Sensitive information moves
Models and connected tools can create unfamiliar paths to customer and business information.
AI security for financial services
Assess emerging AI risks while protecting sensitive information and the trust behind every transaction.
Request an assessmentDefined boundariesReview in contextClear next steps
Security across your AI ecosystem
Models
Applications
Agents
Infrastructure
Data
Innovation carries a new set of risks.
Controls need evidence
Support internal review with documented scope, reproducible findings and clear remediation priorities.
SECURITY FOR FINANCIAL SERVICES
Build security into financial AI workflows
Focused assessment, practical context and clear next steps for your team.
AI DISCOVERY
Know what you need to protect.
Map AI applications, model dependencies, connected services and the sensitive information moving between them. Build a useful starting point for assessment and ownership.
EXPLORE THE APPROACH →Security workspace◌ ⋯
System & data flow
Applications12
Models04
Owners08
Inventory by environment
Inventory
67%
67%
AssetEnvironmentReview status
Customer assistantProductionOpen finding
Retrieval pipelineStagingReviewed
Model endpointProductionReviewed
Internal agentStagingOpen finding
AI SUPPLY CHAIN SECURITY
Make model intake a security decision.
Review model sources, packages and integration boundaries before bringing them into your environment. Understand the risks behind a dependency, not only its name.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Model intake review
Model artifactDependency reviewOrigin · integrity · formats
ArtifactSourceReview status
Model weightsRegistryIn review
Tokenizer configRepositoryReviewed
Loading scriptRegistryIn review
Package manifestRepositoryReviewed
AI RUNTIME SECURITY
Strengthen the controls around AI.
Review identity, data access, output handling and operational visibility. Design defenses around the application and the consequences of its actions.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Control assessment
Request→Policy→Model→Response
Interaction monitoring
InteractionControlStatus
Retrieved contextInput boundaryReview
Tool invocationOutput policyAllowed
Model responseOutput policyAllowed
A practical approach to financial AI risk
Map sensitive data flows
Understand the systems, dependencies and information in scope.
Review model dependencies
Put technical risks in the context of your application.
Validate application controls
Use reproducible findings to guide the work ahead.
Support internal assurance
Give your team practical, documented next steps.
ASSESSMENT PLANNINGStart with the right
Start with the right
security questions.
Talk through your AI environment, key information flows and the areas you need to understand before defining an assessment.
Discuss your scopeVIRTUAL ARC SECURITYAI security
AI security
assessment
From system context
to practical next steps.
“A security assessment should make the next decision clearer. We connect technical findings to your application, your priorities and the work your team can take forward.”
Useful evidence. Clear next steps.Our assessment approach
RESOURCESMake informed
Explore resourcesMake informed
security decisions.
Practical perspectives for financial services.
Ready to understand your AI risk?
Start with your environment, your priorities and a clearly scoped security assessment.
Book a scoping call