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AI Trust Center

Policies and guardrails for responsible AI, in one place.

How Zetrixweb governs AI agents and model use in delivery - guardrails, data-in-prompt controls, human oversight thresholds, and standard security/procurement readiness alongside it.

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AI guardrails
Data stewardship
Operations readiness
Trust Center Ledger Live SOC2 Aligned Certifications Signed Audit Reports Logged

AI Guardrails

What governs an AI agent before and after it goes live.

Every agent or model integration we ship carries an explicit scope, an approval threshold, and a monitoring plan - decided with the client before rollout, not assumed by default.

Action Scope

Each agent's tool access and permitted actions are explicitly defined, not left open-ended.

Human Approval Thresholds

Irreversible or high-risk actions - payments, external messages, production changes - route to a human by default.

Data-in-Prompt Controls

Sensitive fields are classified and redacted before they reach a model's context window.

Output Monitoring

Logging and drift checks on model outputs so quality and safety issues surface early, not after the fact.

Control Areas

What enterprise teams typically validate.

Security Engineering

Secure SDLC, role-based access, dependency reviews, and deployment safeguards.

Data Handling

Scoped data access, retention alignment, and controlled movement across systems.

Governance Cadence

Recurring review cycles for risks, quality, release readiness, and KPI progress.

Framework Alignment

How AI governance reviews are structured.

We use the NIST AI Risk Management Framework as a shared vocabulary and EU AI Act-style risk tiers to set the oversight bar - not as certifications we hold, but as the working structure behind how we review AI workflows.

NIST AI RMF: Govern, Map, Measure, Manage

  • Govern: who owns AI risk decisions and how they're escalated.
  • Map: what data and context each workflow actually uses.
  • Measure: quality, safety, and drift metrics tracked over time.
  • Manage: the response plan when a metric crosses a threshold.

Risk Tiering

Workflows touching financial outcomes, safety, or personal data get the strictest human-in-the-loop requirements; low-stakes, high-confidence tasks get lighter oversight. The tier is set with the client, documented, and revisited as the workflow matures.

Enterprise Onboarding

Legal and procurement readiness.

Commercial and legal support

  • NDA execution for pre-discovery discussions.
  • Statement of Work and milestone-linked delivery scopes.
  • Data processing commitments aligned to project context.

Due-diligence checklist support

  • Security and architecture questionnaire walkthroughs.
  • Operational process and release workflow briefings.
  • Governance and reporting cadence definitions.
FAQs

Frequently asked questions.

Every agent gets a defined action scope, a human-approval threshold for irreversible or high-risk actions, and logging on every decision it makes, reviewed before rollout and periodically after.

We structure AI governance reviews around the NIST AI RMF's Govern, Map, Measure, and Manage functions, and apply EU AI Act-style risk tiering to decide how much oversight a given workflow needs.

Yes. We support security questionnaires, architecture walkthroughs, and governance checkpoints during onboarding.

We run documented incident workflows with triage, communication, mitigation, and post-incident review checkpoints.

Yes. NDA and data processing terms are supported as part of project onboarding.

Need a trust and security walkthrough?

We can align your procurement checklist with our delivery and governance model.

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