AI STUDIOSecurity & Responsible AI
An AI system you can't trust is worse than none
Every Khanex system is built with governance, evaluation, monitoring, and human oversight from day one, with practices informed by the NIST AI Risk Management Framework and OWASP's GenAI security guidance.
Our operating principles
Human oversight, always
Customer-facing and high-impact automations ship with a human escalation or approval path. Ambiguous or sensitive actions route to your team with full context. The AI never has the final word on something that matters.
Data minimization
We collect only the data a workflow needs, define retention up front, and document every processor that touches it. Client data is never used to train models for other clients.
Transparency with end users
Where appropriate, end users are told they're interacting with an AI assistant. We don't build systems designed to pass as human, and we never clone a voice or likeness without explicit written rights.
Security by design
We threat-model prompts, data flows, credentials, and integrations before launch, including prompt injection and sensitive-information disclosure risks identified in OWASP's GenAI security guidance.
No unsupported claims
Every public claim we make is attributable to a case, benchmark, methodology, or documented capability. We apply the same standard to the systems we build for you.
Careful with sensitive domains
We design with your compliance obligations in mind and treat regulated or sensitive data with the controls it requires. We deliberately avoid high-risk decisioning domains like HR screening, lending decisions, and medical diagnosis unless scoped with appropriate expertise.
The QA standard every system ships with
Quality assurance isn't a phase. It's part of the product. At minimum, every production deployment includes:
- Evaluation cases grounded in real product or workflow scenarios, not synthetic demos
- Failure-mode logging so problems are found and categorized, not hidden
- Human escalation or approval rules defined and tested before launch
- Security checks on inputs and outputs, including injection resistance
- Prompt, retrieval, and version control, with every change tracked and reversible
- Client UAT signoff before anything goes live
- Rollback options for every production deployment
- Monthly review of performance, quality, reliability, and cost
Governed across the full lifecycle
Following the NIST AI RMF's structure, we govern, map, measure, and manage every system from scoping through ongoing operations.
Govern
Clear policies on what we will and won't build, data handling rules, model/vendor neutrality, and IP ownership defined in contracts before work starts.
Map
Risks identified per use case during the AI Discovery Sprint: data sensitivity, failure impact, evaluation needs, and escalation paths.
Measure
Evaluation suites, task success rates, grounding quality, escalation accuracy, and error incidents measured against real scenarios.
Manage
Continuous monitoring, cost controls, incident handling, and monthly reviews across the system's life.
Data handling and ownership
Your data stays yours
Client-specific deliverables, data mappings, configurations, documentation, and approved custom workflows belong to you. We retain only our pre-existing templates, frameworks, and reusable internal accelerators, and that split is explicit in every contract.
Baseline controls
Multi-factor authentication, managed credentials, encrypted storage, and least-privilege access to client systems. Sensitive-data workflows get documented retention schedules and processor lists, and a Data Processing Addendum is available for privacy-sensitive engagements.