Access and identity
Plan strong authentication and least-privilege authorization.
Trust
Trust, security and responsible AI
Build confidence through concrete controls, honest disclosures, careful data handling and proportionate human oversight.
The opportunity
Trust should be verifiable.
Security is a set of practices applied to a real implementation—not a logo in a website footer. Our trust approach emphasizes threat modeling, appropriate permissions, data minimization, safe deployment and evidence about model and system behavior.
Our emphasis is on clarity before complexity: meaningful use cases, explicit system boundaries, measurable behavior and practical integration into the software and operations people already rely on.
What it can include
Capabilities shaped around your needs
Select the components that solve the problem. More technology is not automatically more value.
Information handling
Map data flows, consent, retention and service providers.
AI safety evaluation
Test model misuse, wrong answers and unexpected tool behavior.
Observability
Record useful diagnostic events with suitable confidentiality controls.
Resilience
Prepare backups, dependency management and incident response.
Transparency
Communicate limitations, actual certifications and operational scope accurately.
Practical applications
Where this approach makes sense
Examples describe potential solutions, not claims about completed client work.
Enterprise due diligence
Discuss architecture, roles and planned controls in a defined engagement.
Consumer product privacy
Build clear permissions, consent and deletion paths.
Agent safety
Establish action boundaries and human approval.
Delivery methodology
A clear path from concept to operation.
Technical decisions should serve product goals, operational readiness and responsible use. Each stage creates a reviewable decision point.
Classify data
Identify sensitive information and its permitted use.
Define safeguards
Select controls appropriate to real risk.
Test and review
Validate implementation rather than relying on documentation alone.
Operate responsibly
Track incidents, fix weaknesses and revisit assumptions.
Design principles
The questions worth asking early.
No unverified certifications
ISO, SOC 2 and other status must be supported by current scope-specific evidence.
Security is ongoing
Architecture diagrams do not substitute for actual configurations and tests.
Responsible AI is practical
Human override, logging boundaries and evaluations matter more than slogans.
Questions & answers
Decisions before deployment
Is Informatix AI ISO 27001 certified?
This website makes no such claim. Any certification should be verified against the actual legal entity and in-scope services.
How is personal information protected?
Controls depend on the actual product and hosting arrangement. Read our draft privacy disclosure and contact us for project-specific requirements.
Start a conversation
Bring your next AI idea into focus.
Tell us about the challenge, the people involved and what a useful result would look like. We can explore an appropriate next step together.
EXPLORE THE WHOLE FIELD
10 additional areas
Explore the related disciplines in depth, from first principles to practical, safe applications.
AI Content Provenance
Design traceability and disclosure patterns for generated and edited material when provenance matters.
ExploreAI Safety Engineering
Design model behavior and system constraints for realistic misuse, mistakes and uncertain outputs.
ExploreData Protection & Confidentiality
Minimize exposure of personal and business data across applications, retrieval and model providers.
ExploreAI Governance
Translate AI priorities into ownership, approvals, documented risk and accountable lifecycle decisions.
ExploreAuditability & Model Traceability
Record meaningful decisions and system changes without retaining unnecessary personal information.
ExploreHuman Oversight
Design review and intervention into AI workflows wherever judgment and accountability remain necessary.
ExploreAccessibility & Inclusive Design
Make intelligent products usable across languages, devices, abilities and connectivity conditions.
ExploreReliability & Incident Readiness
Prepare AI systems for provider outages, tool failures, incorrect outputs and operational change.
ExploreSecure AI Software Development
Extend secure engineering practice to prompts, retrieval, model tools and data pipelines.
ExploreCompliance Readiness
Design evidence and controls around actual obligations without claiming certifications not obtained.
Explore