AI Governance for Financial Services.
Traders, analysts, underwriters, and operations staff are sending account data, positions, client identifiers, and counterparty material into ChatGPT, Copilot, Azure OpenAI, and internal AI tools. The gateway between those users and the model is where GLBA, SOX, PCI DSS, FFIEC, NYDFS 23 NYCRR Part 500, and DORA need to be applied, because the content control plane the LLM provider offers stops at the model boundary and is blind to the customer policy.
DeepInspect runs inline in front of the AI provider. Non-public personal information, cardholder data, material non-public information, and counterparty identifiers are detected and transformed before the payload leaves the customer environment. Every decision is written to a tamper-evident forensic record with the policy version, the actor identity, and the original and transformed payloads preserved. The same configuration applies to interactive chat, retrieval-augmented applications, and autonomous agent workflows that reach into core banking, ledgers, and trading systems.
The risk surface in financial AI
NPI and cardholder data inside prompts
Staff paste account numbers, balances, PAN, SSN, beneficiary records, KYC documents, and client correspondence into AI tools to summarize, classify, or draft responses. Once that payload leaves the customer boundary, the GLBA Safeguards Rule and the PCI DSS service-provider agreement are the only remaining controls, and those agreements cover retention and downstream use, not the act of disclosure.
Material non-public information in research and IB workflows
Deal teams, M&A advisors, and equity research staff prompt models with draft memos, earnings drafts, deal codenames, and counterparty positions. The information barrier inside the firm is enforced at the system layer. The AI gateway is part of that system layer, and absent enforcement, the barrier breaks the moment an analyst pastes a deal name into a shared chat window.
Examiner-grade audit evidence
FFIEC, OCC, FINRA, SEC, and state regulators ask the same question of AI usage that they ask of any production system: who accessed what data, when, under which policy, and what decision did the system produce. Most institutions are unable to answer because the AI interaction log either does not exist or sits inside the LLM provider in a form the examiner cannot retrieve.
Agents reaching into core banking and trading systems
Autonomous agents now query general ledgers, trade blotters, CRM systems, and customer servicing platforms. A misrouted tool call or a prompt-injected agent can move money, change records, or exfiltrate positions. The control needs to live at the agent gateway, because the downstream system trusts the agent identity.
How DeepInspect applies controls
NPI, PAN, and counterparty detection
Deterministic detectors match the data classes that GLBA, PCI DSS, and the firm-specific information classification scheme identify as sensitive. Account numbers, PAN with Luhn validation, SSN, ITIN, IBAN, SWIFT codes, and counterparty identifiers are each matched and routed to the configured action for the user role in effect.
Identity-aware policy and information barriers
Role identity is supplied by the customer IdP at request time. The gateway evaluates the per-role action map and applies the matching transformation. Investment banking staff are blocked from prompting with deal codenames while equity research analysts see tokenized counterparties on the same policy. The action map is part of the policy version, so role changes are captured in the audit trail.
Evidence-grade forensic record
Every interaction writes a signed record containing the actor identity, the policy version, the rule evaluation path, the original payload, the transformed payload, and the upstream response. The signature anchors integrity. The record set is queryable by examiners against a read-only projection, and the audit path leaves its own trace so the institution sees exactly what was retrieved.
Prompt injection and adversarial input handling
Adversarial inputs attempting to override instructions, extract positions, or pivot an agent into restricted systems are scored against the configured detectors and blocked or routed to escalation according to policy. The score, the input, and the action are preserved in the forensic record.
Tool and agent allowlists for core systems
Autonomous agents reach general ledgers, payments rails, trade blotters, and CRM systems. The gateway enforces allowlists and blocklists on the tools an agent invokes and the data sources it reads. An agent that attempts to call a system outside its allowlist is stopped at the gateway with a record of the attempt and an alert routed to the SOC.
