NYDFS requires covered entities to maintain risk-based cybersecurity programs, policies, and procedures.
New York State Department of Financial Services · verified 2026-09-22Industry / Financial Services
AI security assessment for Financial Services
Secure AI across sensitive financial data, third-party services, customer workflows, and regulated operations.
01 / CONTEXT
AI changes both the financial-services attack surface and the evidence reviewers expect.
Financial-services organizations are adopting AI across customer service, fraud detection, underwriting, operations, and security. New York’s Department of Financial Services has specifically warned that AI changes the cybersecurity risk landscape for regulated entities and expects those risks to be addressed through the existing Part 500 risk-based cybersecurity framework.
DFS guidance calls out AI-enabled social engineering as well as risks created by an organization’s own use of AI. It also says covered entities using or relying on AI-enabled products should identify the information systems involved and maintain an inventory, with mitigations prioritized for systems critical to ongoing operations.
Ayliea’s financial-services assessment therefore centers on the actual AI surface: system inventory, nonpublic-information flows, access and authentication, third-party dependencies, model or agent privileges, monitoring, incident readiness, and evidence that risk decisions are documented rather than assumed.
02 / Current signals
What shapes the review.
Primary-source requirements and guidance establish the context. The engagement still follows the actual system, data, and use case in scope.
DFS guidance says entities using or relying on AI should identify and inventory the information systems involved.
NYDFS AI Cybersecurity Guidance · verified 2026-09-22DFS says growing reliance on AI and other third-party technologies increases cybersecurity exposure that covered entities must manage.
NYDFS Third-Party Service Provider Guidance · verified 2026-09-2203 / Requirements & frameworks
Map evidence to the environment around the AI.
Frameworks are not treated as interchangeable certifications. Ayliea uses the requirements and guidance that actually belong in the engagement scope.
NYDFS Part 500
For covered entities, New York’s Cybersecurity Regulation requires a risk-based cybersecurity program. DFS has separately explained how AI-related cyber risks fit within that existing framework.
NIST AI RMF
A voluntary cross-sector structure for documenting and managing AI risks, useful alongside sector-specific cybersecurity and governance requirements.
Third-party risk
DFS guidance emphasizes managing cybersecurity risk from third-party service providers, including increasing reliance on AI-enabled technologies.
Use-case requirements
Payments, insurance, lending, broker-dealer, banking, and other financial use cases can carry different regulatory and contractual obligations; assessment mappings are scoped accordingly.
04 / Assessment surface
What we examine in Financial Services.
The exact procedures are scoped to the organization. These are common areas that shape evidence requests and assessor judgment.
Nonpublic Information in AI
Map customer, account, transaction, employee, and other sensitive financial information entering AI systems, including retention, model/provider access, data isolation, and downstream use.
AI Identity & Access
Review user, service-account, API, and agent permissions around high-value systems and data, with attention to MFA, least privilege, privileged actions, secrets, and machine-to-machine access.
Third-Party AI & Concentration Risk
Assess AI providers, embedded models, APIs, and other technology dependencies for security evidence, contractual boundaries, incident obligations, resilience, and the operational impact of provider failure or compromise.
AI-Enabled Customer & Decision Workflows
For fraud, underwriting, service, operations, or other material workflows, document where AI influences actions and whether human review, authorization boundaries, monitoring, and escalation match the risk of the use case.
AI-Enabled Social Engineering Exposure
Evaluate controls relevant to AI-amplified phishing, impersonation, deepfakes, credential theft, and fraudulent requests, including verification processes for sensitive transactions and privileged changes.
Inventory, Monitoring & Response
Establish which AI-enabled systems are in scope, what logs and telemetry exist, how anomalous activity is detected, and how AI-related events connect to incident response, recovery, and regulatory reporting processes.
05 / Common review needs
Start with the problem you are actually trying to solve.
These focused pages go deeper on common security and diligence questions in this industry.
06 / OUTPUT
Evidence a reviewer can interrogate.
Ayliea documents the systems reviewed, evidence examined, findings, limitations, framework mappings, remediation priorities, and accountable human review. The report is designed to support diligence and internal risk decisions—not to substitute for legal advice or a regulator’s determination.
ASSESSMENT RECORD
AI system inventory
Data-flow & boundary review
Evidence references
Findings & limitations
Industry-relevant mappings
Remediation priorities
Accountable sign-off
07 / SCOPE THE WORK
Start with the AI system and the evidence you already have.
We will determine whether an independent assessment fits the review, customer, compliance, or security question you need to answer.