Running an authorised AI red-team and evaluation programme in-house
Set up a governed programme that tests your own AI features against misuse before and after release, with scope, owners, evidence and fix tracking.
This is for CISOs, CTOs and AI product owners who already have AI features live or close to launch. Ad hoc testing, where someone tries a few tricky prompts before a demo, misses the failures that matter and leaves no evidence for auditors or customers. A structured programme gives you repeatable checks on every release plus deeper reviews by authorised people. This post lays out how to set one up within your own organisation, with scope, rules, owners and a clear path from finding to fix.
A company launches a customer chatbot after a week of informal testing by the product team. Within a month, support staff notice it occasionally discloses internal escalation notes and agrees to refund requests outside policy. Nobody can say whether these were tested, because there is no record of what was tried, by whom, or which version was checked.
When a large customer sends a security questionnaire asking how the AI feature is tested for misuse, the team scrambles. Findings from earlier informal sessions sit in chat threads with no owners. Fixes were made to prompts, but nobody reran the original cases, so some issues quietly returned in the next release. Testing happened, but it was not a programme.
A digital lender in Gurugram ran a borrower-facing assistant that answered loan status and repayment questions. Testing had been informal. The risk head and CTO signed a scope covering the assistant in staging only, with synthetic borrower data, and named an internal four-person review group. They defined risks such as revealing another borrower's details, promising waivers outside policy and giving unapproved financial advice. Engineering built an evaluation suite from those risks and past support escalations. The first structured exercise surfaced several policy-breaking responses, each tracked to an owner and fixed. After that, every release ran the suite automatically, regressions were caught in staging, and the lender could answer partner due-diligence questions with dated test records.
Do we need an external firm, or can we red-team internally?
Both work. Internal teams know the product and can test often; external testers bring fresh perspective and independence for major launches. Whoever tests needs written authorisation, a clear scope and a shared findings tracker.
How often should we rerun AI evaluations?
Run the automated suite on every release and whenever the model, prompts, retrieval sources or tools change. Schedule deeper human exercises at a cadence you set, and before any major feature launch.
What evidence should we keep for auditors and customers?
Keep the signed scope, test cases, dated run results, findings with severity and owner, fix records and retest outcomes. Store them where they can be exported for due diligence without rebuilding the story later.
Can we test AI products from vendors we use?
Only with the vendor's written permission and within their published or agreed rules. Without that, limit yourself to reviewing their documentation, contracts and security reports, and test your own integration layer instead.
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