Enterprise AI Regulatory Exposure.
Catalog, classify, comply.
AI regulation is moving fast: EU AI Act, US executive orders, state laws, sectoral rules. Leah inventories every AI use case, classifies risk tier, maps obligations per jurisdiction, and surfaces gaps before they become exposure events.
Most enterprises cannot answer the simplest question: where is AI running today?
AI use cases scattered, uncatalogued
Marketing has chatbots. HR has resume screeners. Finance has forecasting models. Engineering has copilots. Nobody has a single inventory of where AI is running inside the business, what data it touches, or who owns it.
EU AI Act classification done ad hoc
Risk tier classifications are made in slide decks and email threads. There is no defensible methodology, no audit trail, and no consistency across business units. The same vendor tool gets classified three different ways.
Cross-jurisdiction obligations not mapped
EU AI Act, US executive orders, Colorado AI Act, NYC bias audits, sectoral rules in financial services and healthcare. Each use case triggers a different combination of obligations. Manual mapping does not scale.
Vendor AI use surfaced too late
Procurement signs SaaS contracts that quietly add AI features. Six months later legal discovers the vendor processes employee data through a model. Risk classification happens after exposure, not before.
No continuous gap detection
Compliance posture is assessed once per year, then drifts as new tools are deployed and new laws take effect. By the time the next audit runs, gaps have accumulated for months without any signal.
Audit prep painful and reactive
When a regulator or board asks for the AI inventory, the legal team scrambles for weeks reconstructing it from emails, spreadsheets, and Slack threads. Documentation that should exist on day one gets built under deadline pressure.
Auto-discover where AI runs in the enterprise
Leah connects to procurement systems, SaaS management platforms, code repositories, and vendor questionnaires to surface every place AI is being used. She builds and maintains a living inventory with owners, data classes, business purpose, and lifecycle stage. Shadow AI gets surfaced before it becomes an exposure event.
“We thought we had forty AI use cases. Leah surfaced over a thousand in the first scan, mostly embedded inside vendor tools we already owned. The inventory itself was the breakthrough.”
Chief Compliance Officer, Global Insurer
Five steps from shadow AI to defensible compliance posture
Leah integrates with the systems you already run. No rip and replace. Inventory and classification in weeks, not quarters.
Connect
Leah integrates with procurement, SaaS management, contract repositories, vendor security reviews, and code platforms. No rip and replace. Existing systems remain authoritative.
Discover AI Uses
Leah surfaces every AI-enabled tool, internal model, and vendor-embedded capability. Owners, data classes, and business context are tagged automatically.
Classify
Each use case is classified by EU AI Act risk tier and equivalents. Classifications are reasoned, sourced, and consistent across the enterprise.
Map Obligations
Jurisdictional and sectoral obligations are mapped per use case. Transparency notices, bias audits, registrations, and human oversight requirements all become trackable items.
Report
Continuous gap detection runs against the live inventory. Board reports, regulator submissions, and audit packs generate on demand from a defensible record.
Got Questions? Get Answers.
Traditional GRC platforms manage controls and policies abstractly. Leah operates on the actual AI use cases inside the enterprise, with risk classification reasoned against specific regulatory text and obligations decomposed per jurisdiction. GRC tools tell you what your policy says. Leah tells you which of your one thousand AI use cases are out of compliance with the EU AI Act today, and why.
Leah reads the actual EU AI Act text, including the annexes, and classifies each use case based on its purpose, data class, deployment context, and population affected. Every classification is supported by sourced reasoning citing the relevant article or annex, ready for legal review or regulator inquiry. The methodology is consistent across business units, so the same use case is never classified two different ways.
Yes. Leah maintains a structured library covering US federal executive orders, state-level AI laws (Colorado, California, New York, Illinois, and others), sectoral rules in financial services, healthcare, and employment, plus international frameworks. New laws and amendments are added as they take effect, and the use case map is re-evaluated automatically.
Leah scans procurement contracts, SaaS expense data, vendor security questionnaires, DPIAs, and product release notes to surface AI capabilities that were added to existing tools. Embedded AI in CRMs, HR platforms, productivity suites, and customer support tools is the largest source of unmapped exposure in most enterprises.
Use cases that operate across jurisdictions get the union of applicable obligations, with conflicts surfaced for legal review. A hiring tool deployed in EU, Colorado, and NYC will be mapped to EU AI Act high-risk obligations, Colorado AI Act consumer notices, and NYC Local Law 144 bias audit requirements simultaneously, with each obligation tracked independently.
Yes. Leah is deployed by major manufacturers, financial institutions, and healthcare networks with strict data security requirements. Inventory data does not train Leah's underlying models. Customer data is encrypted in transit and at rest. SOC 2 Type II, GDPR, CCPA, HIPAA-ready, and ISO 27001 aligned. Private instance deployment is available for customers with strict data isolation requirements.
