Secure AI Implementation

Enterprise buyers will not touch your product without SOC2. Regulated industries need ISO 27001. Healthcare needs HIPAA. And LLMs introduce entirely new attack surfaces that traditional security frameworks do not cover — prompt injection, data exfiltration through model outputs, and training data leakage.

We build AI systems where security is architectural, not bolted on.

The AI Security Gap

Traditional application security assumes deterministic software. AI systems are probabilistic. An LLM can be convinced to ignore its instructions, leak context from other users, or generate outputs that violate your data policies. These are not theoretical risks — they are active attack vectors in production AI systems.

Most security teams lack the AI-specific expertise to address these risks. Most AI teams lack the security expertise to implement proper controls. We bridge that gap.

What We Implement

Input Guardrails — Prompt injection detection, input sanitisation, and content filtering that blocks malicious inputs before they reach your model.

Output Controls — Response validation, PII detection and redaction, and format enforcement that ensures your AI never leaks sensitive data or generates harmful content.

Access Architecture — Role-based access controls, API key management, rate limiting, and tenant isolation for multi-user AI systems.

Audit and Compliance — Comprehensive logging of all AI interactions, model versions, and configuration changes. Immutable audit trails that satisfy SOC2 and ISO 27001 auditors.

Data Governance — Encryption at rest and in transit, data retention policies, and clear data flow documentation showing exactly where user data goes and what models process it.