AI access controls
Define which users, teams or agents can access specific AI capabilities, data and tools.
AI Governance & Security for
Production AI Systems
Control matters as much as capability
As AI becomes part of business-critical workflows, the question isn’t just what it can do — it’s what it’s allowed to do, who’s accountable, and what happens when something goes wrong.
We help you answer those questions before they become incidents.
Our AI governance and security work includes
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AI access controls
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Data governance
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Agent permissions
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Human oversight
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AI monitoring
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Auditability
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Model and vendor risk
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AI security architecture
What we help with
Data governance
Control how sensitive, confidential and regulated information is exposed to models.
Agent permissions
Define what AI agents are authorised to do across connected systems.
Human oversight
Introduce approval points where decisions or actions require human review.
AI monitoring
Track system behaviour, usage, failures, cost and unusual activity.
Auditability
Create records of important prompts, retrieval, actions and decisions where appropriate.
Model and vendor risk
Assess the implications of different model providers, deployment approaches and third-party platforms.
AI security architecture
Design controls around authentication, APIs, secrets, tools and infrastructure.
Identity and permissions
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User identity
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Organisation
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Agent identity
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Role
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Data access
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Tool access
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Action permissions
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Time limits
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Approval requirements
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Environment
Sensitive data
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Personally identifiable information
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Financial information
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Health information
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Confidential business data
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Intellectual property
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Customer data
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Internal documents
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Credentials and secrets
How we think about AI risk
Build control into the system from the start
Control is designed in from day one: who can use the AI, what data and tools it can access, what requires approval, and how decisions are logged and access can be revoked.
AI agents create a new security boundary
Agents raise questions traditional software doesn't: who the agent is, who authorised it, what it's allowed to access and do, and when it should stop and ask a person first.
Protecting against prompt injection
We defend against prompt injection with trusted data boundaries and tool restrictions, input validation and permission checks, and a clear separation between instructions and retrieved content.
AI evaluation as governance
Evaluation is itself a governance tool: tracking accuracy, groundedness and hallucination, task completion and failure rates, escalations and bias indicators alongside cost and latency.
Human-in-the-loop controls
Read automatically
Retrieve approved information.
Recommend automatically
Suggest an action without executing it.
Prepare automatically
Create the action and wait for approval.
Execute within limits
Perform predefined low-risk actions.
Escalate
Send higher-risk or unusual cases to a person.
Logging and audit trails
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User
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Agent
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Model
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Prompt
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Retrieved information
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Tools used
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Actions taken
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Approval
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Outcome
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Timestamp
Model and vendor governance
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Approved model providers
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Data processing
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Retention policies
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Hosting location
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Model changes
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Version control
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Cost
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Reliability
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Vendor dependency
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Fallback models
AI security reviews
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Architecture
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Authentication
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Permissions
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Model access
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RAG pipelines
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Agent tool access
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APIs
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Secrets management
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Logging
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Human approval
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Data boundaries
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Monitoring
Our approach
Our approach
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01. Understand
Map the AI system, users, data and workflows.
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02. Identify risk
Determine where AI behaviour could create meaningful impact.
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03. Define controls
Set permissions, approval rules and data boundaries.
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04. Implement
Build controls into the architecture and application.
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05. Evaluate
Test expected behaviour and failure scenarios.
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06. Monitor
Track the system in production.
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07. Evolve
Adjust controls as use cases and autonomy expand.
Where stronger governance matters
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Financial services
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Healthcare
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Legal services
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Government
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Compliance
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HR
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Enterprise software
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AI agents
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Sensitive customer data
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Automated decisions
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High-value transactions
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Regulated environments
Why WeUno
Governance connected to engineering
We can implement controls rather than only recommend them.
AI and software security together
We consider the model, application, APIs, permissions and infrastructure as one system.
Designed for agentic AI
We understand that agents introduce new questions around identity, authority and actions.
Practical risk management
The aim is not to prevent businesses using AI. It is to make increased capability manageable.
Related AI services
Agentic AI Development
We build AI agents that can do more than answer questions. WeUno designs agentic systems that can reason across tasks, use tools, retrieve trusted information and take controlled actions across business systems.
AI Readiness & Strategy
Before investing heavily in AI, understand whether your data, systems, people and processes are ready. WeUno helps businesses assess their current position, identify realistic opportunities and create a practical roadmap for adoption.
AI Consulting
We help businesses work out where AI can create real value, what should be built and how to move from experimentation into production without wasting time or budget.
AI Integration & Automation
Add AI to the tools and workflows you already use. WeUno connects AI models, agents and automation into existing software so businesses can improve efficiency without rebuilding everything from scratch.
RAG Development
We build RAG systems that connect large language models to your own documents, data and knowledge sources. This helps AI produce more accurate, relevant and explainable answers based on information your business actually trusts.