AI Visibility and Control for Financial Services
Maintain visibility, operational boundaries, and runtime control as AI systems spread across banking, wealth management, trading, servicing, and operational workflows.
The Challenge: AI Is Spreading Faster Than Financial Institutions Can Control It
Financial institutions are rapidly deploying AI across customer operations, internal workflows, decision support systems, and employee productivity tools. As adoption grows, visibility into what AI systems can access, automate, and change becomes increasingly difficult to maintain.
Operational Visibility Breaks Down
AI systems often span multiple teams, business units, and platforms. Financial institutions struggle to understand where agents exist, who owns them, what systems they access, and how they are being used.
Sensitive Financial Data Moves Across More Systems
Customer records, transaction data, communications, documents, and operational workflows become increasingly connected to AI systems. Without visibility and boundaries, exposure risk grows quickly.
Permissions and Integrations Accumulate Over Time
AI systems rarely become risky overnight. Access expands incrementally through new integrations, workflows, permissions, and automations until operational complexity becomes difficult to manage.
High-Impact Decisions Require Oversight
Whether supporting customer operations, fraud investigations, underwriting, wealth management, or internal processes, organizations need confidence that AI systems remain within approved operational boundaries.
How Prefactor Helps Financial Services Teams Stay in Control
Prefactor provides runtime visibility, operational boundaries, and intervention across AI systems as adoption scales.
Agent Inventory
Maintain a centralized view of active AI systems, ownership, connected systems, frameworks, and operational scope.
- Agent registration
- Ownership visibility
- Connected system tracking
- Deployment and lifecycle status
Runtime Boundaries
Define operational limits around what AI systems can access, automate, and change.
- Real-time enforcement
- Access restrictions
- Action blocking and throttling
- Approval and escalation workflows
Sensitive Data Detection
Detect and respond to sensitive financial data exposure across AI systems in real time.
- Financial PII detection
- Real-time classification
- Configurable protection rules
- Automated escalation workflows
Runtime Activity History
Every runtime action, access attempt, escalation, and policy decision is logged and queryable.
- Immutable activity records
- Full-text search
- Operational investigation support
- Incident and audit workflows
Operational Drift Detection Coming Soon
Detect changes in access patterns, integrations, permissions, and runtime behavior before they become operational incidents.
- Drift monitoring
- Risk pattern detection
- Threshold-based alerts
- Automated response workflows
Runtime Risk Scoring Coming Soon
Aggregate runtime activity, permissions, integrations, policy violations, and sensitive data exposure into a unified operational risk signal.
- Multi-factor scoring
- Configurable thresholds
- Trend-based alerts
- Automated safeguards
Built for Financial Services AI Operations
Prefactor supports operational visibility and runtime control across:
Wealth management assistants
Customer servicing workflows
Fraud operations
Internal copilots
Trading and research support
Banking operations
AI-enabled operational tooling
Operational Visibility Across Every Framework
Track AI systems across every framework, workflow, and connected system from a single operational layer.
Claude
OpenAI
LangChain
CrewAI
AutoGen
Semantic Kernel
Google ADK
Custom Frameworks
Regulatory Alignment
Prefactor helps organizations operationalize AI governance expectations while maintaining visibility into real-world AI operations.
APRA CPS 234
MAS Technology Risk Management
Fed SR 11-7
Basel III and Model Risk Management
Internal Risk and Compliance Programs
Frequently Asked Questions
How does Prefactor help with regulatory compliance for AI systems in banking?
Prefactor provides runtime visibility, immutable activity history, sensitive data detection, and operational boundaries that help financial institutions maintain evidence and control across AI systems.
Can Prefactor detect sensitive financial information?
Yes. Prefactor helps detect sensitive financial data exposure across AI systems in real time, including financial PII and customer data moving through connected workflows.
What compliance frameworks does Prefactor support?
Prefactor helps teams operationalize expectations across APRA CPS 234, MAS Technology Risk Management, Fed SR 11-7, Basel III, model risk management, and internal risk programs.
How does Prefactor help financial services teams scale AI safely?
Prefactor gives teams a centralized view of active AI systems, connected systems, runtime activity, operational drift, and enforcement actions so AI adoption can scale with control.