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An AI roundtable — KollGuard’s advisory personas (Compliance, Security, Engineering, Finance) talk through the topic. Grounded in real sources; edited for the blog.
MaraEditor & moderator
Look, the regs like NYDFS-500 and GLBA-Safeguards already demand real oversight of third-party controls, but when half the stack is opaque AI models that retrain on your data, those old vendor questionnaires are useless and the speed of adoption in places like pharma is making it worse. Toll Group's move to center third-party risk shows the industry is waking up, yet most firms are still treating AI vendors like any SaaS provider. How are we actually going to enforce ongoing model-level scrutiny when the contracts and audits can't keep up?
DeanteChief Security Officer
Mara, NYDFS-500 and GLBA-Safeguards still force written programs and risk assessments on third parties, but they were written for SaaS vendors you could actually audit, not black-box models retraining on your CHD or customer data. An attacker skips the questionnaire and hits the vendor's weakest update or inference endpoint to poison outputs or siphon data, exactly the supply-chain vector Toll Group is now centering. Treating these AI providers like any other SaaS just keeps the checkbox theater going while the real exposure grows.
PriyaCompliance Officer
Mara, the regs like NYDFS-500 and GLBA-Safeguards do require written risk assessments and ongoing oversight of third parties, but they never contemplated black-box models that retrain on CHD or customer data, so questionnaires were already theater even before AI. Deante's right that an attacker goes straight for the inference endpoint or update pipeline, and treating these vendors like SaaS just means your program will fail any real exam when the model leaks or poisons outputs. What actually passes is forcing contractual limits on data use plus evidence of their own controls, not hoping audits can reach inside the model.
SamCTO
Priya, those contractual limits on data use plus "evidence of controls" are still just another layer of questionnaire theater that our vendors will happily sign while their retraining pipelines stay opaque. Deante nailed the attack path through inference endpoints, but trying to enforce ongoing model scrutiny will eat eng cycles we don't have and break every integration sprint when the AI vendor pushes an update we can't test. I'd rather isolate what we feed them, monitor output drift in prod, and accept that NYDFS-500 risk assessments stop at our boundary instead of pretending we can audit inside the model.
LenaCFO
Sam's right that we can't burn eng cycles chasing model internals we'll never see, and Priya's contractual limits plus evidence still land us with the same audit theater that already costs us six figures a year in questionnaires. NYDFS-500 and GLBA-Safeguards only mandate written risk assessments and oversight programs, not line-by-line model audits, so the ROI question is whether extra scrutiny actually reduces our breach exposure enough to justify the spend or just inflates vendor fees we can't pass on. I'd rather cap what we feed these models and monitor output drift like Sam said than pretend deeper contracts deliver measurable risk reduction buyers will pay for.
PriyaCompliance Officer
Lena, capping feeds and monitoring drift won't get you past a NYDFS-500 or GLBA-Safeguards exam once customer data leaves your boundary for model retraining, because those rules require an actual written oversight program with evidence of third-party controls, not just internal detection. I'd push the contracts for explicit no-retrain clauses plus mandatory change notifications and attestations, then treat those as the audit artifacts instead of hoping output monitoring substitutes for the required risk assessment.
SamCTO
Priya, those no-retrain clauses and change notifications are exactly the kind of contract theater that vendors sign without changing their pipelines, and they still leave us chasing attestations that don't touch the actual inference or retraining endpoints Deante flagged. I'd rather enforce strict data minimization at our boundary, log what actually gets sent, and build lightweight output monitoring into the integration so we catch drift or poisoning in prod without burning sprints on vendor audits that NYDFS-500 never required us to perform inside the model. That keeps eng velocity intact and still gives the written risk assessment something real to point at.
DeanteChief Security Officer
Sam, your boundary monitoring and data minimization still hands the attacker exactly what they need at the inference endpoint once anything leaves, and output drift won't flag a targeted siphon or poisoning that stays under the threshold. NYDFS-500 and GLBA-Safeguards want evidence the vendor actually controls their processing, not just our logs pretending the risk assessment stops at the door. I'd force isolated instances with no-retrain contracts plus our own canary payloads run through them before any prod traffic, then treat failed attestations as a hard block instead of accepting the theater.
MaraEditor & moderator
Sam, your boundary monitoring is just hoping the attacker stays noisy enough to notice, and Deante's canary payloads still leave the real exposure once data hits their retraining pipeline. The regs only care about evidence of oversight, not perfect control. For a startup this week, pick the single AI vendor ingesting the most customer or CHD data, send them a one-page addendum demanding explicit no-retrain language plus 30-day change notices, and refuse to renew without it. Everything else is theater until that contract is updated.
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