Stratalize Governance

get_stratalize_overview

START HERE — Returns the complete Stratalize tool catalog: governed MCP tools across finance, healthcare, governance, real estate, crypto, and intelligence. Available via public MCP (no auth) or x402 micropayments on Base ($0.02 atomic · $0.10 benchmark · $0.50 synthesis · $1.00 premium). Org intelligence, agent governance, and role briefs require OAuth. Call this first to discover tools by role or vertical.

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get_adoption_stage

Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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get_eu_ai_act_coverage

Use when assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing. Returns a composite payload with framework, deadline, total_controls, controls[], hint, and query timestamp, optionally filtered by NIST function from compliance_controls reference data. Example: Filter by MAP to review mapped EU AI Act controls and implementation statuses in the returned controls array for governance planning. Source: EU AI Act mappings in compliance_controls reference data.

get_uk_fca_coverage

Use when assessing FCA model risk management compliance readiness or benchmarking an AI governance program against UK regulatory expectations. Returns coverage across 13 control objectives from FCA Policy Statement PS7/24. Example: PS7/24 requires documented model validation methodology, ongoing performance monitoring, and board-level model risk appetite statement — gaps in any of the three trigger supervisory concern. Source: FCA Policy Statement PS7/24.

get_occ_enforcement_actions

Use when assessing regulatory risk for a national bank or federal thrift before a merger, acquisition, partnership, correspondent banking relationship, or vendor engagement. Returns active and historical OCC enforcement actions — formal agreements, consent orders, cease-and-desist orders, and civil money penalties — the same records OCC examiners pull during supervisory reviews. Example: First National Bank of Springfield — formal agreement active since March 2022 requiring BSA/AML program overhaul, independent compliance consultant, and quarterly progress reports to OCC — agreement not yet terminated, elevates acquisition risk materially. Source: OCC Enforcement Actions — official supervisory records.

get_sba_loan_market_data

Use when assessing small business lending opportunity in a market, benchmarking a bank's SBA production against competitors, evaluating CRA lending performance by geography, or identifying industries with unmet capital needs. Returns SBA 7(a) and 504 loan approval data — counts, amounts, average sizes, top lenders, and industry concentration by state and NAICS sector. Example: Illinois manufacturing sector — 847 SBA loans approved in 2023, $425K average, top 3 lenders holding 31% market share — 69% of market accessible to community bank competition. Source: SBA Public Loan Disclosure Data.

get_cra_performance_ratings

Use when evaluating a bank's Community Reinvestment Act track record before a merger application, charter acquisition, branch expansion approval, or community lending partnership. CRA ratings — Outstanding, Satisfactory, Needs to Improve, Substantial Noncompliance — are a primary federal approval factor for bank mergers and acquisitions. A 'Needs to Improve' rating can delay or block merger approval by 12-24 months. Example: Heartland Community Bank — Outstanding CRA rating, 2023 FDIC exam, fourth consecutive Outstanding — maximum approval runway for pending acquisition of Gateway Savings Bank. Source: FFIEC CRA Ratings Database — the official federal record.

get_federal_court_cases

Use when screening a company, executive, vendor, or counterparty for federal litigation exposure before a contract award, acquisition, investment, board appointment, or enterprise partnership. Returns active and historical federal court dockets across all US district and appellate courts — case names, docket numbers, courts, filing dates, nature of suit, and active status. Example: Acme Corp — 4 active federal cases: patent infringement N.D. Cal. (filed 2023), FLSA collective action S.D.N.Y. with 847 plaintiffs (filed 2023), FTC antitrust investigation D.D.C. (filed 2024), securities class action S.D.N.Y. (filed 2024) — aggregate litigation liability exposure estimated above $200M. Source: CourtListener, 1M+ federal court documents.

get_fec_campaign_finance

Federal campaign finance activity — PAC committees, total political disbursements, receipts, and political footprint signal. Source: FEC electronic filings. Use for political risk monitoring and PAC compliance. Every response is ML-DSA-65 signed and independently verifiable. Includes cryptographic receipt at trust.stratalize.com/verify.

get_ftc_enforcement_history

Use when evaluating antitrust exposure, consumer protection liability, data privacy enforcement history, or deceptive practices risk for a company before an acquisition, strategic partnership, or enterprise vendor selection. FTC consent orders impose ongoing behavioral restrictions lasting 10-20 years and carry $50,000+ per day penalties for violations. Example: Tech Platform Corp — FTC consent order 2021, $150M civil penalty, 20-year restrictions on data monetization practices, biennial compliance reporting — restrictions survive acquisition and bind acquirer. Source: FTC Enforcement Cases and Proceedings.

get_dol_labor_violations

Use when screening an employer, vendor, or acquisition target for wage and hour compliance risk before a contract award, supply chain partnership, PE acquisition, or HR due diligence review. Returns DOL Wage and Hour Division enforcement history — FLSA overtime violations, minimum wage violations, child labor violations — with back wages assessed and employees affected. Repeat violations are a strong predictor of class action exposure. Example: Logistics Co LLC — 3 WHD investigations 2019-2023, $1.2M back wages, 891 employees affected for FLSA overtime violations — classified repeat violator, 340% higher class action probability vs first-time violators. Source: DOL WHISARD Enforcement Database.

get_colorado_ai_act_requirements

Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024.

get_nist_ai_rmf_requirements

Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

get_us_state_ai_legislation

Use when mapping AI regulatory compliance obligations across multiple states, advising on jurisdiction-specific AI deployment requirements, or briefing legal and compliance teams on the US state AI legislation landscape. As of May 2026, Colorado (June 30), Illinois, Texas, California, Virginia, and 9 additional states have enacted or advanced material AI legislation — creating a patchwork of obligations for multi-state AI deployments without a federal standard. Example: Financial institution deploying AI in 12 states faces 4 distinct compliance regimes with conflicting definitions of high-risk AI — multi-state compliance cost estimated $800K-$2M annually for mid-size institutions. Source: NCSL + Stratalize Regulatory Intelligence.

get_model_risk_management_standards

Use when preparing for a model risk management examination, building an SR 26-2 compliant model governance program, or assessing a financial institution's MRM framework against regulatory expectations. Returns Federal Reserve SR 26-2 and OCC requirements across development, independent validation, ongoing monitoring, and governance — with exam deficiency rates showing where institutions most commonly fail. For AI and ML models, SR 26-2 explicitly requires independent validation even for vendor-supplied models and black-box systems. Example: Documentation deficiencies are the most common exam finding at 67% of reviewed institutions — inadequate conceptual soundness documentation for credit scoring models triggers immediate MRA (Matter Requiring Attention). Source: Federal Reserve SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026.