AI Security & Governance
Your AI deployment is a new attack surface with an old governance gap.
AI moved into production faster than the controls around it.
Models are exposed through APIs. Prompts carry injection risk. Training data carries regulated information. And the frameworks are only now catching up, with Bank of Ghana’s 2026 directive already adding AI governance expectations. We secure the workload and build the governance evidence in parallel.
Workload and runtime security
Securing the infrastructure, containers and pipelines hosting AI workloads, with runtime threat detection across the model serving layer.
Model and prompt protection
Guardrails against prompt injection, model extraction, data leakage through inference and misuse of AI-facing APIs.
Governance and assurance
Frameworks aligned to ISO/IEC 42001, with the model inventory, control evidence and risk documentation regulators are starting to ask for, managed in a GRC platform rather than a spreadsheet.
Posture assessment
Assessment of an existing AI deployment against a defined control set, with a prioritised remediation roadmap.
Platforms we work with
Questions this answers
What AI workloads are running in production, and who approved them?
Can a user pull regulated data out of our model through the prompt interface?
Do we have the evidence to answer a regulator's question about AI use?
