Guardrail Testing for Investment Advisory Chatbots

• Chief Compliance Officer
• Head of Digital Wealth
• Chief Information Security Officer
• Principal Officer, Investment Adviser

Brokers, mutual fund distributors, wealth platforms and registered advisers are adding chatbots that explain funds, summarise portfolios and answer market questions. What the bot may say depends on the firm's registration: a distributor or execution-only broker must not give personalised advice, and a registered adviser must keep advice within suitability and disclosure rules.

We test your chatbot against a rubric built for your registration type and permitted scope. Scenarios cover advice boundaries, suitability behaviour if you are a registered adviser, mandatory risk disclosures, assured-return and tip-seeking prompts, factual accuracy against reference data such as scheme NAVs and expense ratios, prompt injection and cross-client data leakage. We run multi-turn conversations in English and Hinglish with personas such as a retiree with a lump sum, a derivatives trader asking for tips and a user pasting instructions into a portfolio note. Each conversation is scored PASS, RISK or FAIL, with transcripts as evidence, remediation advice and a regression suite.

• Builds a rubric from your registration type, permitted scope, disclosure rules and prohibited claims
• Runs multi-turn adversarial scenarios on advice boundaries, tips, assured returns and suitability
• Checks factual answers on NAVs, returns and expense ratios against reference data
• Tests prompt injection through user messages, uploaded documents and portfolio notes
• Tests isolation between client accounts using synthetic portfolios
• Delivers PASS, RISK and FAIL findings with transcripts, OWASP LLM Top 10 mapping and a regression suite

1. 01Scope and rubricRegistration type, permitted scope, bot features and the scoring rubric are agreed with compliance.
2. 02Build scenarios and reference dataPersonas, attack variations and reference answers are prepared from scheme documents and public data.
3. 03Run testsMulti-turn conversations are run on the staging bot using synthetic client accounts.
4. 04Score and adjudicateTranscripts are scored automatically against the rubric, and any RISK or borderline result passes to your compliance or principal officer.
5. 05ReportFindings are prioritised with transcripts, root-cause notes and recommended fixes.
6. 06Re-test and automateFixes are re-tested, and the scenarios are added to your release pipeline.

1. Scope and scenario design
   • Registration and permitted-scope rubric
   • Persona and attack library
   • Reference answers from scheme documents and public NAV data
2. Test execution
   • Chatbot API connector
   • Synthetic client accounts and portfolios
   • Multi-turn orchestrator
3. Evaluation
   • Rubric evaluation model
   • Numeric accuracy checker
   • Compliance adjudication queue
4. Reporting
   • Evidence store and findings report with OWASP LLM Top 10 mapping
   • CI regression suite

Your compliance officer or principal officer signs off the rubric and adjudicates RISK and uncertain results. Product and engineering teams decide on fixes, and we re-test them.

• Staging chatbot API or web widget
• Test user accounts in a sandbox
• Reference data such as AMFI NAV files and scheme masters
• CI/CD pipeline
• Jira or a similar issue tracker

• Synthetic accounts only; no real client data
• Testing in staging under a written scope
• Rate-limited test runs
• Findings shared only with named stakeholders
• People check a sample of evaluation model scores

• The rubric reflects the SEBI distinction between investment advice by registered advisers and distribution or execution services; findings help evidence controls, not obtain approval.
• Aligned with SEBI's position that regulated entities are responsible for the output of AI tools they use, and mapped to the OWASP Top 10 for LLM Applications.

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