AI is moving into some of the most sensitive areas of financial services, from research and advisory copilots to customer service, payments, credit, onboarding and AML. Those are risks that traditional cybersecurity and model risk management were not built to address.
Cygeniq helps banks and financial institutions discover AI assets, test AI systems against real-world threats, enforce runtime guardrails, govern AI risk and detect AI-native attacks, all through one Runtime AI Trust Platform.
One platform across banking, risk and customer AI
Banks and financial institutions are deploying AI where access to sensitive information and critical systems is highest.
A malicious instruction hidden in a customer document, email or KYC file can manipulate an AI system. An AI agent with legitimate system access can be influenced into taking an unintended action. Unapproved AI tools can also operate outside established security and governance processes.
AI copilots and agents just support staff and customers, so existing cybersecurity and model risk management already cover them.
AI agents can carry legitimate system access, and a malicious instruction hidden in a document, email or KYC file can influence that agent into taking an unintended action.
This creates risks including:
Financial services AI security needs to protect the model, data, prompts, agents, tools and actions around it.
AI copilots working with internal research and sensitive information can expose confidential data when manipulated through untrusted content or prompts.
AI agents connected to business systems can turn a manipulated instruction into an unintended action through legitimate permissions and tool calls.
AI models supporting credit, onboarding and AML processes require strong governance, oversight, risk monitoring and supporting evidence.
KYC files, claims, loan applications and other third-party content can carry malicious instructions into AI-powered workflows.
Unapproved copilots and AI agents may operate outside the organization's inventory, risk assessment, security controls and audit processes.
Cygeniq brings AI discovery, adversarial testing, runtime protection, AI risk management, governance and AI-native threat detection together through one Runtime AI Trust Platform.
Create an AI Bill of Materials (AI BOM) covering models, agents, prompt flows, data sources and APIs, including sanctioned and shadow AI. Maintain visibility into ownership, data classification and risk tier so security and governance teams understand what AI exists and where risk is concentrated.
Run adversarial red-team testing against AI applications, copilots and agents to identify weaknesses such as prompt injection, jailbreaks, sensitive information exposure, manipulated customer documents and unsafe AI agent behavior. Testing can be mapped to established AI security frameworks, including the OWASP Top 10 for LLM Applications, OWASP Agentic guidance and MITRE ATLAS.
Monitor and validate AI interactions while systems are running. Runtime guardrails help organizations apply controls across prompts, outputs and AI agent actions. Changes to models, prompts or connected knowledge sources can also trigger renewed security testing, extending security from one-time testing to continuous AI protection.
Understand AI risk at the individual asset level based on factors such as the sensitive data, systems and business processes an AI application can access. Risk information can then be viewed from the individual AI asset through business-unit and enterprise levels, helping security and risk leaders prioritize the systems that matter most.
Bring AI assets, risks, controls and supporting evidence into a structured governance environment. Cygeniq helps financial institutions map AI controls across relevant frameworks and requirements and maintain evidence collected from the operating environment, supporting a more continuous approach to financial services AI governance and AI compliance.
Extend existing security operations with visibility into attacks occurring through the AI layer. CyberTix AI helps identify threats such as data exfiltration through AI outputs, AI agent misuse, workflow poisoning and suspicious AI interactions, connecting with existing security operations and response workflows.
AI-crafted phishing and BEC against treasury and payments, deepfake voice and video that impersonates executives or customers to authorize transfers or pass verification, AI credential attacks on customer and employee accounts, and autonomous machine-speed intrusions, detected, blocked and contained under human control.
Book a walkthrough of discovery, adversarial testing, runtime protection and AI-native detection for financial services AI.
| AI Use Case | Key AI Security Risk |
|---|---|
| Advisory & Research Copilots | Prompt manipulation and exposure of sensitive or confidential information |
| Customer Service & Banking Copilots | Sensitive data exposure, unsafe responses and unauthorized access to customer information |
| Service & Payment Agents | Excessive permissions, manipulated instructions and unintended actions through connected systems |
| Credit, Onboarding & AML Models | AI governance, risk monitoring, human oversight and decision-related risk |
| KYC & Document Processing | Malicious instructions embedded in customer documents, emails and other third-party content |
| AI-Powered Fraud Workflows | Manipulated inputs, unreliable AI decisions and misuse of connected tools |
| Internal Enterprise AI | Shadow AI, uncontrolled data access and AI systems operating outside approved governance processes |
Controls and evidence map once, in GRCortex AI, to the regimes a financial institution answers to: the EU AI Act (credit scoring and creditworthiness assessment are high-risk uses), DORA and NIS2, NYDFS Part 500, PCI DSS 4.0, model risk management expectations such as SR 11-7, ISO/IEC 42001 and the NIST AI RMF, so one control satisfies many regimes, with technical testing aligned to the OWASP LLM and Agentic Top 10 and MITRE ATLAS.
Continuous adversarial red teaming from a 1M+ scenario library, model validation and drift monitoring for every model, copilot and agent in scope, with findings flowing into GRCortex AI as evidence and into CyberTix AI as detection context.
Dynamic AI risk register fed by runtime findings, controls mapped once across every framework in scope, continuous control monitoring, audit-ready evidence and board-ready AI assurance.
The runtime security layer for enterprise AI. CyberTix Secure discovers every LLM, RAG pipeline and agent, blocks AI-native attacks inline and contains threats in real time. CyberTix Defend stops AI phishing and BEC, deepfakes, AI malware and autonomous intrusions, one backbone, one AI-risk picture.
Discover → Understand → Validate → Protect → Govern → Monitor → Assure → Improve
A financial services customer runs Cygeniq across credit-scoring AI, customer and HR chatbots and a loan-approval bot, with an AI risk register, compliance mapping and continuous control monitoring, no AI security breaches during the engagement, full regulatory compliance and a protected AI attack surface.
Reference calls available under NDA.AI security for financial services protects AI models, applications, copilots and agents used by banks and financial institutions. It addresses risks such as prompt injection, sensitive data exposure, unsafe AI agent actions and shadow AI.
Banks should identify their AI assets, understand what data and systems they can access, test them against AI-specific attacks, apply runtime guardrails and maintain appropriate security and governance controls. Cygeniq brings these capabilities together across GRCortex AI, Hexashield AI and CyberTix AI.
Common AI security risks for banks include prompt injection, jailbreak attacks, sensitive data exposure, unsafe AI agent actions, retrieval poisoning, excessive permissions and shadow AI operating outside approved security and governance processes.
Cygeniq helps financial institutions discover AI assets, quantify their risk, test them against threats, monitor runtime behavior and maintain governance evidence.
GRCortex AI creates an inventory of AI assets and connects them with ownership, risk, controls and evidence. This helps financial institutions manage AI governance more consistently across models, applications, copilots and agents.
Hexashield AI tests AI applications and agents against direct and indirect prompt injection and provides runtime controls across prompts, outputs and AI agent actions.
No. Cygeniq complements existing model risk management and cybersecurity programs by adding AI asset discovery, AI-specific security testing, runtime protection, risk quantification and AI-native attack detection.
Yes. CyberTix AI extends existing security operations with visibility into AI-native attacks and supports investigation and response workflows.
Gain visibility into your AI environment, identify risk, test AI against real-world attacks and apply security and governance controls as AI systems evolve.
Cygeniq helps you secure AI systems, govern AI risk and prove control.
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