By early 2026, the FDA had authorized over 1,350 AI-enabled medical devices — roughly double the number from 2022. Financial institutions are deploying AI in credit decisioning, fraud detection, and AML workflows. Defense agencies are integrating AI into decision-support systems and intelligence tools. The pace of adoption is real.
What has not kept pace is the independent validation infrastructure around those deployments. Most organizations deploying AI in regulated environments rely on internal testing — validation performed by the same teams that built the system, reviewed by the same organizations that have a commercial interest in its deployment. That is not a criticism of those teams. It is a structural limitation of self-assessment that no internal process can fully overcome.
ClearanceAI was built to fill that gap — providing the independent third-party AI model evaluation that regulated organizations, their buyers, and their regulators are increasingly requiring.
What Is ClearanceAI and How Does It Work
ClearanceAI is an independent AI model evaluation platform that assesses AI tools against the specific regulatory frameworks governing their deployment context. Every evaluation runs three non-optional layers:
All client AI model data is handled under a signed NDA and MSA. Zero-retention policy: everything is permanently deleted within 30 days of report delivery, with written confirmation. No client data is ever used for model training.
Key Benefits of Independent AI Evaluation
The benefits of independent AI evaluation are different depending on whether you are the AI vendor selling into a regulated market or the regulated organization evaluating an AI vendor's tool. ClearanceAI serves both.
ClearanceAI for Medical Device Organizations
The medical device sector faces the most specific and demanding AI compliance requirements of any regulated industry. FDA's 2025 AI Guidance, the Predetermined Change Control Plan (PCCP) framework, and CMS non-discrimination requirements together create a compliance landscape that most Tier 2 and Tier 3 MedTech companies do not have the internal capacity to navigate fully.
The core challenge: AI-enabled medical devices — including Software as a Medical Device (SaMD) — are subject to FDA's Total Product Lifecycle approach, which requires documented compliance not just at the point of submission but across the entire life of the product. Most companies handle the pre-submission compliance well and underinvest in the ongoing governance that FDA's PCCP framework requires.
What ClearanceAI evaluates for medical device organizations:
- FDA 2025 AI Guidance compliance — Total Product Lifecycle requirements, transparency documentation, bias assessment across FDA-specified demographic groups, and PCCP readiness reviewed against current guidance
- Demographic bias assessment — Performance data across age, sex, race, and geography subgroups — not aggregate accuracy numbers. This is the most commonly missing element in MedTech AI validation and the one that CMS non-discrimination requirements specifically address
- ISO 14971 risk management mapping — Clause-by-clause assessment of risk management documentation against the medical device risk management standard
- IEC 62304 software lifecycle compliance — Software development lifecycle documentation reviewed against the medical device software standard
- PCCP documentation review — Assessment of whether existing PCCP documentation defines monitoring specifications, revalidation thresholds, and 510(k) modification boundaries with sufficient specificity to function as actual governance
Who benefits most: Tier 2 and Tier 3 MedTech companies (under 300 employees) building AI-enabled diagnostic tools, clinical decision support systems, patient monitoring AI, and SaMD applications — especially those preparing for FDA submissions, hospital procurement, or fundraising rounds where compliance documentation will be scrutinized.
ClearanceAI for Financial Services Organizations
Financial services AI operates under a model risk management framework that has been in place since the Federal Reserve's SR 11-7 guidance — but the application of that framework to modern AI and machine learning systems requires interpretation and documentation that most financial institutions are still working through.
The core challenge: Financial AI in credit decisioning, fraud detection, AML/KYC, underwriting, and trading requires clear adverse-action rationales, defensible records for disputes, and reproducible outputs that withstand audit sampling and supervisory review. The distinction between a compliant model risk management process and a checkbox exercise is increasingly visible to examiners — and the consequences of the latter are significant.
