AI Advisory Services
Three service lines for community banks and regulated small and mid-size businesses. Each links to detailed service lines, outcomes, and engagement models.
AI Implementation
Cijara Group helps small and mid-size businesses in highly regulated industries - community banks above all - put AI to work without losing control of risk. We start from your workflows and your regulatory obligations, not from a vendor demo, and we stay through rollout so the tools actually get used.
Explore AI Implementation →AI & Software Supply-Chain Security
Your core banking system, digital banking platform, and loan origination tools come from vendors - and all of it rests on open-source software you never see. Cijara Group maps that hidden layer, shows you which known flaws actually reach your environment, and researches the components you depend on for the flaws nobody has found yet.
Explore AI →AI Governance & Model Risk
Cijara Group builds the governance layer regulators expect around AI and models - risk frameworks, inventories, testing evidence, and board reporting. Our principal spent 15+ years inside model risk and operational risk functions at major financial institutions, including MRIA remediation under direct regulatory scrutiny. We bring that operator-grade discipline to institutions that cannot staff it full-time.
Explore AI Governance →Responsible AI
Governance is the differentiator.
Plenty of firms can stand up an AI tool. Far fewer can hand your board the paper trail that lets a regulated institution deploy it - and defend it. Cijara Group turns Responsible AI obligations into deliverables that close the gap between a technical capability and an approved deployment.
Governance-led, not model-led
We lead with risk discipline and regulatory alignment, then apply AI inside that structure. Regulated businesses need defensible process; the model is the easy part.
Artifacts an examiner can accept
Use-case inventories, risk-management plans mapped to the NIST AI RMF Govern/Map/Measure/Manage functions, model documentation, and human-oversight runbooks - written to survive supervisory review.
Built for regulated environments
Community banks and regulated SMBs face the same expectations as the largest institutions. Our frameworks are calibrated to banking regulatory standards from day one, not adapted after the fact.
Right-sized obligations
Each use case carries only the controls that actually apply, and your team can defend that calibration on review. Governance that slows delivery is governance that gets bypassed.
AI Governance FAQ
What regulators expect on AI.
AI adoption inside a regulated institution is a governance question before it is a technology question. These are the frameworks and expectations that shape a defensible program.
See our AI Governance capability →What is the NIST AI Risk Management Framework?
The NIST AI RMF is a voluntary framework for managing AI risk, organized into four functions: Govern, Map, Measure, and Manage. It is the reference most regulators and examiners recognize for AI risk programs, which makes it the natural backbone for a defensible governance structure.
What do banking examiners expect on AI?
Examiners expect AI use to fit inside your existing risk management framework: a documented inventory of use cases, risk assessments that distinguish inherent from residual risk, effective controls, and board-level oversight. 12 CFR Part 30 (OCC) and Part 364 (FDIC) safety and soundness standards apply to AI the same way they apply to any other material technology risk.
What is ISO/IEC 42001?
ISO/IEC 42001 is the international standard for AI management systems. It defines the policies, roles, and controls an organization should operate to develop and use AI responsibly. It pairs well with NIST AI RMF: the RMF structures risk thinking, 42001 structures the management system.
What counts as a high-risk AI use case?
Any use case whose output materially affects customers, financial decisions, compliance obligations, or the institution's safety and soundness. Credit, fraud, and customer-facing decisions carry the heaviest obligations: pre-deployment testing, ongoing monitoring, human oversight, and documented review. Getting that classification right is what keeps obligations right-sized.
How does a small institution govern AI without a dedicated team?
By right-sizing the program: a use-case inventory, a policy defining acceptable use, risk assessments proportionate to materiality, and a monitoring routine that actually runs. The framework does not need to match a national bank's - it needs to be documented, followed, and defensible.
How does Cijara Group support these requirements?
We lead with governance discipline built inside regulated institutions, then apply AI implementation and security work inside that structure. Deliverables are the artifacts a board or examiner can accept: use-case inventories, risk-management plans mapped to the NIST AI RMF, model documentation, and human-oversight runbooks - sized so your institution carries only the controls that actually apply.