AI inventory & shadow AI discovery
Azure OpenAI and AI Foundry deployments, Copilot usage, and third-party AI apps found and catalogued, each with an owner and a stated purpose.
Solutions · AI & Automation
AI Audit & Governance
Copilots, chat assistants, and models are already in use across your organisation — some approved, some not, and some fed with company data. When a customer, auditor, or regulator asks what AI you run and how it is controlled, you need an answer with evidence behind it. Symetriz audits your AI estate and puts governance in place on Azure and Microsoft Purview.
The problem
Who signed off on that model?
You don't know what AI is in use
Staff paste data into public chatbots, and teams spin up Azure OpenAI deployments with no review.
How we fix it: Discovery of AI apps, models, and usage across the estate.
Sensitive data leaking into prompts
Customer and financial data ends up in prompts and responses that nobody can see.
How we fix it: Purview DSPM for AI, sensitivity labels, and DLP applied to AI traffic.
No answer for the auditor
Customer security reviews and new AI regulations ask how models are approved, tested, and monitored.
How we fix it: A risk register, approval workflow, and exportable evidence.
The solution
AI you can account for
- Inventory of AI in use: sanctioned tools, shadow AI, and models in your Azure tenant
- Guardrails on prompts, data access, and outputs, enforced in the platform rather than in a policy PDF
- Audit-ready evidence mapped to the EU AI Act, ISO/IEC 42001, NIST AI RMF, and the DPDP Act
Audit what you already run
An AI audit starts with what is actually deployed and used, not with a policy template.
Risk assessment per use case
Each AI use case rated for data sensitivity, decision impact, and regulatory exposure, so the controls match the risk.
Model & prompt security review
Exposure to prompt injection, jailbreaks, and data exfiltration checked with Azure AI Content Safety and Defender for Cloud AI threat protection.
Governance that holds in production
Controls only count if they are still enforced months after launch.
Data guardrails for AI
Sensitivity labels, DLP, and access controls that decide what data copilots and models can read and return.
Approval & lifecycle workflow
A lightweight path from idea to approved model, with a named owner, evaluation results, and a review date recorded for each.
Monitoring & audit evidence
Usage, content-filter hits, and policy violations logged and summarised, with evidence packs ready for auditors and enterprise customers.
Ready when you are
Could you show an auditor how your AI is controlled?
We can start with an audit of the models and copilots you already run and show you the risks, the control gaps, and the evidence you are missing before you commit to a governance programme.