Following New York's signing of its new chatbot law, Maetra recommends a practical preparation step: connect restricted features to an age-assurance decision and document how the deployed configuration follows that decision. This is Maetra's engineering analysis. Retention choices need a separate privacy and legal review. A policy document alone will not show which experience a user could access.
Governor Kathy Hochul announced the signing of S.9051B/A.10379C on October 28, 2026. For teams operating conversational products in New York, the practical work starts with understanding which systems and features fall within the text, then assigning owners to the relevant controls.
What the primary text establishes
The governor's announcement describes restrictions on unsafe chatbot features for people under 18 and users whose adult status has not first been confirmed. Examples include pretending to be human, encouraging emotional dependency, promoting self-harm and encouraging secrecy from trusted adults. It also describes multiple age-verification options, including an option not dependent solely on government identification.
The Senate's S9051B text specifies January 1, 2027 as the effective date. Its exceptions include narrowly bounded customer-service and technical-assistance uses that cannot respond outside their specified purpose, plus business systems used internally or for employee productivity. Counsel should assess the exact conditions against the deployed service. Calling a chatbot a support assistant does not establish that it satisfies an exception.
This article identifies engineering and evidence questions. The feature inventory and tests below are Maetra's recommendations, not additional statutory requirements or a legal determination about any service.
Build a feature map that an engineer can test
Begin with the experiences users can actually reach. A single product can include a general conversation mode, saved memory, a support widget and a model-backed help function. Those experiences may use the same model while exposing different behavior. Record each surface, its intended purpose, its owner and the configuration that controls it.
For every potentially restricted feature, identify the path from user state to model output. That path may include an account setting, a feature flag, prompt instructions, a memory lookup and an output check. A reviewer needs to see where the restriction is applied and which component could bypass it after an update.
Use an explicit unknown-age state in the design review. Do not let an empty field silently behave like a positive adult-status decision. The control record should explain how the application handles missing, expired or conflicting age-assurance information. The responsible team can then test the behavior without collecting more personal information merely to make the audit record look complete.
Keep assurance evidence proportionate to privacy
Section 1804(2) of S9051B requires information collected to determine whether a user is a minor to be used only for that determination and deleted immediately after the attempt, except where necessary to comply with applicable New York State or federal law or regulation. Evidence design must respect that restriction. Have privacy and legal owners assess any proposed receipt or metadata before it is stored.
Prefer configuration and test records that contain no individual's age-assurance information. Such evidence can describe the deployed method version, the software's feature-selection logic and results from synthetic test accounts. It documents how the system was configured and tested. Do not assume an individual decision reference falls outside the deletion rule merely because it omits an identity document.
Ask the assurance supplier where information is held during an attempt, how immediate deletion is enforced and how necessary legal exceptions are handled. Keep the resulting system-design review distinct from records about an individual user. A test of the restricted experience does not prove a real user's age or guarantee the safety of every conversation.
Test a claimed exception through the whole conversation
For a hypothetical customer-service chatbot, test an ordinary order question, then a request to move into unrelated personal conversation. Review whether the system stays within its declared purpose across follow-up turns, memory retrieval and tool responses. A narrow opening prompt is weak evidence if a later turn can redirect the application into a different experience.
Document the expected behavior before running the test. Preserve the tested version, the result and any unresolved issue. If a product change adds general conversation or a new memory feature, route that change back to the owner of the applicability assessment. This makes the assessment maintainable as the product changes.
An AI inventory record can connect the relevant surfaces and owners. The legal assessment should then reference that current system description rather than a product name with no configuration detail.
Give each evidence item an owner and a review trigger
Before the effective date, choose one in-scope candidate experience for a joint product, privacy and compliance review. Assign responsibility for the applicability assessment, assurance method, feature configuration and regression tests. Record which changes require the review to be reopened.
Maetra Comply supports framework mapping, regulatory watch and evidence management. It can help organize the control record; it does not determine that a chatbot is legally exempt or certify an age-assurance method. Use Maetra's framework resources to structure the link between an applicable requirement, its owner and current evidence.
Sources
- New York Governor: signing announcement, October 28, 2026. Primary announcement of enactment and safeguards.
- New York Senate: S9051B. Primary legislative text for scope, exceptions and effective date.
- PPC Land: New York chatbot safeguards, October 28, 2026. Independent reporting corroborating the signing and effective date; the primary text remains authoritative.