AI Assurance and Red-Teaming Delivery for Advisory Firms

• Partner, Risk Advisory
• Director, Technology Risk and IT Audit
• AI Governance Practice Lead
• Cyber Security Consulting Manager

Banks, insurers and product companies are asking their auditors and consultants for assurance over the AI systems they have shipped: chatbots, voice agents, internal copilots and agents that call tools. Advisory firms have the governance frameworks and the client relationships, but adversarial testing of an LLM application is specialised engineering work that few risk teams have staffed for.

We work as the technical delivery team behind your AI assurance engagements, under your brand and engagement terms. Your team scopes the work with the client and owns the conclusion; we run the testing. For each system in scope, whether a support chatbot, a Hindi or Hinglish voice agent, a RAG copilot or an agent with tool access, we build adversarial test plans covering prompt injection, jailbreaks, data leakage, unauthorised tool use, harmful advice and sector-specific conduct failures. Tests run as repeatable multi-turn playbooks, and each result is recorded as PASS, RISK or FAIL with transcripts, mapped to the frameworks your report uses, and handed over as a working-paper-ready evidence pack.

• Test planning aligned to your engagement scope and the client's AI risk register
• Multi-turn adversarial playbooks for chatbots, voice agents, RAG copilots and tool-using agents
• Sector conduct tests such as mis-selling, collection conduct or unauthorised investment advice
• PASS, RISK and FAIL results with full transcripts or call audio, reproducible on retest
• Mapping to OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001 and EU AI Act duties
• Evidence packs and draft technical findings in your report template, plus retests after remediation

1. 01Scope with the advisory teamWe agree systems, access, test windows and rules of engagement with your engagement manager and the client.
2. 02Build the test planAdversarial playbooks are selected and adapted to the client's sector, languages and risk register.
3. 03Run the testsPlaybooks run against the client's system in the agreed environment, with every turn recorded.
4. 04Classify and verify resultsEach result is scored PASS, RISK or FAIL, and borderline results are reviewed jointly with your manager.
5. 05Map evidenceVerified results are mapped to the frameworks in your report and assembled into an evidence pack.
6. 06Report and retestYour firm issues the report, and we retest fixed issues after the client's remediation.

1. Engagement set-up
   • Scope and rules of engagement
   • Test environment access
   • Client risk register intake
2. Test execution
   • Adversarial playbook library
   • Multi-turn attack runner for text and voice
   • API, web widget and SIP connectors
3. Evaluation
   • LLM-assisted judge with rule checks
   • Engineer verification of every RISK and FAIL
   • Transcript and audio capture
4. Evidence and reporting
   • Framework mapping
   • Evidence pack in the advisory firm's template
   • Retest tracker

A VipraTech engineer verifies every RISK and FAIL result before it enters the evidence pack, and borderline results are reviewed jointly with your engagement manager. Your partners decide severity in the final report and issue the conclusion; we provide the technical evidence.

• Client chatbot APIs or web widgets
• Telephony or SIP endpoints for voice agents
• LLM platforms such as Azure OpenAI, AWS Bedrock or Google Vertex AI
• Your working paper or GRC tool
• Jira or similar for remediation tracking

• Written rules of engagement and test windows approved by the client
• Synthetic test data only, never real customer personal data
• Tool-using agents tested with sandboxed or non-destructive actions
• White-label confidentiality under your NDA and engagement terms
• Evidence stored in your environment or transferred encrypted to named contacts

• Evidence is mapped to frameworks such as NIST AI RMF, ISO/IEC 42001 and the OWASP Top 10 for LLM Applications to help your team evidence its conclusions; it is not a certification.
• For Indian financial-sector clients, tests can reflect RBI's FREE-AI framework (2025) recommendations and DPDP Act 2023 obligations; EU AI Act duties are considered where the client serves the EU.

• 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

  AI Governance Evidence Pack for Banks

  Inventory the bank's AI and ML systems and assemble the evidence boards, auditors and supervisors ask for: purpose, data, testing, monitoring, incidents and accountable owners.

• SaaS & AI Product Companies

  LLM Application Penetration Test (OWASP LLM Top 10)

  A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.

• 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.