APPNEURAL · Trust Center
Responsible AI
Build AI workflows around clear purpose, human accountability, privacy, security, evaluation, transparency, and proportionate governance.
Review area 01
Principles
For each use case, define purpose and affected people; minimize and authorize data; protect access; communicate meaningful limitations; evaluate representative and adversarial cases; and retain accountable human judgment for consequential decisions.
Review area 02
Risk classification
A project can use low, medium, and high impact categories as a review aid. High-impact domains such as employment, health, finance, and legal decisions require careful legal and domain review; labels alone do not determine regulatory status.
Review area 03
Human oversight and autonomy
Choose whether AI observes and recommends, drafts for approval, acts within explicit bounds, or operates autonomously. Assign a human owner, stop conditions, escalation, fallback, and post-release review for each workflow.
Review area 04
Known limitations
AI outputs can be incomplete, biased, misleading, or unsafe. Grounding, filters, confidence scores, audit logs, and human review must be designed and tested; none guarantees correctness.
Evidence and scope
Distinguish guidance from evidence.
This public summary is not an audit, certification, contract, or proof that a control is deployed. APPNEURAL has not published supporting operational evidence here. For a real engagement, confirm the system, data, owner, control implementation, test evidence, and contractual scope before relying on a statement.
Contact
Start an enterprise trust review.
Use the general enquiry form to describe your request. It is not a dedicated security incident channel, and no response-time commitment is stated here. Do not include passwords, credentials, customer records, or sensitive security evidence.
Contact APPNEURAL