Putting security, governance and cost controls around enterprise GenAI
A centralized AI control layer for organizations moving from isolated GenAI experiments to governed enterprise use. The solution establishes common controls for model access, sensitive data protection, prompt security, usage monitoring, evaluation and cost visibility across AI applications.
AI adoption was moving faster than the controls needed to manage it.
Teams were experimenting with different models, applications and data sources. Without a common control layer, security, governance and cost decisions were being handled differently across projects.
What we found
- Different teams used different AI models and external model endpoints.
- AI applications had inconsistent controls for sensitive information.
- Prompt injection and unsafe input handling were not consistently tested.
- There was limited visibility into which applications were using which models and data sources.
- Model usage and token consumption were difficult to attribute to business teams and use cases.
- AI responses were not consistently evaluated for quality, safety or policy compliance.
- Security and governance teams lacked a repeatable process for reviewing new AI use cases.
What the business needed
- A centralized way to control access to approved AI models.
- Common security checks for prompts, inputs and outputs.
- Protection for sensitive and regulated information before it reached a model.
- Clear policies for approved use cases, models, users and data.
- Usage, token and cost visibility by application, team and business function.
- Evaluation and monitoring after deployment rather than only during development.
- An operating model that allowed AI adoption to continue without creating uncontrolled risk.
We established a centralized AI control layer between enterprise applications and model providers.
The architecture gives security, data and AI teams one place to enforce common controls while allowing individual business applications to evolve independently.
Enterprise AI governance and guardrail platform
The control layer standardizes how AI requests are authenticated, checked, routed, monitored and evaluated.
- Established an AI gateway as the controlled entry point for enterprise AI applications.
- Defined an approved model catalogue based on business use case, data sensitivity, performance and cost.
- Applied identity and access policies before AI requests were processed.
- Added input controls for sensitive data, prompt injection patterns and prohibited content.
- Applied output checks for sensitive information, policy violations and response quality.
- Logged model, application, user, token and cost information for operational visibility.
- Introduced evaluation workflows for accuracy, grounding, safety, latency and failure rates.
- Created approval and review processes for new AI applications and material model changes.
A common control plane connects enterprise data, AI applications and model providers.
The architecture separates business applications from model providers and makes governance controls reusable across AI workloads.
A six stage approach to move from uncontrolled experimentation to governed enterprise AI.
The framework establishes controls early and strengthens them as AI adoption grows across applications, teams and models.
Assess
Inventory AI use cases, models, applications, data sources, users and current controls.
Classify
Define risk tiers based on data sensitivity, business impact and level of automation.
Design
Define model, security, prompt, data, evaluation and cost policies for each risk tier.
Implement
Deploy gateway controls, data protection, model routing, logging and response validation.
Monitor
Track usage, quality, incidents, token consumption, cost and policy violations.
Improve
Use production evidence to update models, prompts, policies and guardrails continuously.
The value comes from making AI safer to scale while improving visibility into how it is used.
Lower unmanaged AI risk
Centralized controls can reduce gaps created when every application implements security and governance independently.
Less manual compliance effort
Common automated checks can reduce repeated manual review of routine AI requests and application controls.
Lower avoidable AI cost
Usage visibility and model routing can identify unnecessary consumption and better fit models to workloads.
Faster AI use case review
Standardized risk assessment and approval patterns can shorten the path from use case proposal to controlled implementation.
Faster incident investigation
Centralized request, model and policy logs make it easier to trace what happened and identify the source of an issue.
Centralized visibility target
Governed workloads can report application, model, usage, policy and cost information through a common control plane.
AI can scale across the enterprise without creating a separate control framework for every application.
The governance layer becomes a shared enterprise capability for security, data, AI and finance teams.
Stronger Security
Common controls protect enterprise data and applications from unsafe inputs, sensitive data exposure and uncontrolled model access.
Faster AI Adoption
Teams can use predefined patterns for model access, security and evaluation instead of designing controls from scratch for every use case.
Better Cost Visibility
Application and model usage can be connected to token consumption and business ownership for more informed AI spending decisions.
Clear Accountability
Every governed AI application can have defined owners, approved models, policies, evaluation criteria and monitoring requirements.
Build the control layer before AI scales across the enterprise.
Establish model access, data protection, prompt security, evaluation, monitoring and cost controls through one governed AI operating model.
Discuss your AI governance program