Majestic Vision AI Governance & Acceptable Use Policy

This policy outlines the governance frameworks, technical guardrails, and transparency requirements governing all artificial intelligence systems, large language models, and agentic workflows developed, deployed, or managed by Majestic Vision. As a digital architecture and DevOps consultancy, we build automated data ingestion pipelines, web scraping services, and intelligent web applications that require strict adherence to global regulatory standards.

1. Core Governance Framework & Risk Mapping

Our internal operations and client deployments are structured around the NIST AI Risk Management Framework, functioning across four core pillars: Govern, Map, Measure, and Manage. To ensure accountability across our digital ecosystem, we enforce the following baseline controls:

AI System Inventory: Every model, third-party vendor feature, and internal application that calls an AI model is logged in a centralized asset registry.
Risk Tiering: Systems are classified into Low, Medium, and High risk tiers based on data exposure and decision-making impact.
Data Governance: We maintain strict dataset acceptance criteria, lineage documentation, and quality testing protocols for all data utilized in model training and validation.

2. Agentic Operations & Technical Guardrails

For custom workflow automation and autonomous agents operating within our server environments and local web applications, Majestic Vision enforces strict operational boundaries to mitigate chain injection attacks and behavioral drift:

Agent Registration & Identity: Every AI agent deployed in our environment is registered in the AI system inventory with a unique identity, authorized capabilities, and an accountable owner.
Capability Scoping: Agents operate under strict, defined capability scopes, ensuring least-privilege access to external APIs, local databases, and scraping targets.
Multi-Agent Trust Boundaries: In multi-agent architectures, inter-agent communications and task delegations are treated as untrusted inputs and require explicit verification.
Memory & Context Controls: Governance controls dictate what an agent is permitted to remember across persistent sessions, mitigating the risk of adversarial context accumulation.
Human Override Protocols: Irreversible data modifications, automated client communications, and external API calls that incur costs require explicit human confirmation before execution.

3. Transparency & Consumer Disclosure

To maintain compliance with the transparency obligations of the EU AI Act (Article 50), Majestic Vision ensures that all user-facing digital interfaces, community portals, and chatbots meet the following standards:

Interaction Disclosure: When deploying chatbots, virtual assistants, or automated response systems, the interface is designed to explicitly inform users that they are interacting with an AI system.
Synthetic Content Marking: Any AI systems configured to generate synthetic audio, image, video, or text outputs must ensure that the content is detectable as AI-generated and marked in a machine-readable format.
Emotion & Biometric Categorization: If any deployed system is designed to recognize human emotions or categorize individuals biometrically, explicit disclosure will be provided to the exposed natural persons.
Human Oversight: Systems are designed with explicit intervention and override points, empowering operators with clear escalation paths (Article 14).

4. Technical Documentation & Incident Response

Continuous monitoring and detailed technical documentation are required to support audit readiness and incident management.

Record-Keeping: We maintain centralized technical documentation detailing system architectures, intended purposes, scope limitations, and logging schemas. Event logs are retained for key automated operations and decisions.
Agent-Specific Incident Response: In the event of anomalous automated behavior, our incident response protocols are capable of isolating, rolling back, and investigating the agent’s behavior at the individual action level, rather than solely at the session level.
Post-Market Monitoring: We conduct periodic adversarial robustness checks, drift monitoring, and pre-release testing to ensure our automated solutions remain secure over time.

Last Updated: October 2026. This policy is reviewed quarterly to ensure alignment with evolving technical frameworks and global regulatory mandates.