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Security & Responsible AI

Practical principles we apply to data handling, access control and AI system design.

AI Strategy • Engineering • Integration • Automation

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AI SYSTEM Identity Encryption Data Access Monitoring Audit

Architecture

Security by Design

Security and responsible-AI practices are built into the system architecture from the first design conversation, not layered on before launch.

Security Architecture

A layered approach to identity, access, data protection, encryption, monitoring and auditability runs through every system we build, scoped to what that system actually handles.

  • Data Protection

    Data is handled according to its sensitivity, with access limited to what a given workflow genuinely needs.

  • Identity & Access

    Every user and system role has its own scoped access, rather than one shared credential covering everything.

  • Encryption

    Sensitive data can be encrypted in transit and at rest using appropriate platform and infrastructure controls where the architecture calls for it.

  • Secure Integrations

    Connections to CRMs, databases and internal systems are authenticated and scoped, not open by default.

  • Logging & Auditability

    Actions and data access are logged, so activity can be reviewed and traced after the fact.

  • Privacy-Conscious Architecture

    Systems are designed to collect and retain only the data a workflow actually requires.

Responsible AI

Human Oversight, Governance & Monitoring

Security is only part of the picture. These practices govern how AI systems behave once they're live.

01

Human Oversight

Higher-impact or ambiguous decisions are routed to a person by design, not left to a model to resolve alone.

02

Model & Provider Governance

Application logic is kept separable from any single model or vendor, and model choices are reviewed against the requirement they serve, not locked in indefinitely.

03

Responsible AI Implementation

AI behavior is evaluated against real examples before launch, with defined boundaries for what the system is -- and isn't -- allowed to decide on its own.

04

Production Monitoring

Live systems are monitored for behavior and performance, not just uptime, so issues surface before they compound.

Frequently Asked Questions

Security and oversight are designed into the system architecture from the start -- scoped access, logging, encryption where appropriate, and a defined human-escalation path for anything a system should not decide alone.

Cortex Texas AI is not a certifying body and holds no third-party compliance certification. Where a project involves healthcare data, we design with HIPAA-aware practices and recommend the client's own compliance and legal review before production use.

No formal SOC 2 or ISO certification is held at this time. Security practices are applied project by project, matched to what that system actually handles.

Client teams review AI outputs and workflow behavior against real examples as part of testing, alongside our own evaluation, before anything reaches production.

Actions and data access are logged, so a client can review system activity after the fact where that is part of the agreed architecture.

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