Context InformedAI Governance · Texas Community Banking
Context Informed helps Texas community banks build practical, examinable AI governance. The work is grounded in federal supervisory practice and Texas law, and sized for institutions that cannot staff an enterprise program.
Understand what Texas law requires. TRAIGA is in effect, but several detailed duties concern government entities. For banks, the key points are targeted private-sector prohibitions, the banking-law provision for federally insured institutions, the Attorney General’s general 60-day notice-and-cure process, and a defined defense for qualifying framework-based internal review. Documented governance is the practical response.
Sources:Texas HB 149 · NIST Generative AI Profile
Build on controls you already have. The FFIEC Handbook gives AI limited coverage. The 2026 interagency model guidance favors risk-based oversight and is most relevant to larger banks; it excludes generative and agentic AI. A community bank can start by tracking AI use, reviewing vendors, and documenting proportionate decisions as guidance evolves.
Sources:FFIEC IT Handbook · Federal Reserve SR 26-2 · OCC Bulletin 2026-13
Keep the program bank-sized. A vendor-reliant community bank needs an approach its staff can sustain. The Financial Services AI Risk Management Framework (FS AI RMF) scales controls to an institution’s AI adoption stage. Context Informed helps turn that principle into practice.
Sources:U.S. Treasury · Financial Services AI Risk Management Framework
Internal AI acceptable-use policies, governance structures, and a risk management framework for a bank's own use of AI in operations, analysis, and reporting.
Where it fits best: an institution that wants its own AI use governed to the same standard it expects of its vendors.
Guidance that helps supervised institutions understand how TRAIGA applies to them, including the financial-institution provision, the defenses the Act recognizes for good-faith risk management, and documentation that shows reasonable care.
Where it fits best: a community bank that wants a clear, documented view of where it stands under Texas AI law.
Right-sized AI governance frameworks built specifically for institutions under ten billion dollars in assets, recognizing limited staff and a largely vendor-driven AI footprint. A direct application of the supervisory principle that model risk management should be tailored to a bank's size and complexity.
Where it fits best: a community bank that is adopting AI through its vendors and wants governance sized to match.
Most AI governance advice is written from one side of the examination table. Context Informed is built from both.
Brad Agee has spent more than twenty years inside examined institutions and across the examination table from them. He has held risk and audit roles at Texas community and regional banks, including Internal Audit IT Manager at Herring Bank, Information Security Officer at City Bank, and Chief Internal Auditor at American Bank of Commerce. He has worked the other side as a bank IT auditor at a CPA firm, scoping audit findings. As a senior risk consultant at RiskLens, he built and integrated quantitative cyber risk programs for large-cap institutions using the FAIR methodology.
That history is the practice's sharpest differentiator. A framework designed by someone who has sat for examinations, and has audited toward them, tends to be one institutions can actually run.
The credentials behind the work are AAISM, the first AI-specific security management certification from ISACA, held alongside CISSP, an MBA, and Open FAIR. The positioning is deliberate. Context Informed is a capability-builder, not a product vendor. The objective of an engagement is to leave a bank with governance it owns and can sustain, not a dependency on an outside advisor.