Framework · Cybersecurity
NYDFS 23 NYCRR 500 for AI.
New York's cybersecurity regulation sets concrete expectations — audit trails, access controls, encryption, and monitoring of nonpublic information. As AI systems touch that data, those expectations follow. Meilynx maps directly to them.
Protect nonpublic information — provably.
Part 500 is prescriptive where it matters: keep audit trails, limit access, encrypt sensitive data, and monitor authorized users. Extending those controls to AI systems is the work.
- Audit trails to detect and respond to cybersecurity events (500.06).
- Access privileges limited to nonpublic information (500.07).
- Encryption and protection of nonpublic information (500.15).
- Monitoring of authorized-user activity (500.14).
Each section, to a control.
A specific Meilynx control for each load-bearing section of Part 500 — and the artifact it produces.
23 NYCRR 500 → Meilynx controls
| Requirement | How Meilynx maps | Examination artifact |
|---|---|---|
Maintain audit trails to detect and respond to cybersecurity events 23 NYCRR 500.06 | A tamper-evident, hash-chained record of every AI request and governance decision — allowed or blocked — gives you an audit trail designed to survive scrutiny. | Examination-ready audit trail |
Limit access privileges to nonpublic information 23 NYCRR 500.07 | Model allow/deny lists, role-based access, and tool controls restrict which users and workloads can reach which models and data. | Access policy + RBAC matrix |
Protect nonpublic information, including encryption 23 NYCRR 500.15 | PII and MNPI detection redact sensitive data inline; raw prompts and responses never leave your perimeter; audit data is encrypted at rest with per-customer keys. | Redaction findings + encryption posture |
Monitor activity of authorized users 23 NYCRR 500.14 | Continuous, inline monitoring of AI traffic captures who used which model, what was governed, and what it cost — attributable by team. | Monitoring telemetry |
Manage third-party service provider risk 23 NYCRR 500.11 | A per-customer isolated data plane keeps raw payload inside your environment; only hashed metadata reaches the shared control plane — narrowing third-party exposure. | Deployment isolation + data-flow record |
Maintain audit trails to detect and respond to cybersecurity events
23 NYCRR 500.06
Maps to · A tamper-evident, hash-chained record of every AI request and governance decision — allowed or blocked — gives you an audit trail designed to survive scrutiny.
Examination artifact · Examination-ready audit trail
Limit access privileges to nonpublic information
23 NYCRR 500.07
Maps to · Model allow/deny lists, role-based access, and tool controls restrict which users and workloads can reach which models and data.
Examination artifact · Access policy + RBAC matrix
Protect nonpublic information, including encryption
23 NYCRR 500.15
Maps to · PII and MNPI detection redact sensitive data inline; raw prompts and responses never leave your perimeter; audit data is encrypted at rest with per-customer keys.
Examination artifact · Redaction findings + encryption posture
Monitor activity of authorized users
23 NYCRR 500.14
Maps to · Continuous, inline monitoring of AI traffic captures who used which model, what was governed, and what it cost — attributable by team.
Examination artifact · Monitoring telemetry
Manage third-party service provider risk
23 NYCRR 500.11
Maps to · A per-customer isolated data plane keeps raw payload inside your environment; only hashed metadata reaches the shared control plane — narrowing third-party exposure.
Examination artifact · Deployment isolation + data-flow record
Evidence for the regulator.
The audit trail renders into a package that speaks to Part 500 — the access policy in force, redaction and monitoring findings, and a tamper-evident record with an integrity hash.
In the package
- Access and model-usage policy snapshot.
- PII / MNPI redaction findings over the period.
- Monitoring telemetry attributed by team.
- Hash-chained audit trail with SHA-256 integrity hash.
Part 500 and AI.
Does 23 NYCRR 500 apply to our use of AI?
Part 500 governs the cybersecurity of nonpublic information held by covered financial-services entities. When AI systems process or can access that information, the regulation's audit-trail, access-control, encryption, and monitoring requirements apply to those systems just as they do to the rest of your environment.
How does Meilynx help with the 500.06 audit-trail requirement?
Meilynx records every AI request and governance decision into a hash-chained, tamper-evident audit trail with a multi-year retention floor. Because the chain breaks if a record is altered, it provides the kind of audit trail 500.06 contemplates — one that can detect and help respond to events.
Where does nonpublic information go?
It stays in your perimeter. The proxy processes prompts and responses inside infrastructure dedicated to your organization; only hashed, aggregate metadata flows to the shared control plane. Raw payload never reaches a shared system.
See exactly what an examiner receives
Download a sample examination package — model inventory, control coverage, a governance policy snapshot, and a SHA-256 integrity hash.