AI governance needs a level of data discipline that privacy teams have already matured in. Addressing concerns like what data is being collected, why was it required, and what consents were procured for it, are essential in developing and implementing AI governance strategies. Therefore, while they cannot effectively flag issues like biased outcomes, hallucinated information, and manipulated operations, privacy teams still have an essential veto in AI governance and AI risk management.

In fact, without allowing privacy teams to own their share of AI risks, companies are likely to allow a lot of these risks to fall through the cracks between siloed operations. Moreover, a lot of regulations are encouraging overlaps in AI governance and privacy frameworks like Connecticut. So, let’s understand the AI risks that can be more efficiently handled only if privacy teams take upon their share of ownership.

Home Turf in AI Governance

Privacy teams had already been laying the floors where a lot of AI governance policies now stand. Data mapping, privacy impact assessments, and consent infrastructure were already handling risks that have now become more concerning with AI in picture. Here is some important AI governance risks that privacy teams can take upon

• Training data provenance: Before a model touches personal data, someone has to answer whether the company had the right to use that data for this purpose in the first place. Data collected for customer service under one consent basis doesn’t automatically carry a license to train a model. This is the single most litigated AI-privacy issue right now, and it’s why Connecticut’s CTDPA amendment specifically targets it: the disclosure requirement exists because regulators assume companies are repurposing data without checking whether they’re allowed to.
• Re-identification and de-anonymization risk: Models trained on “anonymized” or aggregated data can sometimes be prompted or probed to reconstruct individual records. Privacy teams are uniquely positioned to catch this risk because they already understand de-identification standards and re-identification attack vectors.
• Individual rights applied to trained models
  Access, correction, and deletion rights don’t stop at the database. If a customer requests deletion, does that obligation reach a model that was trained on their record? Can the company honor a correction request when the wrong data is now baked into model weights? Privacy teams already run rights-request infrastructure.
• Vendor and third-party data flows: Most companies don’t train models entirely in-house and send data to AI vendors, use third-party APIs, or plug in SaaS tools with embedded AI features. Every one of those is a data-sharing relationship, and vetting data-sharing relationships (contracts, sub-processor obligations, cross-border transfer mechanisms) is privacy’s existing job.
• Sensitive data exposure in outputs: Even when training data wasn’t obviously sensitive, models can infer or surface sensitive categories through pattern-matching a company never explicitly asked for.
• Cross-border and residency exposure via AI infrastructure: Cloud-based AI tools often route data through infrastructure or model providers outside the jurisdiction where it was collected, which can trigger cross-border transfer obligations that have nothing to do with the AI use case itself and everything to do with where the servers sit. This is a direct extension of transfer mechanisms privacy teams already manage for non-AI vendors.

Privacy Without the Handbrake

Practically, a collaboration between AI governance board and the privacy teams might get messy with the usual innovation vs caution tug-of-war. Therefore, to meaningfully help serve the purpose of AI governance, privacy teams will have to strategize in ways that don’t hold back the objectives of the AI push.

• Collaborating from day 1: To meaningfully, help AI projects comply with privacy regulations, the privacy teams need to get embedded right at design stage. Owning AI risk means sitting in the room when a model is being scoped and asking questions like what data it’ll train on and what decisions it’ll influence.
• AI Inventory: Privacy teams can’t own risk for systems they don’t know exist. The starting point to this is an AI inventory. This will account for every model, tool, and vendor-embedded AI feature in use across the company. It will also include shadow AI adopted by individual teams without formal procurement.
• AI-specific data-mapping: Standard data maps track where personal data lives and flows for normal operations. AI needs its own layer on top: which datasets feed which models, whether that data was collected for a compatible purpose, and where model outputs might expose or infer information the original data never explicitly contained.
• Risk-Tiering: Beyond a flat inventory, mature programs classify each AI system by risk level based on what data it touches and what decisions it influences. This will help the review intensity to scale with actual risk instead of every system getting the same treatment.
• Vendor-risk assessment: Every third-party AI tool, API, or embedded feature is a data-sharing relationship. A dedicated AI vendor register that tracks what data each vendor touches, their sub-processors, and contractual AI-use restrictions will help make the ownership actually operational.

The Natural AI Governance Partners

Privacy teams have already been doing the closest adjacent work when AI governance became urgent. That head start is bags them the rightful ownership in managing AI risks. The companies that get this right don’t have to ask privacy and AI governance to merge but to allow the required collaboration. It will definitely take an explicit decision to give privacy a seat at the table early, the tooling to back that seat up, and enough trust between functions. Get that right, and privacy doesn’t just support AI governance but becomes one of the reasons a company can move fast on AI without moving recklessly.