Governed inventory
01
Placeholder: owned AI assets and workflows
Primary Market
Control enterprise AI in banking before unmanaged usage becomes supervisory risk.
Placeholder: banking-specific introduction copy to be finalized for regulated institutions, board scrutiny, and evidence-backed AI governance.
Shadow AI detectionPrompt controlEvidence ledgerThird-party AI onboarding
Control posture
02
Placeholder: coverage mapped to policy and evidence
Exception queue
03
Placeholder: supervisory review and remediation workload
Evidence readiness
04
Placeholder: pack and ledger readiness for scrutiny
Sector focusBanking
Operating modelEvidence-first control
Review postureBoard, audit, regulator
DeploymentOn-prem / private / hybrid
Operating fit
Built for high-control banking environments
- Customer-data sensitive workflows
- Maker-checker operating model
- Supervisory reporting posture
- Exception and remediation governance
Sector reality
Sensitive customer environments
Control posture
Embedded maker-checker
Deployment fit
Private, hybrid, on-prem
Scrutiny readiness
Supervisory inspection-ready
Why this industry is different
Control requirements are sector-specific, not generic.
Placeholder: concise sector framing to be finalized. This section should explain why AI governance in Banking requires different controls, evidence, and supervisory expectations.
Shadow AI sprawl
Placeholder: unmanaged employee AI usage across front office, operations, risk, and support functions.
Decision accountability
Placeholder: credit, fraud, and servicing workflows require reviewable oversight.
Policy enforcement
Placeholder: guardrails must be embedded, not left to manual discipline.
Third-party onboarding
Placeholder: external AI tools need due diligence and ongoing monitoring.
Priority AI use cases
Structured workflows where governance must be operational, not aspirational.
Placeholder: use-case framing copy to be finalized for Banking.
01 · Employee AI copilots
Employee AI copilots
Placeholder: governed internal usage with attributable records and policy control.
Banking workflow
Employee AI copilots Placeholder design panel to be populated with final product visual or diagram.02 · Customer servicing AI
Customer servicing AI
Placeholder: oversight for AI-assisted servicing, support, and communications.
Banking workflow
Customer servicing AI Placeholder design panel to be populated with final product visual or diagram.03 · Credit and decision support
Credit and decision support
Placeholder: reviewable governance around decision-support workflows.
Banking workflow
Credit and decision support Placeholder design panel to be populated with final product visual or diagram.04 · Fraud and AML support workflows
Fraud and AML support workflows
Placeholder: evidence-backed oversight for high-priority investigative support.
Banking workflow
Fraud and AML support workflows Placeholder design panel to be populated with final product visual or diagram.05 · Third-party AI onboarding
Third-party AI onboarding
Placeholder: due diligence, risk scoring, and approval workflow for vendor AI.
Banking workflow
Third-party AI onboarding Placeholder design panel to be populated with final product visual or diagram.Risk and control model
Map sector risk to required control and expected evidence.
Risk themes
Required controls
Evidence expectations
Unmanaged internal usage
Usage visibility, access policy, prompt control, and exception triage
User attribution, event history, and control action evidence
High-risk workflows
Named owners, review workflow, and monitoring thresholds
Decision lineage, reviewer actions, and remediation trail
Third-party risk
Due diligence workflow, contractual control expectations, and monitoring
Vendor dossiers, approvals, and periodic review outputs
How SENTRUM fits
Modules selected for this industry control model.
These are the modules most relevant to the Banking landing page. Final module copy can be expanded in the content round.
AI Usage Visibility
Detect who is using what, where, and under which ownership structure.
Shadow AI Detection
Find unmanaged tools and convert findings into governed action.
Policy Enforcement & Control
Apply AI policy, prompt controls, and review gates consistently.
Audit Evidence Ledger
Reconstruct Prompt Guard and control decisions under audit challenge.
AI Due-Diligence Workflow
Govern onboarding of third-party AI tools, models, and workflows.
Compliance Reports
Generate regulator-ready reports grounded in evidence, not narrative only.
Operating stakeholders
Multi-buyer relevance for enterprise sales, governance, and implementation.
CRO / Risk
Placeholder: risk tier visibility, exception tracking, and control posture by workflow.
Compliance
Placeholder: policy adherence, obligations mapping, and defensible reporting.
Internal Audit
Placeholder: exportable evidence lineage, reviewable decisions, and proof of control execution.
CIO / Technology
Placeholder: deployment governance, integration fit, and operating-model control.
Deployment and architecture fit
Control architecture for front office, operations, risk, and technology
Placeholder: architecture narrative for banking-grade AI controls, evidence lineage, and supervisory reporting.
Architecture notes
- Segregation of duties and maker-checker flow
- Policy and Prompt Guard integration points
- Evidence-first escalation and remediation workflow
Evidence and reporting
Designed for audit, executive review, and regulator-facing evidence requests.
Placeholder: final copy to describe evidence capture, approval lineage, exception reporting, and pack generation.
FAQ
Decision-stage questions for deployment, control, and evidence.
Can we govern internal copilots and third-party AI together?
Placeholder: answer to be finalized.
Can this support regulator or internal audit evidence requests?
Placeholder: answer to be finalized.
Does the platform fit private cloud or on-prem deployment?
Placeholder: answer to be finalized.
Next step
Move from sector interest to architecture-level discussion.
Placeholder: final CTA copy to be aligned to Banking buyer priorities.