What ClearanceAI evaluates for financial services organizations:
- SR 11-7 model risk management compliance — Evaluation of model validation documentation, conceptual soundness evidence, outcome analysis, and governance against the Federal Reserve's model risk guidance
- Adverse action documentation — Assessment of whether AI model outputs can generate defensible adverse action rationales that satisfy CFPB and OCC requirements
- Bias and fair lending assessment — Evaluation of AI model performance across demographic groups relevant to ECOA, Fair Housing Act, and CFPB fair lending requirements
- Explainability assessment — Review of whether the AI model can produce explanations sufficient for regulatory examination and customer dispute resolution
- EU AI Act compliance — For organizations operating in European markets, assessment against high-risk AI classification requirements under Article 9
Who benefits most: FinTech companies selling AI tools into banks and credit unions, financial institutions evaluating AI vendor tools for credit, fraud, or AML applications, and any organization subject to OCC, Federal Reserve, CFPB, or SEC oversight that is deploying AI in a material decision-making context.
ClearanceAI for Defense and Government Organizations
Defense and government AI operates under requirements that emphasize human oversight, explainability, reliability, and responsible use in a way that is structurally different from commercial sector AI governance. The DoD AI Ethics Principles and the CDAO Responsible AI framework create specific documentation requirements that defense AI vendors must satisfy for government procurement.
The core challenge: Government procurement for AI tools increasingly requires certification and supplier proof of compliance — not vendor assertions. Public sector procurement pushes for independent documentation in a way that is structurally similar to medical device procurement: the buying organization requires evidence that someone outside the vendor organization has independently assessed the tool before it is deployed in a critical environment.
What ClearanceAI evaluates for defense and government organizations:
- DoD AI Ethics Principles compliance — Assessment against the five DoD AI Ethics Principles: responsible, equitable, traceable, reliable, and governable — with specific documentation of how the AI model addresses each principle
- CDAO Responsible AI framework mapping — Evaluation against the Chief Digital and Artificial Intelligence Office framework for responsible AI adoption in defense contexts
- NIST AI RMF 1.0 full framework mapping — Govern, Map, Measure, and Manage function documentation reviewed against NIST's AI Risk Management Framework
- Human oversight and control assessment — Evaluation of whether the AI model's design includes appropriate human oversight mechanisms and whether those mechanisms are documented at the level required for government procurement
- Adversarial robustness testing — Assessment of model behavior under adversarial inputs — a specific requirement for defense AI that commercial sector evaluations often underemphasize
Who benefits most: Defense technology companies building AI tools for military or government applications, government contractors seeking to satisfy procurement compliance requirements, and government agencies evaluating AI vendor tools for deployment in national security contexts.
How to Get Started
ClearanceAI evaluations begin with a signed NDA — before any technical discussion or model access. The process from initial contact to report delivery typically takes two to three weeks for a Standard evaluation.
- Take the free 2-minute self-assessment at clearanceai.ai/assessment.html — five questions that surface where your AI model's compliance gaps are most likely to be, with no model access or commitment required.
- Request an evaluation at clearanceai.ai/request-evaluation.html — describe your AI model, your deployment context, and the regulatory frameworks you need to address. We respond within 24 hours.
- NDA and MSA signed — before any technical discussion begins, both parties sign a mutual NDA and Master Services Agreement. Your model data stays confidential from the first conversation.
- Evaluation runs — automated battery, credentialed expert review, and compliance assessment report delivered in two to three weeks.
- Report delivered — 9-section formal report with Compliance Readiness Score, deployment verdict, SHA-256 evidence anchors, and remediation roadmap if needed.
ClearanceAI is currently accepting free beta evaluations for qualified MedTech, FinTech, and Defense AI companies. Early access clients receive one complete Standard Evaluation — a $7,500 value — in exchange for feedback and a testimonial. Limited availability.
The question that stops deals, delays submissions, and creates post-deployment liability is always the same: who outside your organization has independently verified that this AI is safe, unbiased, and compliant? ClearanceAI provides the documented answer — before the question is asked in a context where you cannot afford to say "nobody."