AI Governance
We help businesses put the right controls around AI: who can do what, which actions need approval, how outputs are evaluated and how every decision can be traced.
The service
Governance is most effective when it is built into how AI systems work, not added afterwards as paperwork. That means approval steps, permission models, logging and evaluation implemented in the software itself.
We support both new and existing AI initiatives with practical risk assessments and technical controls. We do not certify compliance with any law or standard; we help you build the evidence and controls your compliance and legal teams need to make that assessment.
Teams use AI tools informally, with no visibility into what data is shared or how outputs are used.
When an AI system produces a result, nobody can explain how it got there.
It is not defined who approves AI actions or who is responsible when something goes wrong.
AI output quality changes over time and nobody is measuring it.
Key capabilities
Structured review of use cases, data flows, impact and required controls.
Approval steps placed where risk or impact requires human judgment.
Permission models for users, agents and data sources.
Tamper-evident logging of inputs, outputs, tool calls and approvals.
Test suites and ongoing sampling to measure accuracy, safety and consistency.
Track quality, cost, latency and anomalies with alerts when thresholds are crossed.
Realistic use cases
Illustrative examples of how businesses apply this service. Outcomes depend on your process, data and scope.
Example workflow
Assessment before build, controls during operation and evidence throughout.
01 · Trigger
Document purpose, data, users and potential impact.
02 · AI step
Classify risk and define the required controls.
03 · Systems & tools
Access, approvals, logging and evaluation built in.
04 · Human checkpoint
A named person reviews results and approves changes.
05 · Outcome
Ongoing metrics, audits and improvement cycles.
Security & governance
We provide controls and documentation; your legal and compliance teams determine regulatory compliance.
Our approach can be aligned with recognized frameworks your organization chooses to follow.
Data minimization, redaction and retention controls are considered from the start.
Controls match the risk: lighter for internal drafting tools, stronger for customer-facing or financial actions.
Implementation approach
List current and planned AI use across the business.
Evaluate risk, data handling and existing controls for each use case.
Add technical controls, approval flows and evaluation.
Monitor, review and update controls as systems and requirements change.
FAQ
No service can automatically make a system compliant. We implement controls and produce documentation that support your legal and compliance teams in making that determination.
Yes. We design approval queues where reviewers can approve, edit or reject AI outputs before they take effect, with a full audit record.
Through least-privilege access, encryption, careful choice of model providers and data processing terms, redaction where appropriate and retention controls.
Yes, but proportionate. A small internal tool needs lighter controls than a system that acts on customer accounts. We scale recommendations to the risk.
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