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Maestro.
Security & Trust

Documented, not implied.

Maestro's pitch is defensible evidence — so the platform itself has to be defensible. This page covers how we handle data, govern AI, and protect academic integrity, in the language your security review actually uses.

Compliance posture

Where we stand, stated plainly.

We label what is certified, what is aligned, and what is underway — inflated claims fail procurement, and they should.

Program underway

SOC 2

Controls are designed against the SOC 2 Trust Services Criteria; independent audit is on the roadmap and documentation is available under NDA.

Readiness

GDPR

Data-protection by design: lawful-basis mapping, data-subject rights workflows, and EU data-residency options for European institutions.

Aligned

FERPA

Student education records are handled under institutional control. Maestro operates as a school official with a legitimate educational interest.

In transit & at rest

Encryption

TLS 1.2+ for every connection; AES-256 at rest. Secrets are managed, rotated, and never present in application code.

SAML / OIDC

Access & SSO

Learners and staff arrive through your identity provider. Role-based access, least privilege, and full administrative audit logs.

WCAG 2.2 AA target

Accessibility

Accessibility is a design law, not a retrofit: every learning template ships with a first-class accessible variant.

Academic integrity

Integrity by design, not surveillance.

The defense stack makes honesty the rational choice: tasks ask what only the learner knows, answers don't travel between learners, and behavioural signals route support — they never accuse.

i

Honesty is the cheapest path

Incentives are shaped so the easiest route through is the real one.

ii

Tasks ask what only the learner knows

Not what a screenshot or a chatbot can hand back.

iii

No two answers travel

Per-learner variation means copying gets you nowhere.

iv

Signals support, never accuse

Behavioural evidence guides help — it is not surveillance.

v

Mastery is confirmed in dialogue

A reflective conversation, not a gate to slip past.

Read the full defense-stack deep dive
AI governance

AI-native means AI-governed.

The adaptive engine is powerful precisely because it operates inside hard boundaries. These are the four your review committee will ask about.

The human gate

No academic decision is made by AI alone. Content, assessment, and grading standards are approved by your subject-matter experts before anything reaches a learner.

Documented model use

Where models are used, for what, and with which data is documented and available to your review teams — no silent capability changes.

Auditability of adaptation

Every adaptive decision traces to the evidence that produced it. Learners see the reasoning; institutions can audit it.

Learner data boundaries

Learner data trains no external foundation models. Institutional data stays institutional — contractually and architecturally.

Architecture & data flow

One source of truth, clear boundaries.

Postgres-first architecture, standards-based LTI 1.3 and SSO integration, and a governed content pipeline — documented for your technical review.

For your review team

Get the security packet.

Data-flow diagrams, subprocessor list, DPA template, and the AI-governance policy — everything your security and procurement review needs.