Forensic deep analysis for fraud and slow exfiltration
Patterns across the forensic store surface anomalous access, repeated near-miss policy hits, coordinated prompt sequences, and the kind of slow exfiltration that single-event monitoring misses. The analysis runs against the customer projection and produces queryable findings that map back to the source interactions.
Regulatory mapping
GLBA Safeguards Rule
NPI detection and transformation apply 16 CFR Part 314 handling at the AI layer. The signed audit trail supports the 314.4(d) periodic risk-assessment evidence requirement and the 314.4(i) change-management documentation requirement. Identity-aware access decisions cover the 314.4(c)(1) access controls obligation. The forensic record supports the 314.4(h) incident response program with the original payload preserved.
SOX Section 404 and PCAOB AS 2201
AI-assisted financial reporting workflows fall inside the scope of ICFR. Cryptographically signed, attributable records of every AI interaction in a reporting workflow provide the evidence external auditors need to test the control. Policy versioning produces the change-control trail that AS 2201 expects on automated controls.
PCI DSS 4.0
PAN detection with Luhn validation prevents cardholder data from entering the model context. Requirement 3.4.1 truncation and tokenization, 10.2 audit log generation, and 12.10 incident response evidence map directly to the gateway record. Requirement 8.2 user identification persists into every AI interaction.
NYDFS 23 NYCRR Part 500
500.6 audit trail requirements, 500.7 access privileges, 500.14 monitoring and training, and 500.17 cybersecurity event notification all rest on the contemporaneous record that the gateway produces. The 72-hour notification clock under 500.17(a) is unworkable without that record.
FFIEC IT Examination Handbook
The Operations and Information Security booklets expect contemporaneous audit logs, identity-based access, and third-party risk evidence on every production data path. The gateway record is the artifact examiners ask for when an AI workflow is in scope.
DORA (EU 2022/2554)
LLM providers are ICT third-party service providers under Article 28. The gateway enforces the contractual boundary, feeds the register of information under Article 28(3), and produces the operational resilience evidence Articles 24 through 27 require. Article 17 incident classification and reporting relies on the same record set.
EU AI Act
Credit scoring and creditworthiness assessment of natural persons are high-risk uses under Annex III. Policy versioning produces the change-control trail relevant to Article 17. The forensic record covers Article 12 record-keeping. Inline enforcement with fail-closed default behavior addresses Article 9 risk management.
The scale of the gap
is the average cost of a data breach in the financial services sector, the second-highest of any industry and 22% above the cross-industry average of $4.88M.
of organizations across regulated sectors reported confirmed or suspected AI agent security incidents in the past year. Financial services sit at or above that average in every published industry breakdown.
of builders cite the absence of auditability and logging as a top concern. Only 7.7% audit agent activity daily, which leaves most institutions without the contemporaneous record that NYDFS Part 500.6 and FFIEC examination guidance require.
of teams treat AI agents as identity-bearing entities. The remainder authenticate agents with shared API keys or hardcoded credentials, which makes per-agent attribution and the FFIEC and NYDFS access-management expectations impossible to satisfy.
of annual worldwide turnover is the upper bound on DORA penalties for ICT third-party risk failures under the supervisory framework. The number turns on whether the financial entity can produce contemporaneous evidence of the controls in place.
Source: Regulation (EU) 2022/2554 (DORA).
Deployment
The gateway runs self-hosted in the customer VPC or on-premises. SaaS and hybrid deployments are available for organizations with different sovereignty requirements. Cardholder data, NPI, the forensic store, and the transaction object store stay inside the customer boundary in every configuration.
DeepInspect sits inline between users, agents, and the AI provider. It works with OpenAI, Azure OpenAI, Anthropic, Bedrock, and internal models without requiring a model migration. Existing IdP, SIEM, DLP, and core banking integrations stay in place. Production cutover typically lands inside two weeks for a defined application scope.
Policy on every AI interaction, enforced before data leaves the boundary.