Security Standards

Last updated: 21 June 2026

At garyjudge.com, security is built into the architecture of every AI agent blueprint, RAG framework, and customised implementation guide. I advocate for a security-first, zero-trust approach when designing and deploying AI systems in production. This document outlines the security measures applied to this website, alongside the engineering standards recommended for AI integrations.

1. Website and Form Security

I employ modern web engineering best practices to ensure that your details are handled safely and secure from unauthorized access. Key security protocols active on this site include:

  • Transport Layer Security (TLS): All communications between your browser and my servers are encrypted in transit using industry-standard TLS 1.3 encryption (HTTPS).
  • Cross-Site Request Forgery (CSRF) Protection: All form submissions generate a cryptographically secure, random CSRF token tied to your active PHP session. This ensures that only you can submit requests on your behalf.
  • Spam and Bot Defense: Submissions are validated through invisible honeypot check fields. No external tracking scripts or CAPTCHA tools are used, keeping your browsing experience fast, clean, and private.
  • Input Sanitisation and Validation: Every input received (names, emails, company information) is strictly validated and sanitised both clientside and serverside to prevent injection attacks (such as SQL injection, Cross-Site Scripting (XSS), or script execution).
  • Protected Storage: Data collected via download requests (stored in leads.json) is kept on a secured server directory with restricted filesystem permissions. Directory browsing is disabled, preventing unauthorized external access.

2. Recommended AI Architecture Security Standards

When designing custom artificial intelligence solutions—such as those detailed in the Autonomous Agent Blueprint—I promote the following architectural security standards to protect corporate assets and proprietary information:

Data Privacy and Governance

  • No-Training Guarantees: Recommend using LLM providers that offer explicit enterprise API data policies guaranteeing that your input prompts and vector embeddings are never used to train public base models (e.g. OpenAI Enterprise, Anthropic Console, Google Vertex AI).
  • Self-Hosted / Local LLMs: Support and guide the hosting of open-source models (such as Llama 3, Mistral, or Phi) locally or inside a private cloud environment (VPC) to ensure complete data sovereignty and isolation.

Retrieval-Augmented Generation (RAG) Guardrails

  • Access Control Mapping: Vector databases should reflect original document repository access control lists (ACLs). An LLM-powered RAG system must never retrieve or present documents that a user is not authorised to view in the underlying source system.
  • Hallucination Defences: Implement semantic overlap validation, cosine similarity limits, and source-attribution engines to check that model outputs are strictly grounded in retrieved reference material.

Autonomous Agent Sandbox Controls

  • Restricted Tool Scopes: AI agents equipped with action tools (e.g. database querying, code execution, email dispatch) should be assigned the absolute minimum permissions needed to complete their tasks (least-privilege model).
  • Isolated Sandboxes: Tools capable of running dynamic code or executing scripts must run inside containerised, temporary sandboxes (e.g. isolated Docker containers) without network access to the primary system host.
  • Human-in-the-Loop (HITL) Gateways: Critical action steps—such as executing database updates, transferring funds, or sending emails to external clients—require manual human verification before execution.

Mitigation of Prompt Injection

  • System Instruction Protection: Design robust prompts using clear boundaries and structural formatting (e.g., XML/JSON dividers) to isolate user-facing inputs from core system instructions.
  • Output Validation: Treat LLM outputs as untrusted data. Validate, parse, and filter model responses prior to rendering them in a web interface or passing them to downstream APIs.

3. Vulnerability Reporting

If you discover any security vulnerability on this website or in any code snippet included in my guides, please report it immediately by email to hello@garyjudge.com. I take security reports seriously and aim to address and patch confirmed issues within 48 hours of reporting.