AI Governance Evidence Pack for Banks

• Chief Risk Officer
• Chief Compliance Officer
• Head of Model Risk Management
• Head of Internal Audit

Most banks now run AI in more places than anyone has listed: credit scorecards, fraud models, chatbots, document extraction, vendor tools with embedded models and staff use of general-purpose assistants. Boards and auditors increasingly ask a question that is hard to answer: which AI systems do we run, who owns them, how were they tested and how do we know they still behave as intended.

We build the evidence pack with your risk, compliance and technology teams. First we discover and inventory AI systems through questionnaires, procurement and architecture records and interviews, and classify each by use, customer impact and risk tier. A document intelligence pipeline reads existing model documentation, validation reports, vendor contracts and policies, and maps what exists to a control set drawn from FREE-AI recommendations, ISO/IEC 42001, NIST AI RMF and your model risk policy. Gaps are listed per system with an owner. Higher-risk generative AI systems receive targeted tests for leakage, bias and unsafe output, and the results join the pack. The output is a register your teams maintain.

• AI system discovery and inventory across in-house, vendor and embedded models
• Risk tiering by use case, customer impact, autonomy and data sensitivity
• Automated reading of model documents, validation reports and contracts against a control set
• Control mapping to FREE-AI recommendations, ISO/IEC 42001, NIST AI RMF and the bank's own policy
• Targeted testing of higher-risk generative AI systems for leakage, bias and unsafe output
• Gap register with owners, due dates and evidence links, exportable for audit

1. 01Agree the control setWe combine your model risk policy with relevant framework recommendations into one control list approved by risk and compliance.
2. 02Discover AI systemsQuestionnaires, procurement and architecture records and interviews identify AI systems and their owners.
3. 03Collect and read evidenceExisting documents are ingested and mapped to controls, each mapping linked to its source passage.
4. 04Tier and testSystems are risk-tiered, and higher-risk generative AI systems receive targeted tests.
5. 05Review gaps with ownersModel owners and the model risk team confirm mappings and accept or dispute each gap.
6. 06Publish the packThe register, evidence links and gap plan go to the risk committee and internal audit.

1. Discovery
   • Owner questionnaires
   • Procurement and architecture record review
   • Interview notes capture
2. Evidence processing
   • Document ingestion for model documents, validations and contracts
   • Control library combining frameworks and bank policy
   • LLM control mapper with passage citations
3. Testing
   • Generative AI leakage and jailbreak tests
   • Bias and output consistency tests
   • Test evidence store
4. Review
   • Owner and model risk review workspace
   • Gap acceptance and remediation tracking
5. Register and reporting
   • AI system register
   • Committee-ready evidence pack export

Model owners, the model risk team and compliance confirm every mapping and gap; the pipeline proposes and people decide. The risk committee owns risk acceptance, and the pack states plainly where evidence is missing.

• GRC platform or risk register
• Model registry or MLOps platform
• Document management and policy repositories
• Procurement and vendor management system
• Ticketing tool for remediation tracking

• Every control mapping linked to the exact passage it relies on
• Unsupported mappings recorded as gaps, never inferred
• Confidential documents processed in the bank's environment with role-based access
• Framework references described as alignment, not certification
• Version history for the register and evidence pack

• Aligned with themes in RBI's FREE-AI framework (2025), a set of recommendations for responsible AI in financial services, and with ISO/IEC 42001 and NIST AI RMF.
• For EU-facing operations, helps prepare documentation expected under the EU AI Act, which treats credit scoring of individuals as high-risk.
• Does not certify compliance; the bank's own assessment and its auditors decide.

• Banking

  Red-Teaming for Bank Chatbots and Voice Agents

  Adversarial testing of bank chatbots and voice agents for prompt injection, data leakage, account-takeover social engineering and unsafe advice, in English, Hindi and Hinglish.

• Banking

  Regulatory Return Preparation Assistant

  Prepare RBI return workings from core banking and GL extracts, run validation and variance checks before upload, and draft explanations that reporting teams review and sign off.

• SaaS & AI Product Companies

  EU AI Act and ISO/IEC 42001 Evidence Pack

  Assemble testing records, risk assessments, oversight design and technical documentation for your AI features, mapped to EU AI Act and ISO/IEC 42001 requirements, ready for counsel and auditors.

• Fintech & Payments

  DPDP-Aligned AI Data Handling Assessment

  Map where personal data flows through your AI features, prompts, logs, vector stores and vendors, and get a gap register and evidence pack against DPDP Act duties for your DPO.