Our Principles
The foundational principles that guide how we build, deploy, and operate AI systems for critical infrastructure.
Security
- Data minimization - only collect what's necessary for effective operation
- Encryption in transit and at rest (platform-dependent implementation)
- Comprehensive access controls with least privilege principles
- Regular security audits and vulnerability assessments
Safety & Guardrails
- Pre-approved actions only - no unauthorized system modifications
- Multi-stage approval workflows for critical operations
- Built-in rollback paths for all automated actions
- Comprehensive audit logging of all system interactions
Reliability
- SLO-centric design - reliability as a first-class citizen
- Extensive failure-mode testing and chaos engineering
- Graceful degradation under adverse conditions
- Circuit breakers and backpressure handling
Responsible AI
- Explainability - clear reasoning for all recommendations
- Operator override - humans always have final control
- Continuous evaluation on representative incident scenarios
- Bias detection and mitigation in decision-making processes
Security by Design
Security isn't an afterthought—it's built into every layer of our architecture
Data Protection
We implement zero-trust architecture with end-to-end encryption, ensuring your telemetry data remains secure throughout processing and analysis.
- • TLS 1.3 for all communications
- • AES-256 encryption at rest
- • Key rotation and HSM integration
Access Control
Role-based access control with multi-factor authentication and regular access reviews ensure only authorized personnel can interact with systems.
- • RBAC with principle of least privilege
- • MFA for all administrative access
- • Regular access audits and reviews
Human-in-the-Loop by Default
AI augments human decision-making rather than replacing it
Recommend
AI analyzes and suggests actions
Review
Human operators evaluate recommendations
Execute
Actions taken only after approval
Continuous Evaluation
We continuously test and improve our systems against real-world scenarios
Model Performance
Regular evaluation against diverse incident scenarios ensures our AI maintains high accuracy and relevance across different environments.
Bias Detection
Automated bias detection and human review processes ensure fair and equitable treatment across different system types and organizations.
Want to learn more about our approach?
Request our detailed security brief to understand how we implement these principles in practice