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How to Control LLM Usage in Enterprise
Controlling LLM usage is one of the biggest challenges in enterprise AI adoption.
The Problem
Without control:
- Teams use different models
- Data flows unpredictably
- Costs become difficult to manage
- Security risks increase
What Control Looks Like
Enterprises need:
- Standardized model access
- Centralized routing of requests
- Visibility into usage
- Governance across teams
Why This Is Hard
Most tools:
- Focus on individual usage
- Do not provide system-level control
- Operate outside your infrastructure
How Peridot Helps
Peridot enables:
- Multi-model control
- Centralized governance
- Visibility into usage
- Secure deployment
Summary
LLM usage without control leads to:
- Risk
- Cost overruns
- Fragmentation
The Real Issue
Most companies think the risk is using AI.
It’s not.
The real risk is:
- Using AI without control
- Letting data flow outside your environment
- Allowing teams to adopt tools without governance
This is how shadow AI spreads.
The Shift
Instead of asking:
“Is this tool safe?”
Enterprises should ask:
“Do we control how AI is used across the company?”
This is especially important for preventing data leakage in AI
Where Peridot Fits
Peridot exists for this exact reason.
It allows companies to:
- Keep AI inside their environment
- Control data, models, and access
- Prevent shadow AI
- Scale usage safely across teams
AI adoption is inevitable.
Lack of control is optional.
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