High or unexplained customer concentration: If one client accounts for >40% of revenue, the vendor could deprioritize smaller customers in a downturn.
• Unwillingness to provide financial summaries or escrow: Vendor resistance to financial transparency or escrow is a sign of hidden risk.
• No human review for disputed flags: Fully automated adjudication without human-in-the-loop increases legal and reputational risk.
• Extreme SLA limits on liability: Vendors that cap liability at subscription fees only are transferring unacceptable risk to institutions.
• Vague model governance: If the vendor cannot list model versions, training data sources, or bias mitigation tests, treat as high risk.

Scoring and risk matrix: a quick procurement rubric

Use this simple 1–5 scoring per category (5 = excellent, 1 = unacceptable). Multiply by weight according to institutional priorities. Example weights:

• Security: weight 25%
• Privacy & Data Residency: 20%
• Compliance & Auditability: 15%
• Operational/SLA: 15%
• Proctoring Efficacy & Fairness: 15%
• Financial Health & Business Continuity: 10%

Score vendors and require any vendor scoring below a defined threshold (e.g., 3.0 weighted) to provide mitigation plans or be disqualified.

Sample contract clauses and minimum requirements

Include these as non-negotiable or high-priority clauses in your RFP/contract:

• FedRAMP/SOC 2 clause: Vendor must maintain applicable certifications; notify buyer within 10 business days of any changes.
• Data residency and deletion clause: All candidate data will remain in [your jurisdiction]; deletion within 30 days of contract termination; certified deletion reports provided.
• Incident notification: Notify the customer within 24 hours of any confirmed data breach that affects customer data.
• Escrow: Source code/model/data pipeline escrow triggered by bankruptcy, material breach, or acquisition that materially changes service delivery (see chain-of-custody and escrow best-practices).
• SLA & remedies: 99.9% uptime, P1 response within 1 hour, financial credits for missed SLAs, and termination rights for repeated SLA failures.
• Audit rights: Annual audit rights and immediate audit in case of suspected misuse.
• Bias & appeals process: Documented appeals workflow, human review within 72 hours, and remediation if errors are confirmed.

Operational playbook: pilot, shadow, and rollout steps

1. Pre-pilot checklist: Verify security report, DPA, and SLA draft; define data flows and mitigation for high-risk data (biometrics).
2. Shadow pilot: Run the proctor in observation-only mode for 2–4 exam cycles with representative student cohorts and device variability.
3. Quantitative evaluation: Require vendor to provide per-exam metrics: flags per 1,000 exams, false positives, and demographic breakdowns.
4. Qualitative evaluation: Faculty and student surveys on usability, fairness perception, and accessibility issues.
5. Legal review: Get counsel to vet indemnities, liability caps, and data residency language.
6. Full rollout with staged gating: Start with low-stakes exams, then mid-stakes, and finally high-stakes with mandatory human review for all flagged events.

Case study: from procurement to contingency

In mid-2025, a mid-sized university piloted an AI proctor that promised FedRAMP-equivalent controls but refused to share SOC 2 reports and had no escrow. Procurement followed a strict checklist: vendor security, retention policies, pilot shadow, and financial review. The vendor later announced a debt restructuring and a pending acquisition of a FedRAMP-enabled platform — a positive for compliance but accompanied by revenue contraction. Because the university required escrow and audited financials, it negotiated a 12-month transition service agreement and escrowed key components before expanding usage. The result: uninterrupted service and documented exit rights when the vendor shifted product focus in 2026.

Advanced strategies for 2026 and beyond

• Demand algorithmic change management: Require a vendor process to notify you of any model updates, why the change occurred, and provide rollback options for 30 days post-deployment (tie this into your deployment pipeline and rollbacks as documented in release best-practices).
• Insist on synthetic test datasets: To validate model behavior without sharing candidate data, require vendors to run tests on institution-provided synthetic datasets and produce performance metrics.
• Conditional procurement: Tie payments or renewals to key metrics: unbiased detection rates, uptime, and resolution times for appeals.
• Cross-vendor interoperability: Avoid vendor lock-in by requiring standardized exports (LTI, xAPI) and documented import formats so alternative proctors can be adopted rapidly (see frameworks for choosing and integrating micro-apps here).
• Continuous monitoring: Build an internal analytics dashboard to monitor proctor flags, demographic impacts, and SLA adherence in real time; consider resilient, edge-aware index and monitoring strategies like those described in edge-first directory playbooks.

Final checklist (printable)

1. Obtain SOC 2 Type II and FedRAMP authorization evidence (where applicable).
2. Confirm data residency and deletion commitments in writing.
3. Validate human-in-loop workflows and appeal handling.
4. Require escrow for source code/model artifacts.
5. Request audited financial statements and insurance certificates.
6. Set clear SLAs with remedies and termination rights.
7. Run shadow pilots and demand model explainability artifacts.
8. Include audit & inspection rights in contract.

“Procurement teams must treat AI proctoring contracts like a combined cybersecurity, privacy, and financial-services purchase — because they are.”

Common vendor responses and how to handle them

• “We’re working toward FedRAMP.” — Ask for a timeline, POA&M, and interim compensating controls. Don’t accept vague timelines for production contracts with public funds.
• “We can’t share our model training data.” — Require summaries, redacted training-set provenance, and an independent bias audit instead.
• “We’re a startup; we don’t do escrow.” — Negotiate limited escrow (critical components), shorter renewal terms, and additional financial reporting covenants.
• “Our false positive rate is proprietary.” — Push for transparency: you can accept aggregated metrics under NDA but require baseline performance numbers.

Key takeaways and next steps

• Don’t buy solely on features. Security, compliance, and financial resilience are equally important to product efficacy.
• Use pilot data to inform contract terms. Shadow runs and synthetic tests provide objective evidence for SLA negotiation.
• Insist on transparency. Third-party audits, escrow, and audit rights turn vendor promises into enforceable obligations.
• Score vendors objectively. A weighted rubric surfaces hidden risks and supports defensible procurement decisions.

Procurement in 2026 is about anticipating change: vendors may acquire FedRAMP-certified platforms or restructure debt overnight — like companies that have recently shifted balance sheets to improve compliance posture. Your contract, technical controls, and financial due diligence must protect your institution from surprises.

Call to action

Ready to turn this checklist into a working RFP or vendor scorecard for your institution? Download our editable vendor checklist and weighted scoring template, or schedule a consultation with our procurement experts to run a vendor risk assessment tailored to your jurisdiction and exam-stakes. Protect academic integrity — without taking on hidden security, compliance, or financial risk.

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