AI Security & Governance Model

AI governance is not a document you write and file away. It is a set of technical controls, organisational processes, and monitoring systems that ensure your AI behaves predictably, securely, and in compliance with your obligations.

Most startups either ignore AI governance (and get blocked by enterprise sales) or over-engineer it (and slow their shipping velocity to a crawl). Our model strikes the balance.

Technical Controls

Input Validation — Every input to your AI system is validated, sanitised, and logged. This prevents prompt injection, blocks malicious inputs, and creates an audit trail for compliance.

Output Filtering — Every output is checked against content policies, PII detection rules, and format requirements before reaching the user. Your AI cannot leak data it should not have access to.

Access Management — Role-based access controls for AI configuration, model selection, prompt templates, and training data. Changes to AI behaviour are tracked and auditable.

Data Lineage — Clear documentation of where data comes from, how it is processed, what models see it, and where outputs go. This is table stakes for SOC2 and GDPR compliance.

Organisational Processes

Change Management — Model updates, prompt changes, and configuration modifications go through a review process with quality gates. No cowboy deployments to production.

Incident Classification — A clear taxonomy for AI-specific incidents (hallucinations, data leakage, quality degradation, adversarial attacks) with defined severity levels and response procedures.

Regular Review — Monthly review of AI system performance, security events, and compliance status. Quarterly external assessment for regulated industries.

Compliance Mapping

We map our governance controls to specific requirements in SOC2, ISO 27001, GDPR, and sector-specific regulations. When your auditor asks "how do you govern your AI systems?", you have a documented, evidence-backed answer.