The AI Risk Your Institution
Can't Afford to Ignore.

The speed at which generative AI is being deployed across financial services has outpaced the security frameworks most institutions have in place to govern it. The attack surface has fundamentally changed. The question is whether your security posture has changed with it.
The Stakes

AI Has Changed The Game for CISOs

Public-facing AI apps and agents have handed attackers a new front door, fundamentally changing the attack surface. Prompt injection, data exfiltration through chatbot interfaces, and model manipulation are now live threat vectors CISOs must own.

01

Customer-Facing Chatbots

Enforce compliance boundaries in real time and block prompt injection attempts before they reach your model. Build the audit evidence your supervisory teams require into every interaction from day one.

02

Fraud Detection & Financial Crime

Stress-test your fraud detection systems against real-world manipulation patterns before adversaries find the gaps. Understand exactly how your models behave under pressure before they encounter it in production.

03

Agentic AI Workflows

Test financial agents against structuring attacks, impersonation attempts, and multi-step exploitation chains before they reach customers. Then, monitor continuously in production so drift and regressions don't become compliance exposures.

04

Wealth Management & Investment AI

Keep AI-generated recommendations and market commentary within defined regulatory boundaries across every interaction and channel. Maintain centralized visibility across all deployments so nothing operates outside policy.

One platform for every stage
of your AI lifecycle.

Pre-Deployment
AI Application Red Teaming at Scale

Simulate context-aware scenarios before a single customer interaction. Finance-specific presets mean coverage for the failure modes that matter.

  • AML, suitability, MNPI, and scope drift presets out of the box
  • Adversarial scenarios based around your app's purpose and archetecture
  • SR 11-7 examination-ready
Screenshot of FinBot v2.3 showing blocked messages flagged for safety policies including fraud, violent extremism, and model poisoning, alongside a chat with requests and responses containing sensitive employee data and instructions to bypass firewalls.
Runtime
Finance-Specific Guardrails in Production

Sub-150ms enforcement at every customer interaction. Jurisdiction-aware: the same deployment applies FCA rules for UK users and FINRA rules for US users simultaneously.

  • Real-time detection across chat, voice, and agentic interactions
  • Automated response workflows for no engineering overhead
  • Audit trails at every interaction, structured for examination
Dashboard screen showing WonderFence app insights with FinBot monitoring OpenAI API version 2.2 in production, displaying 200 tests, 12/89 active fences, 50% health score, activated fences list, and a violations over time graph from January to March.
ongoing evalutions
In-Production Drift Detection

What passed your last examination may not pass today. Every model update is a potential regression, so WonderCheck catches vulterabilities before your examiners do.

  • Automated adversarial evaluation triggered by model updates.
  • Guardrail validation that identifies false positives and false negatives.
  • Policy impact analysis before WonderFence changes go live.
  • Periodic testing evidence trail for examination demonstration
Dashboard interface of WonderCheck showing 17 apps monitored with continuous monitoring, 8,420 live tests from 1st to 25th today, and 3 applications affected by drift detected.

Your AI Can Be Exploited Before You Know It.

See how WonderSuite tests, guardrails, and monitors customer-facing AI so financial institutions can deploy with confidence and stay compliant.
Alice client case study - banking and financial services platform using Alice AI security and safety for GenAI governance and compliance

What the Alternatives Don't Cover.

AlternativeThe Gap
AWS Bedrock GuardrailsContent filtering for internal AI, but no finance-specific detectors, no scope enforcement, and no multi-jurisdictional mapping. Tells you what the model said, not whether it was licensed to say it.
Azure AI Content SafetyHarmful content detection without financial services compliance. No AML, suitability, or MNPI detectors. Governs the model, not the conversation with your customer.
Cisco AI Defense via Robust IntelligenceStrong on model security and adversarial robustness, but weaker on customer-facing regulatory compliance and scope enforcement. Enterprise-broad, not finance-deep.
HiddenLayerProtects the model from attack, but doesn't address what the model says to customers, scope drift, or regulatory consequences of AI conversations in financial services.
In-house SolutionsYou're building generic guardrails without a decade of adversarial intelligence, finance-specific detector libraries, or multi-jurisdictional regulatory mapping. And you're building it while your AI is already talking to customers.
Rabbit Hole - The Intelligence Core

We've Seen the Worst so Your Customers Don't Have to.

Rabbit Hole is the adversarial engine behind WonderSuite. Built on a decade of global trust and safety research and billions of real-world deceptive and manipulative samples, instead of only synthetic data, so that you so you can launch financial AI with confidence that your system has been tested against the threats it will actually face, not the ones someone imagined in a lab.

10+
YEARS聽RESEARCH
10B+
SAMPLES聽ANALYZED
120+
LANGUAGES
Multi-Jurisdictional Coverage

Map Your AI to Every Framework and Regulation

Easily create custom controls that map to any internal or regulatory policies and enforce them across your full AI lifecycle, giving you the flexibility to maintain compliance with virtually any framework or regulation.

UNITED STATES
SEC 路 FINRA 路 SR聽11-7 路 OCC 路 CFPB
Investment advice, model risk management, AML obligations, automated decision disclosures.
UNITED聽KINGDOM
FCA FS22/1 路 Consumer Duty PS22/9
AI/ML guidance, consumer outcomes, fair treatment across every AI-assisted customer interaction.
EUROPEAN聽UNION
EU AI聽Act 路 DORA 路 MiFID II
High-risk AI for credit and risk scoring, digital operational resilience, research and advice rules.
AUSTRALIA
ASIC RG 255 路 APRA CPG 234
Digital advice guardrails, operational resilience for AI-powered financial services delivery.
CANADA
OSFI E-23 路 FINTRAC AML
Model risk management for AI, AML obligations in automated customer interactions.
GLOBAL
FSB 路 IOSCO 路 OWASP LLM Top 10
Cross-border governance frameworks for institutions operating across multiple jurisdictions.

Ready to advance unafraid? Let's talk.

Questions Finance Teams Ask Us

How does WonderSuite handle agentic AI systems that take actions rather than just answer questions?

Agentic systems introduce risks that static guardrails weren't designed for. Multi-step vulnerabilities, tool compromise, and context manipulation across conversation turns require a different approach. WonderSuite tests agents against the full spectrum of these attack vectors in simulation before deployment and monitors them continuously in production.

We're concerned about latency. Will WonderSuite slow down our customer-facing financial AI?

WonderFence operates at under 100ms, purpose-built for high-traffic, latency-sensitive environments like payment agents and customer-facing banking applications. Protection doesn't come at the cost of customer experience.

Our AI operates across multiple regions and languages. Can WonderSuite handle that?

WonderSuite covers 120+ languages with native speaker-level nuance, including regional and cultural context. Misclassification from multilingual gaps is one of the most common failure points in global financial AI deployments. WonderSuite is built to close them.

How does WonderSuite address fraud and financial crime risks in agentic workflows?

Financial agents are vulnerable to structuring patterns, impersonation, and multi-step exploitation that bypass individual transaction checks. WonderSuite tests for these vectors in realistic simulated environments and enforces zero-trust verification policies at runtime so fraud risk is addressed at the system level, not just the input level.