Your data stays yours.
How we protect your data, run our AI responsibly and keep our promises, in one place.
01Our principles
01
Read-only by default
We read from your systems. Actions happen in your systems, by your people.
02
Your data, your region
Data stays where you choose.
03
Your data trains only your models
One client’s data never trains another client’s models.
04
People approve
AI recommends; people approve material decisions.
05
Everything logged
Every view, decision and export is recorded.
02The data boundary
The intelligence moves. Your data stays.
This is the promise behind everything on this page: our platform runs inside your environment, reads your data without changing it, and keeps every result there.
- Runs inside your environmentDeployed in your own cloud tenant or on your own servers, in the region you choose.
- Read-only connectorsWe read from your ERP and other systems. We never write to them or change a record.
- Nothing is copied outNo exports to our servers. Data, findings and evidence stay in your tenant.
- Your models stay yoursModels learn only from your data and stay with you. Nothing trains anyone else’s AI.
- Encrypted, logged, yours to controlEncrypted in transit and at rest. You grant access, you see every log, and you can switch it off.
03Privacy by design
Privacy and employee rights, designed in from day one
| Where | Requirement |
|---|---|
| European Union | GDPR; in Germany, works council co-determination on the technical monitoring of employees (the Works Constitution Act) |
| India | The Digital Personal Data Protection Act, 2023, and its rules |
| EU AI Act | Systems that monitor or evaluate employees can be high-risk: documentation and human oversight |
How we design for it
- Employee data is pseudonymised until a finding needs a name
- Only the fields each test needs
- Read-only access, processed in your environment and region
- Every result explained; people decide
- Tests and models documented, versioned and auditable
04Audit the algorithms
Next, audit must test the algorithms themselves
Our own rules, models, LLMs and agents must meet the standards we apply to others: documented, validated and explainable.
| Reference | What it asks for |
|---|---|
| EU AI Act | Risk-based duties; for high-risk uses, including some employee monitoring, documentation and human oversight |
| IDW PS 861 | The German standard for auditing AI systems: governance, data, model, application and infrastructure |
| ISO/IEC 42001 | A management system for AI: policy, roles, risk assessment and lifecycle controls |
| IIA Standards | Internal audit’s own use of technology must be governed and its results reliable |
Not legal advice.
05What’s in the Trust Center
- Certifications
- Current certificates only, each with its number and date
- Privacy
- Privacy notice, data processing agreement and the list of sub-processors
- Responsible AI
- Explainable results, human approval, model testing and monitoring, and bias checks where decisions affect people
- Continuity
- Backups and recovery objectives
- Report a vulnerability
- A role-based address, for example security@ajalabs.ai, and a responsible disclosure policy
Where is your money hiding?
Three minutes to an estimate. Thirty minutes to a plan. Six weeks to valued findings on your own data.