Stop guessing whether your AI systems meet regulatory requirements—get a standardized framework that maps every AI risk to actual compliance obligations and corporate policies.
What It Does for Your Business
The AI Risk Repository (AIR) is a comprehensive, MIT-backed database that categorizes and connects AI risks to real-world regulatory frameworks, industry standards, and corporate governance policies. Instead of building compliance documentation from scratch or hiring expensive consultants, your team gets instant access to a structured taxonomy of 400+ AI risks mapped directly to federal regulations (like AI Executive Orders), state laws, industry standards (NIST AI RMF), and established corporate policies. This saves your compliance and product teams hundreds of hours decoding what "AI safety" actually means for your specific business.
Whether you're deploying AI models internally, selling AI-powered products, or integrating third-party AI tools, AIR gives you a common language to discuss risks with legal, engineering, and executive teams. You can audit your current AI systems, document risk mitigation strategies, and demonstrate compliance readiness to regulators, investors, and customers—all using the same framework instead of reinventing governance from scratch.
Key Features
- Standardized AI Risk Taxonomy — 400+ documented AI risks pre-categorized by type (performance, security, fairness, transparency) so you're not defining risks from zero
- Regulatory Mapping — Instant cross-references showing which risks connect to federal AI guidelines, state privacy laws, and industry-specific regulations like healthcare or finance rules
- Policy Templates — Pre-built corporate policy frameworks aligned with your risk profile, cutting policy development time from months to weeks
- Risk Assessment Guidance — Clear methodology for evaluating whether specific AI risks apply to your systems and how to prioritize mitigation
- Compliance Documentation Support — Structured templates for documenting risk assessments, audit trails, and mitigation strategies that regulators actually recognize
- Free Access to Core Database — Academic-grade research backing with no paywall for the foundational taxonomy
Best For
Software companies deploying AI features, SaaS platforms integrating AI, fintech and healthcare firms handling regulated data, enterprise IT teams adopting generative AI, and any small business selling AI-powered products to larger enterprises that demand compliance documentation. Law firms advising clients on AI governance also benefit heavily.
Pricing
Free access to the core AI Risk Repository database and taxonomy. Institutional and enterprise training/consulting services available through MIT partnerships.
Business ROI
For a 20-person tech company, mapping AI risks manually costs $80,000–$150,000 in consulting fees or 300+ internal hours. Using AIR cuts that to 40–60 hours and eliminates the consultant markup. A team that would spend 2 months building governance from scratch completes AI compliance audits in 1–2 weeks instead. When investors, customers, or regulators request AI risk documentation, you already have a defensible, standards-aligned framework ready—no scrambling, no rewrites, and no regulatory rejections that cost time and credibility.
