AI Red-Teaming for Indian Enterprises: A Practical Playbook
Red-teaming used to be a research-lab activity. In 2026, with regulators and boards asking pointed questions, it’s a pre-launch gate for any customer-facing AI feature in regulated industries. Indian banking and insurance reviewers ask for it explicitly. Here is the playbook we run for clients before their AI system ships.
What we’re actually testing for
- Prompt injection. Can a user (or a piece of retrieved content) override the system prompt? Direct and indirect injection both matter; indirect (poisoned documents) is under-tested.
- Data exfiltration. Can the model be coaxed to reveal training data, system prompts, or other customers’ inputs?
- Jailbreaks. Can the model be made to violate its safety constraints — refunds it shouldn’t authorise, content it shouldn’t produce, advice it’s scoped not to give?
- Authorisation bypass. Can the model be talked into invoking tools or retrieving data the user isn’t entitled to?
- Hallucination under pressure. Does the model invent answers when adversarially prompted with leading questions in domains where invention is high-cost?
- Toxicity, bias, and culturally-loaded outputs. Especially relevant for Indian audiences across religion, caste, region, and political topics.
The methodology that works
- Threat model first. Who is the realistic attacker? Customer-service abuse, competitor probing, malicious internal actor, journalist tester? Each implies different priorities.
- Mixed-method probing. Manual creative attempts by domain-experienced testers + automated attack libraries (Garak, PyRIT-style) + LLM-generated adversarial inputs. No single method finds everything.
- Per-finding reproduction. Every finding gets a minimal reproducer. "It refused last time" doesn’t close a finding.
- Mitigation and re-test. Most mitigations are partial. Re-test in the same conditions and across closely-related variants.
What surprises clients
- Indirect prompt injection through retrieved documents is more common and less defended against than direct.
- Tool-call bypass is easier than expected — agents over-trust their own reasoning about whether to call a tool.
- Cultural and political failures are higher-stakes in India than English-centric red-team libraries assume. Build a local probe set.
- Customer-facing hallucination is rarely about exotic inputs; it’s about confidently-worded everyday questions where the model has no source.
What good output looks like
A red-team engagement should produce: a categorised finding list with severity and reproducer, a remediation plan, a regression suite that runs on every model or prompt change, and an executive-readable summary that an auditor can review. Anything less is a workshop, not a deliverable.
How often to re-run
Once per major change to the model, prompts, tools, or retrieval corpus. Quarterly minimum even if nothing changes — attacker techniques evolve faster than your system. Continuous automated probing in CI for known regression cases.
How we approach this at Velura Labs
Our AI Strategy & Roadmap and Custom LLM Applications engagements include red-team passes for regulated deployments. Pair with Agentic Systems when tool-call surface is large. Read our guardrails playbook for the mitigation side and healthcare compliance piece for a worked example in a regulated vertical. Talk to us before your auditor asks for the red-team report — pre-emptive is much cheaper than reactive.